# Nanosystems, Edge Computing, and the Next Generation Computing Systems

^{*}

## Abstract

**:**

## 1. Introduction

_{60}molecules [6], and discerning the various carbon-carbon bonds within the molecules [7] are other examples of boundless success. In “Bonding more atoms together for a single molecule computer”, C. Joachim discussed how the future of computing would depart from solid-state integrated electronics and enter the realm of molecular transistors [8]. Such claims are already being supported by works on single molecules, for example, controlling cis-trans transition in Azobenzene molecule, leading to the molecules being “switched” with spatial selectivity, has been demonstrated [9]. Taming individual atoms towards quantum computing, atom-by-atom assemblers to arrange several trapped neutral atoms in one-dimension [10], in arbitrary two-dimensional patterns [11], and in three-dimensional arrays [12] with controllable single atom capability, have been demonstrated. To scale up the “fabrication” of such atomic and molecular switches, novel concepts are being reported, including the demonstration of monolayer surface patterning at 3.5 nm on a gold surface via self-assembly, offering a potential path to large-area patterning [13].

^{21}bytes (or zettabytes, ZB) [32]. A 2012 industrial study reported an estimated 1 ZB of data generated worldwide with a predicted 40 ZB by the year 2020 [33]. In year 2011, Hilbert and López, estimating the world’s technological capacity to store, communicate, and compute information, concluded that in the year 2007, the world had stored ~0.29 ZB (compressed bytes), communicated ~2 ZB, and carried out 6.4 exaflops (=6.4 × 10

^{18}flops or floating-point operations/s) [19]. For comparison, Hilbert and López noted that the exaflop rate roughly equals the maximum number of nerve impulses/s executed by one human brain, and the ZB stored data is approaching the roughly 100 ZB stored in the DNA of a human adult [19].

^{18}bytes) massive quantities of data in addition to improved flops. The processing, communication, and storage of the large volumes of data by transistor circuits, interconnects, and networks, invented to make use of the digitally represented information, are pervasive. However, these operations are growing increasingly challenging due to data traffic, memory, and computing capacities. To combat the challenges associated with the need to transfer and communicate large amounts of data generated in one location to an HPC data center in a different location, the concept of edge computing (EC) [35] is being intensively investigated [36,37,38,39].

## 2. Edge Computing

## 3. Edge Computing Processor Architectures

^{17}operations/s) HPC at a power consumption of 13 MW, which is not practical yet for EC devices.

^{9}by 2020 [37]) places increasing demand on-chip performance. The power efficiency of EC devices is thus of paramount importance despite the development of better energy storage and power transport technologies. Designers, developers, and manufacturers compete to achieve smaller, lighter, lower cost, but faster, higher performance, and more energy-efficient processors for EC applications. Thus, as the EC-use cases are being more systematically characterized, the design of EC-optimized processors is expected to intensify.

## 4. Nanosystems and Nanoscience: From Edge Sensing to Edge Computing

^{4}) surface [137] or bulk field enhancement [135].

_{2}, transition metal dichalcogenides, black phosphorus [139,184,185], etc. Building processors based on carbon nanomaterial FETs (MOSFET, CMOS, and the multi-gate transistors FinFET [186,187]) have been already demonstrated as in the case of CNT FET. Advanced FETs, such as CNT-based FinFETs [188,189] are being explored as a power-efficient node-scaled platform towards a chip with reduced transistor dimensions and thus increased density. Use of other nanomaterials of relevance includes fabrication and testing via self-assembly of block copolymers to achieve 7 nm node FinFETs [187], and Si, Ge, and SiGe nanowire FinFETs [190]. Block copolymers, belonging to the class of linear copolymers in which the different types of monomers cluster together to form blocks of repeating units, can controllably self-assemble at the nanoscale to form an important class of nanocomposites [185,191]. The resulting functional nanoscale objects can be conductive and semiconductive and, therefore, important to the electronics industry.

#### 4.1. Carbon Nanotube CPU and Edge Device

^{15}s) and thus better chip-making materials.

^{2}states in a hexagonal lattice, the electronic band structure of the CNTs has been studied when investigating its transport properties [278,303].

#### 4.2. The Topological Edge States to Aid Edge Computing

#### 4.3. Nanophotonics and Plasmonics to Aid Edge Devices

#### 4.4. Quantum Processor and Computing to Aid Edge Devices

#### 4.5. Neuromorphic Computing and Edge Devices

^{16}brain operations/s [355,356], and ~20 W power consumption [357]) to produce a decision. In the traditional computing realm, the memory and processing unit are separated, limiting data communication rate (the so-called von Neumann limit). Neuromorphic computing [358] is touted as a potential approach to address the inherent limitations of conventional silicon technology. Biological systems exhibit complex dynamics and responses that are being increasingly exploited in many fields, including sensing and computing [359,360,361], for example, as described in “neuromorphic engineering” [356], where a brief account is given on some of the early developments of brain-like technologies and neural circuits. Neuromorphic or brain-inspired computing, unlike the serial processing of traditional digital computers, is envisioned to achieve massive parallel analog computing at high speed and low power [362]. To be comparable to the brain, neuromorphic hardware requires ~10

^{11}neurons [363] and thus needs to be extremely energy-efficient. An increasing number of studies are emerging toward achieving the basic elements needed to build neuromorphic devices [357,364,365,366,367,368,369], including the recent work on “evolvable organic electrochemical transistor”, which was reported to mimic the biological synapse [148]. Remarkably, similar to biological synapses, which establish, evolve, and operate, the devised transistor channel, formed by an electropolymerized conducting polymer, the first synaptic device was produced that generated new synapses within its working environment [148].

_{2}[378], quantum effects in superconducting circuits [366], photonic integrated-circuit synapse [354], quantum dots-based photonic synapse [370], oxide-based photonic synapse [379], single flux quantum circuits [380], and use of aligned CNT transistors [381].

^{9}transistors chip was reported in 2014 [385]. It featured 1 × 10

^{6}spiking neurons and 256 × 10

^{6}synapses but a power density of only 20 mW/cm

^{2}, compared to tens of W of typical CPUs. A comparison of power densities and clock frequencies of processors with those of the brain has been reported by Merolla et al. [385]. Industry research efforts in developing neuromorphic processors are also producing novel results. The 60-mm

^{2}chip Loihi is a neuromorphic many-core processor fabricated on Intel’s 14-nm process [386,387]. It implements spiking neural networks with on-chip learning and has been shown to outperform other approaches in solving the LASSO (Least Absolute Shrinkage and Selection Operator) optimization problem [386,387].

#### 4.6. Discussions

## 5. Conclusions

## Acknowledgments

## Conflicts of Interest

## References

- Spanner, K.; Gloss, R. New Challenges in Nanopositioning Technologies. In Proceedings of the International Conference and Exhibition on New Actuators and Drive Systems, Bremen, Germany, 14–16 June 2010; pp. 258–263. [Google Scholar]
- Bolonkin, A.A. Femtotechnology: Design of the Strongest AB Matter for Aerospace. J. Aerosp. Eng.
**2010**, 23, 281–292. [Google Scholar] [CrossRef] - Khan, N.; Abas, N.; Kalair, A.R. Electronic and Photonic Communique Bottlenecks Mandate Ultrafast Optics. Nonlinear Opt. Quantum Opt.
**2017**, 48, 185–192. [Google Scholar] - Jones, D.E.H. Nano—Remaking the World Atom by Atom—Regis, E. Nature
**1995**, 374, 835–837. [Google Scholar] [CrossRef] - Vaughan, O. A closer look at the atoms in a molecule. Nat. Nanotechnol.
**2009**, 4, 619. [Google Scholar] [CrossRef] [PubMed] - Vaughan, O. Fullerene synthesis Caught on camera. Nat. Nanotechnol.
**2010**, 5, 386. [Google Scholar] [CrossRef] [PubMed] - Vaughan, O. Scanning probe microscopy a discerning look at the bonds in a molecule. Nat. Nanotechnol.
**2012**, 7, 619. [Google Scholar] [CrossRef] [PubMed] - Joachim, C. Bonding more atoms together for a single molecule computer. Nanotechnology
**2002**, 13, R1–R7. [Google Scholar] [CrossRef] - Vaughan, O. Molecular switches order and control. Nat. Nanotechnol.
**2008**, 3, 644. [Google Scholar] [CrossRef] - Endres, M.; Bernien, H.; Keesling, A.; Levine, H.; Anschuetz, E.R.; Krajenbrink, A.; Senko, C.; Vuletic, V.; Greiner, M.; Lukin, M.D. Atom-by-atom assembly of defect-free one-dimensional cold atom arrays. Science
**2016**, 354, 1024–1027. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Barredo, D.; de Leseleuc, S.; Lienhard, V.; Lahaye, T.; Browaeys, A. An atom-by-atom assembler of defect-free arbitrary two-dimensional atomic arrays. Science
**2016**, 354, 1021–1023. [Google Scholar] [CrossRef] [Green Version] - Barredo, D.; Lienhard, V.; De Leseleuc, S.; Lahaye, T.; Browaeys, A. Synthetic three-dimensional atomic structures assembled atom by atom. Nature
**2018**, 561, 79–82. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Vaughan, O. Patterned surfaces—An organized union. Nat. Nanotechnol.
**2008**, 3, 526. [Google Scholar] [CrossRef] - Electronic Computers in Molecular Quantum Mechanics. Nature
**1956**, 177, 362. [CrossRef] - Normile, D. Molecular computing—DNA-based computer takes aim at genes. Science
**2002**, 295, 951. [Google Scholar] [CrossRef] [PubMed] - Goldup, S. Molecular machines swap rings. Nature
**2018**, 557, 39–40. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Moe-Behrens, G.H. The biological microprocessor, or how to build a computer with biological parts. Comput. Struct. Biotechnol. J.
**2013**, 7, e201304003. [Google Scholar] [CrossRef] [PubMed] - Dunn, K.E.; Trefzer, M.A.; Johnson, S.; Tyrrell, A.M. Towards a Bioelectronic Computer: A Theoretical Study of a Multi-Layer Biomolecular Computing System That Can Process Electronic Inputs. Int. J. Mol. Sci.
**2018**, 19, 2620. [Google Scholar] [CrossRef] - Hilbert, M.; Lopez, P. The World’s Technological Capacity to Store, Communicate, and Compute Information. Science
**2011**, 332, 60–65. [Google Scholar] [CrossRef] - Forbes, B.D.M. Big Data Market Revenues Are Projected to Increase from $42B in 2018 to $103B in 2027 #BigData#Analytics. Available online: http://www.forbes.com/sites/louiscolumbus/2018/05/23/10-charts-that-will-change-your-perspective-of-big-datas-growth/ (accessed on 15 June 2019).
- Henno, J. Information and Interaction. Front. Artif. Intell. Appl.
**2017**, 292, 426–449. [Google Scholar] [CrossRef] - Gleick, J. The Information: A History, a Theory, a Flood. IEEE Trans. Inf. Theory
**2011**, 57, 6332–6333. [Google Scholar] [CrossRef] - Robinson, A. The Information a History, a Theory, a Flood. Science
**2011**, 333, 1826–1827. [Google Scholar] [CrossRef] - Davis, C.H. The Information: A History, a Theory, a Flood. J. Am. Soc. Inf. Sci. Technpl.
**2011**, 62, 2543–2545. [Google Scholar] [CrossRef] - Misa, T.J. The Information: A History, a Theory, a Flood. Nature
**2011**, 471, 300–301. [Google Scholar] [CrossRef] - Smillie, K. The information: A History, a Theory, a Flood. IEEE Ann. Hist. Comput.
**2012**, 34, 99–101. [Google Scholar] - Hobart, M.E. The Information: A History, a Theory, a Flood. Technol. Cult.
**2014**, 55, 489–490. [Google Scholar] [CrossRef] - Akan, O.B.; Andreev, S.; Dobre, C. Internet of Things and Sensor Networks. IEEE Commun. Mag.
**2019**, 57, 40. [Google Scholar] [CrossRef] - Jaeik, C.; Chilamkurti, N.; Wang, S.J. Editorial of special section on enabling technologies for industrial and smart sensor internet of things systems. J. Supercomput.
**2018**, 74, 4171–4172. [Google Scholar] [CrossRef] [Green Version] - Akmandor, A.O.; Yin, H.X.; Jha, N.K. Smart, Secure, Yet Energy-Efficient, Internet-of-Things Sensors. IEEE Trans. Multi-Scale Comput. Syst.
**2018**, 4, 914–930. [Google Scholar] [CrossRef] - Marx, V. The Big Challenges of Big Data. Nature
**2013**, 498, 255–260. [Google Scholar] [CrossRef] - Han, L.X. Towards Sustainable Smart Society: Big Data Driven Approaches. In Proceedings of the International Conference on Future Networks and Distributed Systems (ICFNDS ‘17), Cambridge, UK, 19–20 July 2017. [Google Scholar] [CrossRef]
- The Digital Universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East. 2012. Available online: www.emc.com/leadership/digital-universe/index.htm (accessed on 15 June 2019).
- Kothe, D.; Lee, S.; Qualters, I. Exascale Computing in the United States. Comput. Sci. Eng.
**2019**, 21, 17–29. [Google Scholar] [CrossRef] - Satyanarayanan, M. How we created edge computing. Nat. Electron.
**2019**, 2, 42. [Google Scholar] [CrossRef] - Svorobej, S.; Endo, P.T.; Bendechache, M.; Filelis-Papadopoulos, C.; Giannoutakis, K.M.; Gravvanis, G.A.; Tzovaras, D.; Byrne, J.; Lynn, T. Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges. Future Internet
**2019**, 11, 55. [Google Scholar] [CrossRef] - Ai, Y.; Peng, M.G.; Zhang, K.C. Edge computing technologies for Internet of Things: A primer. Digit. Commun. Netw.
**2018**, 4, 77–86. [Google Scholar] [CrossRef] - Mao, Y.Y.; You, C.S.; Zhang, J.; Huang, K.B.; Letaief, K.B. A Survey on Mobile Edge Computing: The Communication Perspective. IEEE Commun. Surv. Tutor.
**2017**, 19, 2322–2358. [Google Scholar] [CrossRef] [Green Version] - Mach, P.; Becvar, Z. Mobile Edge Computing: A Survey on Architecture and Computation Offloading. IEEE Commun. Surv. Tutor.
**2017**, 19, 1628–1656. [Google Scholar] [CrossRef] - Liu, W.L.; Li, M.; Guzzon, R.S.; Norberg, E.J.; Parker, J.S.; Lu, M.Z.; Coldren, L.A.; Yao, J.P. A fully reconfigurable photonic integrated signal processor. Nat. Photonics
**2016**, 10, 190–195. [Google Scholar] [CrossRef] - Gogoi, N.; Sahu, P.P. All-Optical Surface Plasmonic Universal Logic Gate Devices. Plasmonics
**2016**, 11, 1537–1542. [Google Scholar] [CrossRef] - Brunner, D.; Soriano, M.C.; Mirasso, C.R.; Fischer, I. Parallel photonic information processing at gigabyte per second data rates using transient states. Nat. Commun.
**2013**, 4. [Google Scholar] [CrossRef] - Fu, Y.L.; Hu, X.Y.; Lu, C.C.; Yue, S.; Yang, H.; Gong, Q.H. All-Optical Logic Gates Based on Nanoscale Plasmonic Slot Waveguides. Nano Lett.
**2012**, 12, 5784–5790. [Google Scholar] [CrossRef] [PubMed] - Ferrera, M.; Park, Y.; Razzari, L.; Little, B.E.; Chu, S.T.; Morandotti, R.; Moss, D.J.; Azana, J. On-chip CMOS-compatible all-optical integrator. Nat. Commun.
**2010**, 1. [Google Scholar] [CrossRef] [PubMed] - Kwiat, P.G. Quantum information—An integrated light circuit. Nature
**2008**, 453, 294–295. [Google Scholar] [CrossRef] - Shulaker, M.M.; Hills, G.; Wong, H.S.P.; Mitra, S. Transforming Nanodevices to Next Generation Nanosystems. In Proceedings of the 2016 International Conference on Embedded Computer Systems: Architectures, Modeling and Simulation (SAMOS), Agios Konstantinos, Greece, 17–21 July 2016; pp. 288–292. [Google Scholar]
- Shulaker, M.; Wong, H.S.P.; Mitra, S. Computing With Carbon Nanotubes. IEEE Spectr.
**2016**, 53, 26–52. [Google Scholar] [CrossRef] - Jäck, B.; Xie, Y.; Li, J.; Jeon, S.; Bernevig, B.A.; Yazdani, A. Observation of a Majorana zero mode in a topologically protected edge channel. Science
**2019**. [Google Scholar] [CrossRef] - Mohammadi Estakhri, N.; Edwards, B.; Engheta, N. Inverse-designed metastructures that solve equations. Science
**2019**, 363, 1333. [Google Scholar] [CrossRef] - Pinna, D.; Araujo, F.A.; Kim, J.V.; Cros, V.; Querlioz, D.; Bessiere, P.; Droulez, J.; Grollier, J. Skyrmion Gas Manipulation for Probabilistic Computing. Phys. Rev. Appl.
**2018**, 9. [Google Scholar] [CrossRef] - Lee, V.T.; Alaghi, A.; Pamula, R.; Sathe, V.S.; Ceze, L.; Oskin, M. Architecture Considerations for Stochastic Computing Accelerators. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.
**2018**, 37, 2277–2289. [Google Scholar] [CrossRef] - Alaghi, A.; Qian, W.K.; Hayes, J.P. The Promise and Challenge of Stochastic Computing. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.
**2018**, 37, 1515–1531. [Google Scholar] [CrossRef] - Alaghi, A.; Hayes, J.P. Computing with ramdomness Stochastic computing, a 50-year-old idea, is set for a comeback. IEEE Spectr.
**2018**, 55, 46–51. [Google Scholar] [CrossRef] - Web of Science. Available online: http://apps.webofknowledge.com/WOS_GeneralSearch_input.do?product=WOS&search_mode=GeneralSearch&SID=7BMsN2TzxEC4pCic7wJ&preferencesSaved= (accessed on 15 June 2019).
- Satyanarayanan, M.; Bahl, P.; Caceres, R.; Davies, N. The Case for VM-Based Cloudlets in Mobile Computing. IEEE Pervasive Comput.
**2009**, 8, 14–23. [Google Scholar] [CrossRef] - Satyanarayanan, M.; Kistler, J.J.; Mummert, L.B.; Ebling, M.R.; Kumar, P.; Lu, Q. Experience with Disconnected Operation in a Mobile Computing Environment. In Proceedings of the Usenix Mobile & Location-Independent Computing Symposium, Cambridge, MA, USA, 2–3 August 1993; pp. 11–28. [Google Scholar]
- Satyanarayanan, M. Mobile Computing. Computer
**1993**, 26, 81–82. [Google Scholar] [CrossRef] - Vaughan, O. Working on the edge. Nat. Electron.
**2019**, 2, 2–3. [Google Scholar] [CrossRef] - Tu, W.Q.; Pop, F.; Jia, W.J.; Wu, J.; Iacono, M. High-Performance Computing in Edge Computing Networks. J. Parallel Distrib. Comput.
**2019**, 123, 230. [Google Scholar] [CrossRef] - Take it to the edge. Nat. Electron.
**2019**, 2, 1. [CrossRef] - Suarez-Albela, M.; Fraga-Lamas, P.; Fernandez-Carames, T.M. A Practical Evaluation on RSA and ECC-Based Cipher Suites for IoT High-Security Energy-Efficient Fog and Mist Computing Devices. Sensors
**2018**, 18, 3868. [Google Scholar] [CrossRef] - Park, D.; Kim, S.; An, Y.; Jung, J.Y. LiReD: A Light-Weight Real-Time Fault Detection System for Edge Computing Using LSTM Recurrent Neural Networks. Sensors
**2018**, 18, 2110. [Google Scholar] [CrossRef] - Amazon Elastic Compute Cloud (Amazon EC2). Available online: https://aws.amazon.com/ec2/ (accessed on 15 June 2019).
- Google Cloud Platform. Available online: https://cloud.google.com/ (accessed on 15 June 2019).
- Wachter, S. Data protection in the age of big data. Nat. Electron.
**2019**, 2, 6–7. [Google Scholar] [CrossRef] - Yang, Y. Multi-tier computing networks for intelligent IoT. Nat. Electron.
**2019**, 2, 4–5. [Google Scholar] [CrossRef] - Mujica, G.; Rodriguez-Zurrunero, R.; Wilby, M.; Portilla, J.; Gonzalez, A.B.R.; Araujo, A.; Riesgo, T.; Diaz, J.J.V. Edge and Fog Computing Platform for Data Fusion of Complex Heterogeneous Sensors. Sensors
**2018**, 18, 3630. [Google Scholar] [CrossRef] - Cha, H.J.; Yang, H.K.; Song, Y.J. A Study on the Design of Fog Computing Architecture Using Sensor Networks. Sensors
**2018**, 18, 3633. [Google Scholar] [CrossRef] - Chen, Y.S.; Tsai, Y.T. A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities. Sensors
**2018**, 18, 489. [Google Scholar] [CrossRef] - Barrias, A.; Casas, J.R.; Villalba, S. A Review of Distributed Optical Fiber Sensors for Civil Engineering Applications. Sensors
**2016**, 16, 748. [Google Scholar] [CrossRef] - Inaudi, D.; Glisic, B. Long-Range Pipeline Monitoring by Distributed Fiber Optic Sensing. J. Press. Vessel Technol.
**2010**, 132. [Google Scholar] [CrossRef] - Wang, J.; Li, D. Adaptive Computing Optimization in Software-Defined Network-Based Industrial Internet of Things with Fog Computing. Sensors
**2018**, 18, 2509. [Google Scholar] [CrossRef] - Qureshi, F.; Krishnan, S. Wearable Hardware Design for the Internet of Medical Things (IoMT). Sensors
**2018**, 18, 3812. [Google Scholar] [CrossRef] - Klonoff, D.C. Fog Computing and Edge Computing Architectures for Processing Data From Diabetes Devices Connected to the Medical Internet of Things. J. Diabetes Sci. Technol.
**2017**, 11, 647–652. [Google Scholar] [CrossRef] [Green Version] - Srivastava, M.; Suvarna, S.; Srivastava, A.; Bharathiraja, S. Automated emergency paramedical response system. Health Inf. Sci. Syst.
**2018**, 6, 22. [Google Scholar] [CrossRef] - Kumari, P.; Lopez-Benitez, M.; Gyu Myoung, L.; Tae-Seong, K.; Minhas, A.S. Wearable Internet of Things—From human activity tracking to clinical integration. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, Korea, 11–15 July 2017; pp. 2361–2364. [Google Scholar] [CrossRef]
- Henson, A.B.; Gromski, P.S.; Cronin, L. Designing Algorithms To Aid Discovery by Chemical Robots. ACS Cent. Sci.
**2018**, 4, 793–804. [Google Scholar] [CrossRef] [Green Version] - Kang, J.; Eom, D.S. Offloading and Transmission Strategies for IoT Edge Devices and Networks. Sensors
**2019**, 19, 835. [Google Scholar] [CrossRef] - Murakami, M.; Kominami, D.; Leibnitz, K.; Murata, M. Drawing Inspiration from Human Brain Networks: Construction of Interconnected Virtual Networks. Sensors
**2018**, 18, 1133. [Google Scholar] [CrossRef] - Jang, I.; Lee, D.; Choi, J.; Son, Y. An Approach to Share Self-Taught Knowledge between Home IoT Devices at the Edge. Sensors
**2019**, 19, 833. [Google Scholar] [CrossRef] - Taherizadeh, S.; Stankovski, V.; Grobelnik, M. A Capillary Computing Architecture for Dynamic Internet of Things: Orchestration of Microservices from Edge Devices to Fog and Cloud Providers. Sensors
**2018**, 18, 2938. [Google Scholar] [CrossRef] - Dinh, N.T.; Kim, Y. An Efficient Availability Guaranteed Deployment Scheme for IoT Service Chains over Fog-Core Cloud Networks. Sensors
**2018**, 18, 3970. [Google Scholar] [CrossRef] - An, C.; Wu, C.; Yoshinaga, T.; Chen, X.; Ji, Y. A Context-Aware Edge-Based VANET Communication Scheme for ITS. Sensors
**2018**, 18, 2022. [Google Scholar] [CrossRef] - Fan, X.; Cui, T.; Cao, C.; Chen, Q.; Kwak, K.S. Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning. Sensors
**2019**, 19, 847. [Google Scholar] [CrossRef] - Dong, C.; Wen, W. Joint Optimization for Task Offloading in Edge Computing: An Evolutionary Game Approach. Sensors
**2019**, 19, 740. [Google Scholar] [CrossRef] - Sun, Y.; Lin, F.; Zhang, N. A security mechanism based on evolutionary game in fog computing. Saudi J. Biol. Sci.
**2018**, 25, 237–241. [Google Scholar] [CrossRef] - Zhao, Y.; Wu, J.; Li, W.; Lu, S. Efficient Interference Estimation with Accuracy Control for Data-Driven Resource Allocation in Cloud-RAN. Sensors
**2018**, 18, 3000. [Google Scholar] [CrossRef] - Zeng, F.; Ren, Y.; Deng, X.; Li, W. Cost-Effective Edge Server Placement in Wireless Metropolitan Area Networks. Sensors
**2018**, 19, 32. [Google Scholar] [CrossRef] - Wu, Y.; Chen, X.; Shi, J.; Ni, K.; Qian, L.; Huang, L.; Zhang, K. Optimal Computational Power Allocation in Multi-Access Mobile Edge Computing for Blockchain. Sensors
**2018**, 18, 3472. [Google Scholar] [CrossRef] - Deniz, O.; Vallez, N.; Espinosa-Aranda, J.L.; Rico-Saavedra, J.M.; Parra-Patino, J.; Bueno, G.; Moloney, D.; Dehghani, A.; Dunne, A.; Pagani, A.; et al. Eyes of Things. Sensors
**2017**, 17, 1173. [Google Scholar] [CrossRef] - Mora-Gimeno, F.J.; Mora-Mora, H.; Marcos-Jorquera, D.; Volckaert, B. A Secure Multi-Tier Mobile Edge Computing Model for Data Processing Offloading Based on Degree of Trust. Sensors
**2018**, 18, 3211. [Google Scholar] [CrossRef] - Fan, K.; Yin, J.; Zhang, K.; Li, H.; Yang, Y. EARS-DM: Efficient Auto Correction Retrieval Scheme for Data Management in Edge Computing. Sensors
**2018**, 18, 3616. [Google Scholar] [CrossRef] - Gogoi, N.; Sahu, P.P. Compact surface plasmonic waveguide component for integrated optical processor. In Proceedings of the International Conference on Optics and Photonics 2015, Kolkata, India, 20–22 February 2015. [Google Scholar] [CrossRef]
- Silva, A.; Monticone, F.; Castaldi, G.; Galdi, V.; Alu, A.; Engheta, N. Performing Mathematical Operations with Metamaterials. Science
**2014**, 343, 160–163. [Google Scholar] [CrossRef] - Rodriguez-Zurrunero, R.; Utrilla, R.; Rozas, A.; Araujo, A. Process Management in IoT Operating Systems: Cross-Influence between Processing and Communication Tasks in End-Devices. Sensors
**2019**, 19, 805. [Google Scholar] [CrossRef] - Zhang, H.; Chen, Z.; Wu, J.; Deng, Y.; Xiao, Y.; Liu, K.; Li, M. Energy-Efficient Online Resource Management and Allocation Optimization in Multi-User Multi-Task Mobile-Edge Computing Systems with Hybrid Energy Harvesting. Sensors
**2018**, 18, 3140. [Google Scholar] [CrossRef] - Perez-Torres, R.; Torres-Huitzil, C.; Galeana-Zapien, H. A Cognitive-Inspired Event-Based Control for Power-Aware Human Mobility Analysis in IoT Devices. Sensors
**2019**, 19, 832. [Google Scholar] [CrossRef] - Nguyen, Q.N.; Liu, J.; Pan, Z.; Benkacem, I.; Tsuda, T.; Taleb, T.; Shimamoto, S.; Sato, T. PPCS: A Progressive Popularity-Aware Caching Scheme for Edge-Based Cache Redundancy Avoidance in Information-Centric Networks. Sensors
**2019**, 19, 694. [Google Scholar] [CrossRef] - Avgeris, M.; Spatharakis, D.; Dechouniotis, D.; Kalatzis, N.; Roussaki, I.; Papavassiliou, S. Where There Is Fire There Is SMOKE: A Scalable Edge Computing Framework for Early Fire Detection. Sensors
**2019**, 19, 639. [Google Scholar] [CrossRef] - Nguyen, V.C.; Dinh, N.T.; Kim, Y. A Distributed NFV-Enabled Edge Cloud Architecture for ICN-Based Disaster Management Services. Sensors
**2018**, 18, 4136. [Google Scholar] [CrossRef] - Zhu, L.; Fu, Y.; Chow, R.; Spencer, B.F.; Park, J.W.; Mechitov, K. Development of a High-Sensitivity Wireless Accelerometer for Structural Health Monitoring. Sensors
**2018**, 18, 262. [Google Scholar] [CrossRef] - Zhang, X.; Lin, J.; Chen, Z.; Sun, F.; Zhu, X.; Fang, G. An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture. Sensors
**2018**, 18, 1828. [Google Scholar] [CrossRef] [PubMed] - Sun, J.; Wang, X.; Wang, S.; Ren, L. A searchable personal health records framework with fine-grained access control in cloud-fog computing. PLoS ONE
**2018**, 13, e0207543. [Google Scholar] [CrossRef] [PubMed] - Athavale, Y.; Krishnan, S. A Device-Independent Efficient Actigraphy Signal-Encoding System for Applications in Monitoring Daily Human Activities and Health. Sensors
**2018**, 18, 2966. [Google Scholar] [CrossRef] [PubMed] - Oueida, S.; Kotb, Y.; Aloqaily, M.; Jararweh, Y.; Baker, T. An Edge Computing Based Smart Healthcare Framework for Resource Management. Sensors
**2018**, 18, 4307. [Google Scholar] [CrossRef] [PubMed] - Rosario, D.; Schimuneck, M.; Camargo, J.; Nobre, J.; Both, C.; Rochol, J.; Gerla, M. Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support. Sensors
**2018**, 18, 329. [Google Scholar] [CrossRef] [PubMed] - Rodriguez, A.; Valverde, J.; Portilla, J.; Otero, A.; Riesgo, T.; de la Torre, E. FPGA-Based High-Performance Embedded Systems for Adaptive Edge Computing in Cyber-Physical Systems: The ARTICo(3) Framework. Sensors
**2018**, 18, 1877. [Google Scholar] [CrossRef] [PubMed] - Deak, N.; Cret, O.; Echim, M.; Teodorescu, E.; Negrea, C.; Vacariu, L.; Munteanu, C.; Hangan, A. Edge computing for space applications: Field programmable gate array-based implementation of multiscale probability distribution functions. Rev. Sci. Instrum.
**2018**, 89, 125005. [Google Scholar] [CrossRef] [PubMed] - Chen, C.L.; Chuang, C.T. A QRS Detection and R Point Recognition Method for Wearable Single-Lead ECG Devices. Sensors
**2017**, 17, 1969. [Google Scholar] [CrossRef] - Idrees, Z.; Zou, Z.; Zheng, L. Edge Computing Based IoT Architecture for Low Cost Air Pollution Monitoring Systems: A Comprehensive System Analysis, Design Considerations & Development. Sensors
**2018**, 18, 3021. [Google Scholar] [CrossRef] - Ferrandez-Pastor, F.J.; Garcia-Chamizo, J.M.; Nieto-Hidalgo, M.; Mora-Martinez, J. Precision Agriculture Design Method Using a Distributed Computing Architecture on Internet of Things Context. Sensors
**2018**, 18, 1731. [Google Scholar] [CrossRef] - Ferrandez-Pastor, F.J.; Garcia-Chamizo, J.M.; Nieto-Hidalgo, M.; Mora-Pascual, J.; Mora-Martinez, J. Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture. Sensors
**2016**, 16, 1141. [Google Scholar] [CrossRef] [PubMed] - Huang, D.; Xu, C.; Zhao, D.; Song, W.; He, Q. A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation. Sensors
**2017**, 17, 2168. [Google Scholar] [CrossRef] [PubMed] - Zhong, P.; Zhang, Y.; Ma, S.; Kui, X.; Gao, J. RCSS: A Real-Time On-Demand Charging Scheduling Scheme for Wireless Rechargeable Sensor Networks. Sensors
**2018**, 18, 1601. [Google Scholar] [CrossRef] [PubMed] - Scionti, A.; Mazumdar, S.; Portero, A. Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud. Sensors
**2018**, 18, 2330. [Google Scholar] [CrossRef] [PubMed] - Sonmez, C.; Ozgovde, A.; Ersoy, C. EdgeCloudSim: An environment for performance evaluation of edge computing systems. Trans. Emerg. Telecommun. Technol.
**2018**, 29. [Google Scholar] [CrossRef] - Wang, H.Z.; Xiong, F.; Li, J.N.; Shi, S.F.; Li, J.Z.; Gao, H. Data management on new processors: A survey. Parallel Comput.
**2018**, 72, 1–13. [Google Scholar] [CrossRef] - Bu, L.K.; Mark, M.; Kinsy, M.A. A Short Survey at the Intersection of Reliability and Security in Processor Architecture Designs. In Proceedings of the 2018 IEEE Computer Society Annual Symposium on VLSI, Hong Kong, China, 8–11 July 2018; pp. 118–123. [Google Scholar] [CrossRef]
- Blake, G.; Dreslinski, R.G.; Mudge, T. A Survey of Multicore Processors A review of their common attributes. IEEE Signal Process. Mag.
**2009**, 26, 26–37. [Google Scholar] [CrossRef] - Vazhkudai, S.S.; Supinski, B.R.d.; Bland, A.S.; Geist, A.; Sexton, J.; Kahle, J.; Zimmer, C.J.; Atchley, S.; Oral, S.; Maxwell, D.E.; et al. The design, deployment, and evaluation of the CORAL pre-exascale systems. In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis, Dallas, TX, USA, 11–16 November 2018; pp. 1–12. [Google Scholar]
- Patterson, D. 50 years of Computer Architecture: From the Mainframe CPU to the Domain-Specific TPU and the Open RISC-V Instruction Set. In Proceedings of the 2018 IEEE International Solid-State Circuits Conference (ISSCC), San Francisco, CA, USA, 11–15 February 2018; pp. 27–31. [Google Scholar]
- Patterson, D. Reduced Instruction Set Computers Then and Now. Computer
**2017**, 50, 10–12. [Google Scholar] [CrossRef] - RISC-V. 2019. Available online: https://riscv.org (accessed on 15 June 2019).
- Karandikar, S.; Mao, H.; Kim, D.; Biancolin, D.; Amid, A.; Lee, D.; Pemberton, N.; Amaro, E.; Schmidt, C.; Chopra, A.; et al. FireSim: FPGA-Accelerated Cycle-Exact Scale-Out System Simulation in the Public Cloud. IEEE Micro
**2019**, 39, 56–65. [Google Scholar] [CrossRef] - ARM. Available online: https://www.arm.com/ (accessed on 15 June 2019).
- Zhang, Y.Q.; Khayatzadeh, M.; Yang, K.Y.; Saligane, M.; Pinckney, N.; Alioto, M.; Blaauw, D.; Sylvester, D. iRazor: Current-Based Error Detection and Correction Scheme for PVT Variation in 40-nm ARM Cortex-R4 Processor. IEEE J. Solid-State Circuits
**2018**, 53, 619–631. [Google Scholar] [CrossRef] - Neoverse. Available online: https://www.arm.com/products/silicon-ip-cpu/neoverse/neoverse-n1 (accessed on 15 June 2019).
- NVIDIA. Available online: https://www.nvidia.com/en-us/data-center/products/egx-edge-computing/ (accessed on 15 June 2019).
- APC. Available online: https://www.apc.com/us/en/solutions/business-solutions/edge-computing.jsp (accessed on 15 June 2019).
- Open Edge Computing Initiative. Available online: https://www.openedgecomputing.org/ (accessed on 15 June 2019).
- Monroe, C.; Kim, J. Scaling the Ion Trap Quantum Processor. Science
**2013**, 339, 1164–1169. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Ohl de Mello, D.; Schäffner, D.; Werkmann, J.; Preuschoff, T.; Kohfahl, L.; Schlosser, M.; Birkl, G. Defect-Free Assembly of 2D Clusters of More Than 100 Single-Atom Quantum Systems. Phys. Rev. Lett.
**2019**, 122, 203601. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Molmer, K.; Sorensen, A. RISQ—Reduced instruction set quantum computers. J. Mod. Opt.
**2000**, 47, 2515–2527. [Google Scholar] [CrossRef] - Spassov, D.; Paskaleva, A.; Krajewski, T.A.; Guziewicz, E.; Luka, G.; Ivanov, T. Al
_{2}O_{3}/HfO_{2}Multilayer High-k Dielectric Stacks for Charge Trapping Flash Memories. Phys. Status Solidi A**2018**, 215. [Google Scholar] [CrossRef] - Amra, C.; Zerrad, M.; Lemarchand, F.; Lereu, A.; Passian, A.; Zapien, J.A.; Lequime, M. Energy density engineering via zero-admittance domains in all-dielectric stratified materials. Phys. Rev. A
**2018**, 97. [Google Scholar] [CrossRef] - Xu, W.G.; Liu, W.W.; Schmidt, J.F.; Zhao, W.J.; Lu, X.; Raab, T.; Diederichs, C.; Gao, W.B.; Seletskiy, D.V.; Xiong, Q.H. Correlated fluorescence blinking in two-dimensional semiconductor heterostructures. Nature
**2017**, 541, 62–67. [Google Scholar] [CrossRef] [PubMed] - Lereu, A.L.; Zerrad, M.; Passian, A.; Amra, C. Surface plasmons and Bloch surface waves: Towards optimized ultra-sensitive optical sensors. Appl. Phys. Lett.
**2017**, 111. [Google Scholar] [CrossRef] - Vigneau, F.; Mizokuchi, R.; Zanuz, D.C.; Huang, X.H.; Tang, S.S.; Maurand, R.; Frolov, S.; Sammak, A.; Scappucci, G.; Lefloch, F.; et al. Germanium Quantum-Well Josephson Field-Effect Transistors and Interferometers. Nano Lett.
**2019**, 19, 1023–1027. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Chen, C.; Youngblood, N.; Peng, R.M.; Yoo, D.; Mohr, D.A.; Johnson, T.W.; Oh, S.H.; Li, M. Three-Dimensional Integration of Black Phosphorus Photodetector with Silicon Photonics and Nanoplasmonics. Nano Lett.
**2017**, 17, 985–991. [Google Scholar] [CrossRef] - Davis, T.J.; Gomez, D.E.; Roberts, A. Plasmonic circuits for manipulating optical information. Nanophotonics
**2017**, 6, 543–559. [Google Scholar] [CrossRef] - Engel, M.; Steiner, M.; Lombardo, A.; Ferrari, A.C.; Lohneysen, H.V.; Avouris, P.; Krupke, R. Light-matter interaction in a microcavity-controlled graphene transistor. Nat. Commun.
**2012**, 3. [Google Scholar] [CrossRef] [PubMed] - Steiner, M.; Xia, F.N.; Qian, H.H.; Lin, Y.M.; Hartschuh, A.; Meixner, A.J.; Avouris, P. Carbon Nanotubes and Optical Confinement—Controlling Light Emission in Nanophotonic Devices. In Carbon Nanotubes and Associated Devices, Proceedings of the Nanoscience + Engineering, San Diego, CA, USA, 10–14 August 2008; Volume 7037, p. 10111712801630.
- Ray, S.K.; Katiyar, A.K.; Raychaudhuri, A.K. One-dimensional Si/Ge nanowires and their heterostructures for multifunctional applications—A review. Nanotechnology
**2017**, 28. [Google Scholar] [CrossRef] [PubMed] - Peng, J.; Sun, S.; Narayana, V.K.; Sorger, V.J.; El-Ghazawi, T. Residue number system arithmetic based on integrated nanophotonics. Opt. Lett.
**2018**, 43, 2026–2029. [Google Scholar] [CrossRef] [PubMed] - Otto, L.M.; Ogletree, D.F.; Aloni, S.; Staffaroni, M.; Stipe, B.C.; Hammack, A.T. Visualizing the bidirectional optical transfer function for near-field enhancement in waveguide coupled plasmonic transducers. Sci. Rep.
**2018**, 8. [Google Scholar] [CrossRef] [PubMed] - Yan, H.; Choe, H.S.; Nam, S.W.; Hu, Y.J.; Das, S.; Klemic, J.F.; Ellenbogen, J.C.; Lieber, C.M. Programmable nanowire circuits for nanoprocessors. Nature
**2011**, 470, 240–244. [Google Scholar] [CrossRef] - Crone, B.; Dodabalapur, A.; Lin, Y.Y.; Filas, R.W.; Bao, Z.; LaDuca, A.; Sarpeshkar, R.; Katz, H.E.; Li, W. Large-scale complementary integrated circuits based on organic transistors. Nature
**2000**, 403, 521–523. [Google Scholar] [CrossRef] [PubMed] - Gerasimov, J.Y.; Gabrielsson, R.; Forchheimer, R.; Stavrinidou, E.; Simon, D.T.; Berggren, M.; Fabiano, S. An Evolvable Organic Electrochemical Transistor for Neuromorphic Applications. Adv. Sci.
**2019**, 6, 1801339. [Google Scholar] [CrossRef] [Green Version] - Wu, T.F.; Li, H.T.; Huang, P.C.; Rahimi, A.; Hills, G.; Hodson, B.; Hwang, W.; Rabaey, J.M.; Wong, H.S.P.; Shulaker, M.M.; et al. Hyperdimensional Computing Exploiting Carbon Nanotube FETs, Resistive RAM, and Their Monolithic 3D Integration. IEEE J. Solid-State Circuits
**2018**, 53, 3183–3196. [Google Scholar] [CrossRef] - Luo, S.; Song, M.; Li, X.; Zhang, Y.; Hong, J.; Yang, X.; Zou, X.; Xu, N.; You, L. Reconfigurable Skyrmion Logic Gates. Nano Lett.
**2018**, 18, 1180–1184. [Google Scholar] [CrossRef] - Sharma, H.; Sandha, K.S. Multilayer Graphene Nanoribbon (MLGNR) as VLSI Interconnect Material at Nano-scaled Technology Nodes. Trans. Electr. Electron. Mater.
**2018**, 19, 456–461. [Google Scholar] [CrossRef] - Paddubskaya, A.; Shuba, M.; Maksimenko, S.; Maffucci, A. Plasmonic carbon interconnects to enable the THz technology: Properties and limits. In Proceedings of the 2017 IEEE 21st Workshop on Signal and Power Integrity (SPI), Baveno, Italy, 7–10 May 2017. [Google Scholar]
- Chen, Z.H. Applications of 2D Materials in Interconnect Technology. In Proceedings of the 2018 International Symposium on Vlsi Technology, Systems and Application (Vlsi-Tsa), Hsinchu, Taiwan, 16–19 April 2018. [Google Scholar]
- Vyas, A.A.; Zhou, C.J.; Yang, C.Y. On-Chip Interconnect Conductor Materials for End-of-Roadmap Technology Nodes. IEEE Trans. Nanotechnol.
**2018**, 17, 4–10. [Google Scholar] [CrossRef] - Xia, Z.B.; Wang, C.Y.; Kalarickal, N.K.; Stemmer, S.; Rajan, S. Design of Transistors Using High-Permittivity Materials. IEEE Trans. Electron Devices
**2019**, 66, 896–900. [Google Scholar] [CrossRef] - Rios, C.; Youngblood, N.; Cheng, Z.G.; Le Gallo, M.; Pernice, W.H.P.; Wright, C.D.; Sebastian, A.; Bhaskaran, H. In-memory computing on a photonic platform. Sci. Adv.
**2019**, 5. [Google Scholar] [CrossRef] [PubMed] - Hu, W.G.; Zhang, C.; Wang, Z.L. Recent progress in piezotronics and tribotronics. Nanotechnology
**2019**, 30. [Google Scholar] [CrossRef] [PubMed] - Alam, M.A.; Si, M.; Ye, P.D. A critical review of recent progress on negative capacitance field-effect transistors. Appl. Phys. Lett.
**2019**, 114, 090401. [Google Scholar] [CrossRef] [Green Version] - Sun, H.D.; Gerasimov, J.; Berggren, M.; Fabiano, S. n-Type organic electrochemical transistors: Materials and challenges. J. Mater. Chem. C
**2018**, 6, 11778–11784. [Google Scholar] [CrossRef] - Schanze, K.S. Forum on Materials and Interfaces for Next-Generation Thin-Film Transistors. ACS Appl. Mater. Interfaces
**2018**, 10, 25833. [Google Scholar] [CrossRef] - Iannaccone, G.; Bonaccorso, F.; Colombo, L.; Fiori, G. Quantum engineering of transistors based on 2D materials heterostructures. Nat. Nanotechnol.
**2018**, 13, 183–191. [Google Scholar] [CrossRef] - Hwang, C.S.; Dieny, B. Advanced memory-Materials for a new era of information technology. MRS Bull.
**2018**, 43, 330–333. [Google Scholar] [CrossRef] - Zhang, Y.H.; Mei, Z.X.; Liang, H.L.; Du, X.L. Review of flexible and transparent thin-film transistors based on zinc oxide and related materials. Chin. Phys. B
**2017**, 26. [Google Scholar] [CrossRef] - Kumar, B.; Kaushik, B.K.; Negi, Y.S. Organic Thin Film Transistors: Structures, Models, Materials, Fabrication, and Applications: A Review. Polym. Rev.
**2014**, 54, 33–111. [Google Scholar] [CrossRef] - Zhou, Y.; Ramanathan, S. Correlated Electron Materials and Field Effect Transistors for Logic: A Review. Crit. Rev. Solid State Mater. Sci.
**2013**, 38, 286–317. [Google Scholar] [CrossRef] [Green Version] - Dekker, C. How we made the carbon nanotube transistor. Nat. Electron.
**2018**, 1, 518. [Google Scholar] [CrossRef] - Han, S.J.; Tang, J.S.; Kumar, B.; Falk, A.; Farmer, D.; Tulevski, G.; Jenkins, K.; Afzali, A.; Oida, S.; Ott, J.; et al. High-speed logic integrated circuits with solution-processed self-assembled carbon nanotubes. Nat. Nanotechnol.
**2017**, 12, 861–865. [Google Scholar] [CrossRef] [PubMed] - Hu, Z.Y.; Comeras, J.M.M.L.; Park, H.; Tang, J.S.; Afzali, A.; Tulevski, G.S.; Hannon, J.B.; Liehr, M.; Han, S.J. Physically unclonable cryptographic primitives using self-assembled carbon nanotubes. Nat. Nanotechnol.
**2016**, 11, 559–565. [Google Scholar] [CrossRef] [PubMed] - Cao, Q.; Han, S.J.; Tulevski, G.S.; Zhu, Y.; Lu, D.D.; Haensch, W. Arrays of single-walled carbon nanotubes with full surface coverage for high-performance electronics. Nat. Nanotechnol.
**2013**, 8, 180–186. [Google Scholar] [CrossRef] [PubMed] - Zhang, T.T.; Jiang, Y.; Song, Z.D.; Huang, H.; He, Y.Q.; Fang, Z.; Weng, H.M.; Fang, C. Catalogue of topological electronic materials. Nature
**2019**, 566, 475–479. [Google Scholar] [CrossRef] - Xue, H.R.; Yang, Y.H.; Gao, F.; Chong, Y.D.; Zhang, B.L. Acoustic higher-order topological insulator on a kagome lattice. Nat. Mater.
**2019**, 18, 108–112. [Google Scholar] [CrossRef] [PubMed] - Vergniory, M.G.; Elcoro, L.; Felser, C.; Regnault, N.; Bernevig, B.A.; Wang, Z.J. A complete catalogue of high-quality topological materials. Nature
**2019**, 566, 480–485. [Google Scholar] [CrossRef] [Green Version] - Tang, F.; Po, H.C.; Vishwanath, A.; Wan, X. Comprehensive search for topological materials using symmetry indicators. Nature
**2019**, 566, 486–489. [Google Scholar] [CrossRef] [Green Version] - Ni, X.; Weiner, M.; Alu, A.; Khanikaev, A.B. Observation of higher-order topological acoustic states protected by generalized chiral symmetry. Nat. Mater.
**2019**, 18, 113–120. [Google Scholar] [CrossRef] [PubMed] - He, H.L.; Qiu, C.Y.; Ye, L.P.; Cai, X.X.; Fan, X.Y.; Ke, M.Z.; Zhang, F.; Liu, Z.Y. Topological negative refraction of surface acoustic waves in a Weyl phononic crystal. Nature
**2018**, 560, 61–64. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Hafezi, M.; Mittal, S.; Fan, J.; Migdall, A.; Taylor, J.M. Imaging topological edge states in silicon photonics. Nat. Photonics
**2013**, 7, 1001–1005. [Google Scholar] [CrossRef] [Green Version] - Wachter, S.; Polyushkin, D.K.; Bethge, O.; Mueller, T. A microprocessor based on a two-dimensional semiconductor. Nat. Commun.
**2017**, 8, 14948. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Bardeen, J. Research Leading to Point-Contact Transistor. Science
**1957**, 126, 105–112. [Google Scholar] [CrossRef] [PubMed] - Aly, M.M.S.; Wu, T.F.; Bartolo, A.; Malviya, Y.H.; Hwang, W.; Hills, G.; Markov, I.; Wootters, M.; Shulaker, M.M.; Wong, H.S.P.; et al. The N3XT Approach to Energy-Efficient Abundant-Data Computing. Proc. IEEE
**2019**, 107, 19–48. [Google Scholar] [CrossRef] - Balestra, F. Nanoscale FETs for high performance and ultra low power operation at the end of the Roadmap. In Proceedings of the 2018 14th IEEE International Conference on Solid-State and Integrated Circuit Technology (ICSICT), Qingdao, China, 31 October–3 November 2018; pp. 46–49. [Google Scholar]
- Qiu, C.G.; Liu, F.; Xu, L.; Deng, B.; Xiao, M.M.; Si, J.; Lin, L.; Zhang, Z.Y.; Wang, J.; Guo, H.; et al. Dirac-source field-effect transistors as energy-efficient, high-performance electronic switches. Science
**2018**, 361, 387–391. [Google Scholar] [CrossRef] - Vasen, T.; Ramvall, P.; Afzalian, A.; Doornbos, G.; Holland, M.; Thelander, C.; Dick, K.A.; Wernersson, L.E.; Passlack, M. Vertical Gate-All-Around Nanowire GaSb-InAs Core-Shell n-Type Tunnel FETs. Sci. Rep.
**2019**, 9. [Google Scholar] [CrossRef] - Pandey, R.; Ghosh, R.; Datta, S. Band Structure Engineered Germanium-Tin (GeSn) p-channel Tunnel Transistors. In Proceedings of the 2016 International Symposium on VLSI Technology, Systems and Application (VLSI-TSA), Hsinchu, Taiwan, 25–27 April 2016. [Google Scholar]
- Nourbakhsh, A.; Zubair, A.; Sajjad, R.N.; Tavakkoli, K.G.A.; Chen, W.; Fang, S.; Ling, X.; Kong, J.; Dresselhaus, M.S.; Kaxiras, E.; et al. MoS
_{2}Field-Effect Transistor with Sub-10 nm Channel Length. Nano Lett.**2016**, 16, 7798–7806. [Google Scholar] [CrossRef] - Kinloch, I.A.; Suhr, J.; Lou, J.; Young, R.J.; Ajayan, P.M. Composites with carbon nanotubes and graphene: An outlook. Science
**2018**, 362, 547–553. [Google Scholar] [CrossRef] [Green Version] - Raychowdhury, A. MRAM and FinFETs team up. Nat. Electron.
**2018**, 1, 618–619. [Google Scholar] [CrossRef] - Liu, C.C.; Franke, E.; Mignot, Y.; Xie, R.L.; Yeung, C.W.; Zhang, J.Y.; Chi, C.; Zhang, C.; Farrell, R.; Lai, K.F.; et al. Directed self-assembly of block copolymers for 7 nanometre FinFET technology and beyond. Nat. Electron.
**2018**, 1, 562–569. [Google Scholar] [CrossRef] - Hills, G.; Bardon, M.G.; Doornbos, G.; Yakimets, D.; Schuddinck, P.; Baert, R.; Jang, D.Y.; Mattii, L.; Sherazi, S.M.Y.; Rodopoulos, D.; et al. Understanding Energy Efficiency Benefits of Carbon Nanotube Field-Effect Transistors for Digital VLSI. IEEE Trans. Nanotechnol.
**2018**, 17, 1259–1269. [Google Scholar] [CrossRef] - Zhang, P.P.; Qiu, C.G.; Zhang, Z.Y.; Ding, L.; Chen, B.Y.; Peng, L.M. Performance projections for ballistic carbon nanotube FinFET at circuit level. Nano Res.
**2016**, 9, 1785–1794. [Google Scholar] [CrossRef] - Mobarakeh, M.S.; Omrani, S.; Vali, M.; Bayani, A.; Omrani, N. Theoretical logic performance estimation of Silicon, Germanium and SiGe nanowire Fin-Field Effect Transistor. Superlattice Microstruct.
**2018**, 120, 578–587. [Google Scholar] [CrossRef] - Muller, K.; Bugnicourt, E.; Latorre, M.; Jorda, M.; Sanz, Y.E.; Lagaron, J.M.; Miesbauer, O.; Bianchin, A.; Hankin, S.; Bolz, U.; et al. Review on the Processing and Properties of Polymer Nanocomposites and Nanocoatings and Their Applications in the Packaging, Automotive and Solar Energy Fields. Nanomaterials
**2017**, 7, 74. [Google Scholar] [CrossRef] [PubMed] - Altebaeumer, T.; Gotsmann, B.; Pozidis, H.; Knoll, A.; Duerig, U. Nanoscale Shape-Memory Function in Highly Cross-Linked Polymers. Nano Lett.
**2008**, 8, 4398–4403. [Google Scholar] [CrossRef] - Vettiger, P.; Cross, G.; Despont, M.; Drechsler, U.; Durig, U.; Gotsmann, B.; Haberle, W.; Lantz, M.A.; Rothuizen, H.E.; Stutz, R.; et al. The “millipede”—Nanotechnology entering data storage. IEEE Trans. Nanotechnol.
**2002**, 1, 39–55. [Google Scholar] [CrossRef] - Cho, Y.; Hong, S. Scanning probe-type data storage beyond hard disk drive and flash memory. MRS Bull.
**2018**, 43, 365–370. [Google Scholar] [CrossRef] - Srimani, T.; Hills, G.; Bishop, M.D.; Radhakrishna, U.; Zubair, A.; Park, R.S.; Stein, Y.; Palacios, T.; Antoniadis, D.; Shulaker, M.M. Negative Capacitance Carbon Nanotube FETs. IEEE Electron Device Lett.
**2018**, 39, 304–307. [Google Scholar] [CrossRef] - Lau, C.; Srimani, T.; Bishop, M.D.; Hills, G.; Shulaker, M.M. Tunable n-Type Doping of Carbon Nanotubes through Engineered Atomic Layer Deposition HfOX Films. ACS Nano
**2018**, 12, 10924–10931. [Google Scholar] [CrossRef] [PubMed] - Kanhaiya, P.S.; Hills, G.; Antoniadis, D.A.; Shulaker, M.M. DISC-FETs: Dual Independent Stacked Channel Field-Effect Transistors. IEEE Electron Device Lett.
**2018**, 39, 1250–1253. [Google Scholar] [CrossRef] - Park, R.S.; Hills, G.; Sohn, J.; Mitra, S.; Shulaker, M.M.; Wong, H.S.P. Hysteresis-Free Carbon Nanotube Field-Effect Transistors. ACS Nano
**2017**, 11, 4785–4791. [Google Scholar] [CrossRef] [PubMed] - Gielen, G.; Van Rethy, J.; Marin, J.; Shulaker, M.M.; Hills, G.; Wong, H.S.P.; Mitra, S. Time-Based Sensor Interface Circuits in CMOS and Carbon Nanotube Technologies. IEEE Trans. Circuits Syst. I
**2016**, 63, 577–586. [Google Scholar] [CrossRef] - Pitkanen, O.; Jarvinen, T.; Cheng, H.; Lorite, G.S.; Dombovari, A.; Rieppo, L.; Talapatra, S.; Duong, H.M.; Toth, G.; Juhasz, K.L.; et al. On-chip integrated vertically aligned carbon nanotube based super- and pseudocapacitors. Sci. Rep.
**2017**, 7, 16594. [Google Scholar] [CrossRef] - Hills, G.; Bankman, D.; Moons, B.; Yang, L.T.; Hillard, J.; Kahng, A.; Park, R.; Verhelst, M.; Murmann, B.; Shulaker, M.M.; et al. TRIG: Hardware Accelerator for Inference-Based Applications and Experimental Demonstration Using Carbon Nanotube FETs. In Proceedings of the 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, 24–28 June 2018. [Google Scholar] [CrossRef]
- Hills, G.; Zhang, J.; Shulaker, M.M.; Wei, H.; Lee, C.S.; Balasingam, A.; Wong, H.S.P.; Mitra, S. Rapid Co-Optimization of Processing and Circuit Design to Overcome Carbon Nanotube Variations. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.
**2015**, 34, 1082–1095. [Google Scholar] [CrossRef] [Green Version] - Cao, Q.; Han, S.J.; Penumatcha, A.V.; Frank, M.M.; Tulevski, G.S.; Tersoff, J.; Haensch, W.E. Origins and characteristics of the threshold voltage variability of quasiballistic single-walled carbon nanotube field-effect transistors. ACS Nano
**2015**, 9, 1936–1944. [Google Scholar] [CrossRef] - Wei, H.; Wang, Z.X.; Tian, X.R.; Kall, M.; Xu, H.X. Cascaded logic gates in nanophotonic plasmon networks. Nat. Commun.
**2011**, 2. [Google Scholar] [CrossRef] - Siampour, H.; Kumar, S.; Bozhevolnyi, S.I. Nanofabrication of Plasmonic Circuits Containing Single Photon Sources. ACS Photonics
**2017**, 4, 1879–1884. [Google Scholar] [CrossRef] [Green Version] - Lundeberg, M.B.; Gao, Y.D.; Asgari, R.; Tan, C.; Van Duppen, B.; Autore, M.; Alonso-Gonzalez, P.; Woessner, A.; Watanabe, K.; Taniguchi, T.; et al. Tuning quantum nonlocal effects in graphene plasmonics. Science
**2017**, 357, 187–190. [Google Scholar] [CrossRef] - Savage, K.J.; Hawkeye, M.M.; Esteban, R.; Borisov, A.G.; Aizpurua, J.; Baumberg, J.J. Revealing the quantum regime in tunnelling plasmonics. Nature
**2012**, 491, 574–577. [Google Scholar] [CrossRef] [PubMed] - Morton, J.J.L.; McCamey, D.R.; Eriksson, M.A.; Lyon, S.A. Embracing the quantum limit in silicon computing. Nature
**2011**, 479, 345–353. [Google Scholar] [CrossRef] [PubMed] - Wu, J.Y.; Liu, B.Y.; Peng, J.Z.; Mao, J.M.; Jiang, X.H.; Qiu, C.Y.; Tremblay, C.; Su, Y.K. On-Chip Tunable Second-Order Differential-Equation Solver Based on a Silicon Photonic Mode-Split Microresonator. J. Lightwave Technol.
**2015**, 33, 3542–3549. [Google Scholar] [CrossRef] - Polman, A. Photonic materials—Teaching silicon new tricks. Nat. Mater.
**2002**, 1, 10–12. [Google Scholar] [CrossRef] [PubMed] - Dong, P.; Kim, K.W.; Melikyan, A.; Baeyens, Y. Silicon Photonics: A Scaling Technology for Communications and Interconnects. In Proceedings of the 2018 IEEE International Electron Devices Meeting (IEDM), San Francisco, CA, USA, 1–5 December 2018. [Google Scholar]
- Vishkin, U.; Smolyaninov, I.; Davis, C. Plasmonics and the parallel programming problem—Art no. 64770M. In Proceedings of the Silicon Photonics II, San Jose, CA, USA, 22–27 January 2007; Volume 6477. [Google Scholar] [CrossRef]
- Lereu, A.L. Modulation—Plasmons lend a helping hand. Nat. Photonics
**2007**, 1, 368–369. [Google Scholar] [CrossRef] - Passian, A.; Lereu, A.L.; Arakawa, E.T.; Wig, A.; Thundat, T.; Ferrell, T.L. Modulation of multiple photon energies by use of surface plasmons. Opt. Lett.
**2005**, 30, 41–43. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Passian, A.; Lereu, A.L.; Ritchie, R.H.; Meriaudeau, F.; Thundat, T.; Ferrell, T.L. Surface plasmon assisted thermal coupling of multiple photon energies. Thin Solid Film
**2006**, 497, 315–320. [Google Scholar] [CrossRef] - He, X.; Htoon, H.; Doorn, S.K.; Pernice, W.H.P.; Pyatkov, F.; Krupke, R.; Jeantet, A.; Chassagneux, Y.; Voisin, C. Carbon nanotubes as emerging quantum-light sources. Nat. Mater.
**2018**, 17, 663–670. [Google Scholar] [CrossRef] - Tans, S.J.; Verschueren, A.R.M.; Dekker, C. Room-temperature transistor based on a single carbon nanotube. Nature
**1998**, 393, 49–52. [Google Scholar] [CrossRef] - Postma, H.W.C.; Teepen, T.; Yao, Z.; Grifoni, M.; Dekker, C. Carbon nanotube single-electron transistors at room temperature. Science
**2001**, 293, 76–79. [Google Scholar] [CrossRef] - Bachtold, A.; Hadley, P.; Nakanishi, T.; Dekker, C. Logic circuits with carbon nanotube transistors. Science
**2001**, 294, 1317–1320. [Google Scholar] [CrossRef] [PubMed] - Tang, X.P.; Kleinhammes, A.; Shimoda, H.; Fleming, L.; Bennoune, K.Y.; Sinha, S.; Bower, C.; Zhou, O.; Wu, Y. Electronic structures of single-walled carbon nanotubes determined by NMR. Science
**2000**, 288, 492–494. [Google Scholar] [CrossRef] [PubMed] - Hone, J.; Batlogg, B.; Benes, Z.; Johnson, A.T.; Fischer, J.E. Quantized phonon spectrum of single-wall carbon nanotubes. Science
**2000**, 289, 1730–1733. [Google Scholar] [CrossRef] [PubMed] - Zhang, H.; Liu, C.X.; Gazibegovic, S.; Xu, D.; Logan, J.A.; Wang, G.Z.; van Loo, N.; Bommer, J.D.S.; de Moor, M.W.A.; Car, D.; et al. Quantized Majorana conductance. Nature
**2018**, 556, 74–79. [Google Scholar] [CrossRef] [PubMed] - Kim, S.; Yan, R.X. Recent developments in photonic, plasmonic and hybrid nanowire waveguides. J. Mater. Chem. C
**2018**, 6, 11795–11816. [Google Scholar] [CrossRef] - Chen, Y.; Lee, C.; Lu, L.; Liu, D.; Wu, Y.K.; Feng, L.T.; Li, M.; Rockstuhl, C.; Guo, G.P.; Guo, G.C.; et al. Quantum plasmonic NOON state in a silver nanowire and its use for quantum sensing. Optica
**2018**, 5, 1229–1235. [Google Scholar] [CrossRef] - Gazibegovic, S.; Car, D.; Zhang, H.; Balk, S.C.; Logan, J.A.; de Moor, M.W.A.; Cassidy, M.C.; Schmits, R.; Xu, D.; Wang, G.Z.; et al. Epitaxy of advanced nanowire quantum devices. Nature
**2017**, 548, 434–438. [Google Scholar] [CrossRef] - Petersson, K.D.; McFaul, L.W.; Schroer, M.D.; Jung, M.; Taylor, J.M.; Houck, A.A.; Petta, J.R. Circuit quantum electrodynamics with a spin qubit. Nature
**2012**, 490, 380–383. [Google Scholar] [CrossRef] [Green Version] - Nadj-Perge, S.; Frolov, S.M.; Bakkers, E.P.A.M.; Kouwenhoven, L.P. Spin-orbit qubit in a semiconductor nanowire. Nature
**2010**, 468, 1084–1087. [Google Scholar] [CrossRef] - Buonacorsi, B.; Cai, Z.Y.; Ramirez, E.B.; Willick, K.S.; Walker, S.M.; Li, J.H.; Shaw, B.D.; Xu, X.S.; Benjamin, S.C.; Baugh, J. Network architecture for a topological quantum computer in silicon. Quantum Sci. Technol.
**2019**, 4. [Google Scholar] [CrossRef] - Zhang, X.; Li, H.O.; Wang, K.; Cao, G.; Xiao, M.; Guo, G.P. Qubits based on semiconductor quantum dots. Chin. Phys. B
**2018**, 27. [Google Scholar] [CrossRef] - Watson, T.F.; Philips, S.G.J.; Kawakami, E.; Ward, D.R.; Scarlino, P.; Veldhorst, M.; Savage, D.E.; Lagally, M.G.; Friesen, M.; Coppersmith, S.N.; et al. A programmable two-qubit quantum processor in silicon. Nature
**2018**, 555, 633–637. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Kim, J.H.; Aghaeimeibodi, S.; Richardson, C.J.K.; Leavitt, R.P.; Waks, E. Super-Radiant Emission from Quantum Dots in a Nanophotonic Waveguide. Nano Lett.
**2018**, 18, 4734–4740. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Fogarty, M.A.; Chan, K.W.; Hensen, B.; Huang, W.; Tanttu, T.; Yang, C.H.; Laucht, A.; Veldhorst, M.; Hudson, F.E.; Itoh, K.M.; et al. Integrated silicon qubit platform with single-spin addressability, exchange control and single-shot singlet-triplet readout. Nat. Commun.
**2018**, 9. [Google Scholar] [CrossRef] [PubMed] - Wu, X.F.; Jiang, P.; Razinskas, G.; Huo, Y.H.; Zhang, H.Y.; Kamp, M.; Rastelli, A.; Schmidt, O.G.; Hecht, B.; Lindfors, K.; et al. On-Chip Single-Plasmon Nanocircuit Driven by a Self-Assembled Quantum Dot. Nano Lett.
**2017**, 17, 4291–4296. [Google Scholar] [CrossRef] [PubMed] - Veldhorst, M.; Eenink, H.G.J.; Yang, C.H.; Dzurak, A.S. Silicon CMOS architecture for a spin-based quantum computer. Nat. Commun.
**2017**, 8. [Google Scholar] [CrossRef] - Veldhorst, M.; Yang, C.H.; Hwang, J.C.C.; Huang, W.; Dehollain, J.P.; Muhonen, J.T.; Simmons, S.; Laucht, A.; Hudson, F.E.; Itoh, K.M.; et al. A two-qubit logic gate in silicon. Nature
**2015**, 526, 410–414. [Google Scholar] [CrossRef] - Zu, C.; Wang, W.B.; He, L.; Zhang, W.G.; Dai, C.Y.; Wang, F.; Duan, L.M. Experimental realization of universal geometric quantum gates with solid-state spins. Nature
**2014**, 514, 72–75. [Google Scholar] [CrossRef] [Green Version] - Hemmer, P.; Lukin, M. Room-temperature solid-state quantum processors in diamond. In Proceedings of the Quantum Information and Computation VI, Orlando, FL, USA, 19–20 March 2008; Volume 6976. [Google Scholar] [CrossRef]
- Cirac, J.I.; Zoller, P. A scalable quantum computer with ions in an array of microtraps. Nature
**2000**, 404, 579–581. [Google Scholar] [CrossRef] - Faraji-Dana, M.; Arbabi, E.; Arbabi, A.; Kamali, S.M.; Kwon, H.; Faraon, A. Compact folded metasurface spectrometer. Nat. Commun.
**2018**, 9. [Google Scholar] [CrossRef] - Babashah, H.; Kavehvash, Z.; Koohi, S.; Khavasi, A. Integration in analog optical computing using metasurfaces revisited: Toward ideal optical integration. J. Opt. Soc. Am. B
**2017**, 34, 1270–1279. [Google Scholar] [CrossRef] - Achouri, K.; Lavigne, G.; Salem, M.A.; Caloz, C. Metasurface Spatial Processor for Electromagnetic Remote Control. IEEE Trans. Antennas Propag.
**2016**, 64, 1759–1767. [Google Scholar] [CrossRef] [Green Version] - Lu, L.; Joannopoulos, J.D.; Soljacic, M. Topological photonics. Nat. Photonics
**2014**, 8, 821–829. [Google Scholar] [CrossRef] [Green Version] - Friedman, R.S.; McAlpine, M.C.; Ricketts, D.S.; Ham, D.; Lieber, C.M. High-speed integrated nanowire circuits. Nature
**2005**, 434, 1085. [Google Scholar] [CrossRef] [PubMed] - Wang, R.X.; Xia, H.Y.; Zhang, D.G.; Chen, J.X.; Zhu, L.F.; Wang, Y.; Yang, E.C.; Zang, T.Y.; Wen, X.L.; Zou, G.; et al. Bloch surface waves confined in one dimension with a single polymeric nanofibre. Nat. Commun.
**2017**, 8. [Google Scholar] [CrossRef] - Tans, S.J.; Dekker, C. Molecular transistors—Potential modulations along carbon nanotubes. Nature
**2000**, 404, 834–835. [Google Scholar] [CrossRef] - Park, R.S.; Shulaker, M.M.; Hills, G.; Liyanage, L.S.; Lee, S.; Tang, A.; Mitra, S.; Wong, H.S.P. Hysteresis in Carbon Nanotube Transistors: Measurement and Analysis of Trap Density, Energy Level, and Spatial Distribution. ACS Nano
**2016**, 10, 4599–4608. [Google Scholar] [CrossRef] - Cao, Q.; Kim, H.S.; Pimparkar, N.; Kulkarni, J.P.; Wang, C.J.; Shim, M.; Roy, K.; Alam, M.A.; Rogers, J.A. Medium-scale carbon nanotube thin-film integrated circuits on flexible plastic substrates. Nature
**2008**, 454, 495–500. [Google Scholar] [CrossRef] - Yamamoto, T.; Watanabe, K.; Hernandez, E.R. Mechanical properties, thermal stability and heat transport in carbon nanotubes. Top. Appl. Phys.
**2008**, 111, 165–194. [Google Scholar] - Zhang, S.; Kang, L.; Wang, X.; Tong, L.; Yang, L.; Wang, Z.; Qi, K.; Deng, S.; Li, Q.; Bai, X.; et al. Arrays of horizontal carbon nanotubes of controlled chirality grown using designed catalysts. Nature
**2017**, 543, 234–238. [Google Scholar] [CrossRef] - Shulaker, M.M.; Hills, G.; Park, R.S.; Howe, R.T.; Saraswat, K.; Wong, H.S.P.; Mitra, S. Three-dimensional integration of nanotechnologies for computing and data storage on a single chip. Nature
**2017**, 547, 74–78. [Google Scholar] [CrossRef] [PubMed] - Gielen, G.; Van Rethy, J.; Shulaker, M.M.; Hills, G.; Wong, H.S.P.; Mitra, S. Time-Based Sensor Interface Circuits in Carbon Nanotube Technology. In Proceedings of the 2015 IEEE International Symposium on Circuits and Systems (ISCAS), Lisbon, Portugal, 24–27 May 2015; pp. 2924–2927. [Google Scholar]
- McCoy, M. Nantero to move nanotubes into computer chips. Chem. Eng. News Arch.
**2004**, 82, 14. [Google Scholar] [CrossRef] - Wolf, L. The Nanotube Computer Debuts. Chem. Eng. News Arch.
**2013**, 91, 7. [Google Scholar] - Winkless, L. Carbon nanotube computer becomes reality. Mater. Today
**2013**, 16, 415–416. [Google Scholar] [CrossRef] - Welter, K. The First Carbon Nanotube Computer. ChemPhysChem
**2013**, 14, 3439. [Google Scholar] - Wei, H.; Shulaker, M.; Wong, H.S.P.; Mitra, S. Monolithic Three-Dimensional Integration of Carbon Nanotube FET Complementary Logic Circuits. In Proceedings of the 2013 IEEE International Electron Devices Meeting (IEDM), Honolulu, HI, USA, 9–12 June 2013. [Google Scholar]
- Wei, H.; Shulaker, M.; Hills, G.; Chen, H.Y.; Lee, C.S.; Liyanage, L.; Zhang, J.; Wong, H.S.P.; Mitra, S. Carbon Nanotube Circuits: Opportunities and Challenges. In Proceedings of the Conference on Design, Automation and Test in Europe, Grenoble, France, 18–22 March 2013; pp. 619–624. [Google Scholar]
- Talbot, D. Nanotube Computers. Technol. Rev.
**2013**, 116, 84–86. [Google Scholar] - Shulaker, M.M.; Hills, G.; Patil, N.; Wei, H.; Chen, H.Y.; PhilipWong, H.S.; Mitra, S. Carbon nanotube computer. Nature
**2013**, 501, 526–530. [Google Scholar] [CrossRef] - Shulaker, M.; Van Rethy, J.; Hills, G.; Chen, H.Y.; Gielen, G.; Wong, H.S.P.; Mitra, S. Experimental Demonstration of a Fully Digital Capacitive Sensor Interface Built Entirely Using Carbon-Nanotube FETs. In Proceedings of the 2013 IEEE International Solid-State Circuits Conference Digest of Technical Papers, San Francisco, CA, USA, 17–21 February 2013; Volume 56, pp. 112–113. [Google Scholar]
- Shulaker, M.; Van Rethy, J.; Hills, G.; Chen, H.Y.; Gielen, G.; Wong, H.S.P.; Mitra, S. Sacha: The Stanford Carbon Nanotube Controlled Handshaking Robot. In Proceedings of the 50th Annual Design Automation Conference, Austin, TX, USA, 29 May–7 June 2013. [Google Scholar]
- Sealy, C. Scientists switch on first carbon nanotube computer. Nano Today
**2013**, 8, 555–556. [Google Scholar] [CrossRef] - Kreupl, F. ELECTRONICS The carbon-nanotube computer has arrived. Nature
**2013**, 501, 495–496. [Google Scholar] [CrossRef] - Garber, L. Researchers Build First Carbon-Nanotube Computer. Computer
**2013**, 46, 21–22. [Google Scholar] - Wong, H.S.P.; Mitra, S.; Akinwande, D.; Beasley, C.; Chai, Y.; Chen, H.Y.; Chen, X.Y.; Close, G.; Deng, J.; Hazeghi, A.; et al. Carbon Nanotube Electronics—Materials, Devices, Circuits, Design, Modeling, and Performance Projection. In Proceedings of the 2011 IEEE International Electron Devices Meeting (IEDM), Washington, DC, USA, 5–7 December 2011. [Google Scholar]
- Wei, H.; Zhang, J.; Wei, L.; Patil, N.; Lin, A.; Shulaker, M.M.; Chen, H.Y.; Wong, H.S.P.; Mitra, S. Carbon Nanotube Imperfection-Immune Digital VLSI: Frequently Asked Questions Updated Invited Paper. In Proceedings of the International Conference on Computer-Aided Design, San Jose, CA, USA, 7–10 November 2011; pp. 227–230. [Google Scholar]
- Faster nanotube transistors can speed computers. Intech
**2004**, 51, 6–18. - Chen, Z.H.; Appenzeller, J.; Lin, Y.M.; Sippel-Oakley, J.; Rinzler, A.G.; Tang, J.Y.; Wind, S.J.; Solomon, P.M.; Avouris, P. An integrated logic circuit assembled on a single carbon nanotube. Science
**2006**, 311, 1735. [Google Scholar] [CrossRef] [PubMed] - Sandha, K.S.; Thakur, A. Comparative Analysis of Mixed CNTs and MWCNTs as VLSI Interconnects for Deep Sub-micron Technology Nodes. J. Electron. Mater.
**2019**, 48, 2543–2554. [Google Scholar] [CrossRef] - Dale, M.; Miller, J.F.; Stepney, S.; Trefzer, M.A. Evolving Carbon Nanotube Reservoir Computers. Lect. Notes Comput. Sci.
**2016**, 9726, 49–61. [Google Scholar] [CrossRef] - Tanaka, G.; Yamane, T.; Héroux, J.B.; Nakane, R.; Kanazawa, N.; Takeda, S.; Numata, H.; Nakano, D.; Hirose, A. Recent advances in physical reservoir computing: A review. Neural Netw.
**2019**, 115, 100–123. [Google Scholar] [CrossRef] [PubMed] - Ouyang, M.; Huang, J.L.; Cheung, C.L.; Lieber, C.M. Energy gaps in “metallic” single-walled carbon nanotubes. Science
**2001**, 292, 702–705. [Google Scholar] [CrossRef] - Hou, Q.W.; Cao, B.Y.; Guo, Z.Y. Thermal conductivity of carbon nanotube: From ballistic to diffusive transport. Acta Phys. Sin.
**2009**, 58, 7809–7814. [Google Scholar] - Donadio, D.; Galli, G. Thermal Conductivity of Isolated and Interacting Carbon Nanotubes: Comparing Results from Molecular Dynamics and the Boltzmann Transport Equation. Phys. Rev. Lett.
**2007**, 99, 255502. [Google Scholar] [CrossRef] - Ilani, S.; McEuen, P.L. Electron Transport in Carbon Nanotubes. Annu. Rev. Condens. Matter Phys.
**2010**, 1, 1–25. [Google Scholar] [CrossRef] - Chiodarelli, N.; Fournier, A.; Dijon, J. Impact of the contact’s geometry on the line resistivity of carbon nanotubes bundles for applications as horizontal interconnects. Appl. Phys. Lett.
**2013**, 103. [Google Scholar] [CrossRef] - Chiodarelli, N.; Masahito, S.; Kashiwagi, Y.; Li, Y.L.; Arstila, K.; Richard, O.; Cott, D.J.; Heyns, M.; De Gendt, S.; Groeseneken, G.; et al. Measuring the electrical resistivity and contact resistance of vertical carbon nanotube bundles for application as interconnects. Nanotechnology
**2011**, 22. [Google Scholar] [CrossRef] [PubMed] - Bandaru, P.R. Electrical properties and applications of carbon nanotube structures. J. Nanosci. Nanotechnol.
**2007**, 7, 1239–1267. [Google Scholar] [CrossRef] [PubMed] - Sfeir, M.Y.; Beetz, T.; Wang, F.; Huang, L.M.; Huang, X.M.H.; Huang, M.Y.; Hone, J.; O’Brien, S.; Misewich, J.A.; Heinz, T.F.; et al. Optical spectroscopy of individual single-walled carbon nanotubes of defined chiral structure. Science
**2006**, 312, 554–556. [Google Scholar] [CrossRef] [PubMed] - Lee, J.; Stein, I.Y.; Devoe, M.E.; Lewis, D.J.; Lachman, N.; Kessler, S.S.; Buschhorn, S.T.; Wardle, B.L. Impact of carbon nanotube length on electron transport in aligned carbon nanotube networks. Appl. Phys. Lett.
**2015**, 106. [Google Scholar] [CrossRef] - Pyatkov, F.; Futterling, V.; Khasminskaya, S.; Flavel, B.S.; Hennrich, F.; Kappes, M.M.; Krupke, R.; Pernice, W.H.P. Cavity-enhanced light emission from electrically driven carbon nanotubes. Nat. Photonics
**2016**, 10, 420–427. [Google Scholar] [CrossRef] - Xu, J.L.; Dai, R.X.; Xin, Y.; Sun, Y.L.; Li, X.; Yu, Y.X.; Xiang, L.; Xie, D.; Wang, S.D.; Ren, T.L. Efficient and Reversible Electron Doping of Semiconductor-Enriched Single-Walled Carbon Nanotubes by Using Decamethylcobaltocene. Sci. Rep.
**2017**, 7. [Google Scholar] [CrossRef] [PubMed] - Srimani, T.; Hills, G.; Bishop, M.D.; Shulaker, M.M. 30-nm Contacted Gate Pitch Back-Gate Carbon Nanotube FETs for Sub-3-nm Nodes. IEEE Trans. Nanotechnol.
**2019**, 18, 132–138. [Google Scholar] [CrossRef] - Shulaker, M.M.; Wu, T.F.; Pal, A.; Zhao, L.; Nishi, Y.; Saraswat, K.; Wong, H.S.P.; Mitra, S. Monolithic 3D Integration of Logic and Memory: Carbon Nanotube FETs, Resistive RAM, and Silicon FETs. In Proceedings of the 2014 IEEE International Electron Devices Meeting, San Francisco, CA, USA, 15–17 December 2014. [Google Scholar]
- Shulaker, M.M.; Van Rethy, J.; Wu, T.F.; Liyanage, L.S.; Wei, H.; Li, Z.Y.; Pop, E.; Gielen, G.; Wong, H.S.P.; Mitra, S. Carbon Nanotube Circuit Integration up to Sub-20 nm Channel Lengths. ACS Nano
**2014**, 8, 3434–3443. [Google Scholar] [CrossRef] - Shulaker, M.M.; Van Rethy, J.; Hills, G.; Wei, H.; Chen, H.Y.; Gielen, G.; Wong, H.S.P.; Mitra, S. Sensor-to-Digital Interface Built Entirely with Carbon Nanotube FETs. IEEE J. Solid-State Circuits
**2014**, 49, 190–201. [Google Scholar] [CrossRef] - Shulaker, M.M.; Saraswat, K.; Wong, H.S.P.; Mitra, S. Monolithic Three-Dimensional Integration of Carbon Nanotube FETs with Silicon CMOS. In Proceedings of the 2014 Symposium on VLSI Technology (VLSI-Technology): Digest of Technical Papers, Honolulu, HI, USA, 9–12 June 2014. [Google Scholar]
- Shulaker, M.M.; Pitner, G.; Hills, G.; Giachino, M.; Wong, H.S.P.; Mitra, S. High-Performance Carbon Nanotube Field-Effect Transistors. In Proceedings of the 2014 IEEE International Electron Devices Meeting, San Francisco, CA, USA, 15–17 December 2014. [Google Scholar]
- Shulaker, M.; Hills, G.; Wei, H.; Chen, H.Y.; Patil, N.; Wong, H.S.P.; Mitra, S. Advancements With Carbon Nanotube Digital Systems. In Proceedings of the 2014 IEEE International Interconnect Technology Conference/Advanced Metallization Conference (IITC/AMC), San Jose, CA, USA, 20–23 May 2014; pp. 319–321. [Google Scholar]
- Hills, G.; Shulaker, M.; Wei, H.; Chen, H.Y.; Wong, H.S.P.; Mitra, S. Robust Design and Experimental Demonstrations of Carbon Nanotube Digital Circuits. In Proceedings of the IEEE 2014 Custom Integrated Circuits Conference, San Jose, CA, USA, 15–17 September 2014. [Google Scholar]
- Keren, K.; Berman, R.S.; Buchstab, E.; Sivan, U.; Braun, E. DNA-templated carbon nanotube field-effect transistor. Science
**2003**, 302, 1380–1382. [Google Scholar] [CrossRef] - Carbon nanotube computers. Technol. Rev.
**2006**, 109, 92. - Jacoby, M. ACS meeting—Carbon nanotube computer circuits—Novel processing and microfabrication lead to first single-molecule logic gate. Chem. Eng. News
**2001**, 79, 9. [Google Scholar] [CrossRef] - Jarillo-Herrero, P.; van Dam, J.A.; Kouwenhoven, L.P. Quantum supercurrent transistors in carbon nanotubes. Nature
**2006**, 439, 953–956. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Colwell, R.P. How we made the Pentium processors. Nat. Electron.
**2019**, 2, 83–84. [Google Scholar] [CrossRef] - Qiu, C.G.; Zhang, Z.Y.; Xiao, M.M.; Yang, Y.J.; Zhong, D.L.; Peng, L.M. Scaling carbon nanotube complementary transistors to 5-nm gate lengths. Science
**2017**, 355, 271–276. [Google Scholar] [CrossRef] [PubMed] - Franklin, A.D. ELECTRONICS The road to carbon nanotube transistors. Nature
**2013**, 498, 443–444. [Google Scholar] [CrossRef] [PubMed] - LeMieux, M.C.; Roberts, M.; Barman, S.; Jin, Y.W.; Kim, J.M.; Bao, Z.N. Self-sorted, aligned nanotube networks for thin-film transistors. Science
**2008**, 321, 101–104. [Google Scholar] [CrossRef] [PubMed] - Kanungo, M.; Lu, H.; Malliaras, G.G.; Blanchet, G.B. Suppression of Metallic Conductivity of Single-Walled Carbon Nanotubes by Cycloaddition Reactions. Science
**2009**, 323, 234–237. [Google Scholar] [CrossRef] [PubMed] - Jin, S.H.; Dunham, S.N.; Song, J.Z.; Xie, X.; Kim, J.H.; Lu, C.F.; Islam, A.; Du, F.; Kim, J.; Felts, J.; et al. Using nanoscale thermocapillary flows to create arrays of purely semiconducting single-walled carbon nanotubes. Nat. Nanotechnol.
**2013**, 8, 347–355. [Google Scholar] [CrossRef] - Park, H.; Afzali, A.; Han, S.J.; Tulevski, G.S.; Franklin, A.D.; Tersoff, J.; Hannon, J.B.; Haensch, W. High-density integration of carbon nanotubes via chemical self-assembly. Nat. Nanotechnol.
**2012**, 7, 787–791. [Google Scholar] [CrossRef] - Javey, A.; Guo, J.; Wang, Q.; Lundstrom, M.; Dai, H.J. Ballistic carbon nanotube field-effect transistors. Nature
**2003**, 424, 654–657. [Google Scholar] [CrossRef] [PubMed] - Odom, T.W.; Huang, J.L.; Kim, P.; Lieber, C.M. Atomic structure and electronic properties of single-walled carbon nanotubes. Nature
**1998**, 391, 62–64. [Google Scholar] [CrossRef] - International Technology Roadmap for Semiconductors 2.0 2015 Edition. 2015. Available online: www.itrs2.net/itrs-reports.html (accessed on 15 June 2019).
- Cao, Q.; Tersoff, J.; Farmer, D.B.; Zhu, Y.; Han, S.-J. Carbon nanotube transistors scaled to a 40-nanometer footprint. Science
**2017**, 356, 1369–1372. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Vandenberghe, W. Two-dimensional Topological Insulator Transistors as Energy Efficient Switches Robust against Material and Device Imperfections. In Proceedings of the 2017 Fifth Berkeley Symposium on Energy Efficient Electronic Systems & Steep Transistors Workshop (E3S), Berkeley, CA, USA, 19–20 October 2017. [Google Scholar]
- Vandenberghe, W.G.; Fischetti, M.V. Imperfect two-dimensional topological insulator field-effect transistors. Nat. Commun.
**2017**, 8, 14184. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Bradlyn, B.; Elcoro, L.; Cano, J.; Vergniory, M.G.; Wang, Z.; Felser, C.; Aroyo, M.I.; Bernevig, B.A. Topological quantum chemistry. Nature
**2017**, 547, 298–305. [Google Scholar] [CrossRef] [PubMed] - Ginley, T.; Wang, Y.; Wang, Z.; Law, S. Dirac plasmons and beyond: The past, present, and future of plasmonics in 3D topological insulators. MRS Commun.
**2018**, 8, 782–794. [Google Scholar] [CrossRef] - Okuyama, R.; Izumida, W.; Eto, M. Topological classification of the single-wall carbon nanotube. Phys. Rev. B
**2019**, 99. [Google Scholar] [CrossRef] - Pelzman, C.; Chanover, N.; Voelz, D.; Cho, S.Y. Plasmonic device for spectral analysis. Electron. Lett.
**2019**, 55, 142–143. [Google Scholar] [CrossRef] - Liu, Z.H.; Ding, L.Z.; Yi, J.P.; Wei, Z.C.; Guo, J.P. Design of a multi-bits input optical logic device with high intensity contrast based on plasmonic waveguides structure. Opt. Commun.
**2019**, 430, 112–118. [Google Scholar] [CrossRef] - Ciminelli, C.; Dell’Olio, F.; Conteduca, D.; Armenise, M.N. Integrated Photonic and Plasmonic Resonant Devices for Label-Free Biosensing and Trapping at the Nanoscale. Phys. Status Solidi A
**2019**, 216. [Google Scholar] [CrossRef] - Yan, H.G.; Li, X.S.; Chandra, B.; Tulevski, G.; Wu, Y.Q.; Freitag, M.; Zhu, W.J.; Avouris, P.; Xia, F.N. Tunable infrared plasmonic devices using graphene/insulator stacks. Nat. Nanotechnol.
**2012**, 7, 330–334. [Google Scholar] [CrossRef] [Green Version] - Zhao, W.J.; Qi, J.W.; Lu, Y.; Wang, R.D.; Zhang, Q.; Xiong, H.; Zhang, Y.Q.; Wu, Q.; Xu, J.J. On-chip plasmon-induced transparency in THz metamaterial on a LiNbO
_{3}subwavelength planar waveguide. Opt. Express**2019**, 27, 7373–7383. [Google Scholar] [CrossRef] [PubMed] - Welser, J.; Pitera, J.W.; Goldberg, C. Future Computing Hardware for AI. In Proceedings of the 2018 IEEE International Electron Devices Meeting (IEDM), San Francisco, CA, USA, 1–5 December 2018. [Google Scholar]
- Lu, C.C.; Hu, X.Y.; Yang, H.; Gong, Q.H. Integrated all-optical logic discriminators based on plasmonic bandgap engineering. Sci. Rep.
**2013**, 3. [Google Scholar] [CrossRef] [PubMed] - Llatser, I.; Abadal, S.; Sugranes, A.M.; Cabellos-Aparicio, A.; Alarcon, E. Graphene-enabled Wireless Networks-on-Chip. In Proceedings of the 2013 First International Black Sea Conference on Communications and Networking (BlackSeaCom), Batumi, Georgia, 3–5 July 2013; pp. 69–73. [Google Scholar]
- Lin, Y.M.; Valdes-Garcia, A.; Han, S.J.; Farmer, D.B.; Meric, I.; Sun, Y.N.; Wu, Y.Q.; Dimitrakopoulos, C.; Grill, A.; Avouris, P.; et al. Wafer-Scale Graphene Integrated Circuit. Science
**2011**, 332, 1294–1297. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Ni, G.X.; McLeod, A.S.; Sun, Z.; Wang, L.; Xiong, L.; Post, K.W.; Sunku, S.S.; Jiang, B.Y.; Hone, J.; Dean, C.R.; et al. Fundamental limits to graphene plasmonics. Nature
**2018**, 557, 530–533. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Yablonovitch, E. Photonic crystals—Towards rational material design. Nat. Mater.
**2003**, 2, 648–649. [Google Scholar] [CrossRef] - Stutzer, S.; Plotnik, Y.; Lumer, Y.; Titum, P.; Lindner, N.H.; Segev, M.; Rechtsman, M.C.; Szameit, A. Photonic topological Anderson insulators. Nature
**2018**, 560, 461–465. [Google Scholar] [CrossRef] - Lustig, E.; Weimann, S.; Plotnik, Y.; Lumer, Y.; Bandres, M.A.; Szameit, A.; Segev, M. Photonic topological insulator in synthetic dimensions. Nature
**2019**. [Google Scholar] [CrossRef] - Basov, D.N.; Averitt, R.D.; Hsieh, D. Towards properties on demand in quantum materials. Nat. Mater.
**2017**, 16, 1077–1088. [Google Scholar] [CrossRef] - Zhu, T.; Zhou, Y.; Lou, Y.; Ye, H.; Qiu, M.; Ruan, Z.; Fan, S. Plasmonic computing of spatial differentiation. Nat. Commun.
**2017**, 8, 15391. [Google Scholar] [CrossRef] - Calva, P.A.; Medina, I. Power Breakdown Threshold of a Plasmonic Waveguide Filter. Plasmonics
**2014**, 9, 561–564. [Google Scholar] [CrossRef] - Marpaung, D.; Yao, J.P.; Capmany, J. Integrated microwave photonics. Nat. Photonics
**2019**, 13, 80–90. [Google Scholar] [CrossRef] - Yang, J.Y.; Zhao, Y.; Qiu, C.; Wang, W.J.; Jiang, G.M.; Hao, Y.L.; Jiang, X.Q. Study of Silicon Photonics Based on Standard CMOS Foundry. In Proceedings of the Optoelectronic Devices and Integration III, Beijing, China, 18–20 October 2010; Volume 7847. [Google Scholar] [CrossRef]
- Orcutt, J.S.; Khilo, A.; Holzwarth, C.W.; Popovic, M.A.; Li, H.Q.; Sun, J.; Bonifield, T.; Hollingsworth, R.; Kartner, F.X.; Smith, H.I.; et al. Nanophotonic integration in state-of-the-art CMOS foundries. Opt. Express
**2011**, 19, 2335–2346. [Google Scholar] [CrossRef] [PubMed] - Atabaki, A.H.; Moazeni, S.; Pavanello, F.; Gevorgyan, H.; Notaros, J.; Alloatti, L.; Wade, M.T.; Sun, C.; Kruger, S.A.; Meng, H.Y.; et al. Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip. Nature
**2018**, 556, 349–354. [Google Scholar] [CrossRef] [PubMed] - Popovic, M.A.; Wade, M.T.; Oreutt, J.S.; Shainline, J.M.; Sun, C.; Georgas, M.; Moss, B.; Kumar, E.; Alloatti, L.; Pavanello, F.; et al. Monolithic Silicon Photonics in a Sub-100nm SOI CMOS Microprocessor Foundry: Progress from Devices to Systems. In Proceedings of the Silicon Photonics X, San Francisco, CA, USA, 9–12 February 2015; Volume 9367. [Google Scholar] [CrossRef]
- Liu, Y.S. Pioneering Research in VCSEL-Based Parallel Optical Interconnect Technology for Today’s Data Centers. Nonlinear Opt. Quantum Opt.
**2019**, 50, 217–226. [Google Scholar] - Cheng, Q.X.; Bahadori, M.; Glick, M.; Rumley, S.; Bergman, K. Recent advances in optical technologies for data centers: A review. Optica
**2018**, 5, 1354–1370. [Google Scholar] [CrossRef] - Alexoudi, T.; Terzenidis, N.; Pitris, S.; Moralis-Pegios, M.; Maniotis, P.; Vagionas, C.; Mitsolidou, C.; Mourgias-Alexandris, G.; Kanellos, G.T.; Miliou, A.; et al. Optics in Computing: From Photonic Network-on-Chip to Chip-to-Chip Interconnects and Disintegrated Architectures. J. Lightwave Technol.
**2019**, 37, 363–379. [Google Scholar] [CrossRef] - Moaied, M.; Palomba, S.; Ostrikov, K. Quantum plasmonics: Longitudinal quantum plasmons in copper, gold, and silver. J. Opt.
**2017**, 19. [Google Scholar] [CrossRef] - Bozhevolnyi, S.I.; Khurgin, J.B. The case for quantum plasmonics. Nat. Photonics
**2017**, 11, 398–400. [Google Scholar] [CrossRef] - Nechepurenko, I.A.; Dorofeenko, A.V.; Vinogradov, A.P.; Nikitov, S.A. Passively Q-switched Spaser as a Terahertz Clock Oscillator for Plasmon Computer. J. Commun. Technol. Electron.
**2017**, 62, 1209–1215. [Google Scholar] [CrossRef] - Saiki, T. Switching of localized surface plasmon resonance of gold nanoparticles using phase-change materials and implementation of computing functionality. Appl. Phys. A Mater.
**2017**, 123. [Google Scholar] [CrossRef] - Morsy-Osman, M.; Plant, D.V. A Comparative Study of Technology Options for Next Generation Intra- and Inter-datacenter Interconnects. In Proceedings of the 2018 Optical Fiber Communications Conference and Exposition (OFC), San Diego, CA, USA, 11–15 March 2018. [Google Scholar]
- Thraskias, C.A.; Lallas, E.N.; Neumann, N.; Schares, L.; Offrein, B.J.; Henker, R.; Plettemeier, D.; Ellinger, F.; Leuthold, J.; Tomkos, I. Survey of Photonic and Plasmonic Interconnect Technologies for Intra-Datacenter and High-Performance Computing Communications. IEEE Commun. Surv. Tutor.
**2018**, 20, 2758–2783. [Google Scholar] [CrossRef] [Green Version] - Lereu, A.L.; Farahi, R.H.; Tetard, L.; Enoch, S.; Thundat, T.; Passian, A. Plasmon assisted thermal modulation in nanoparticles. Opt. Express
**2013**, 21, 12145–12158. [Google Scholar] [CrossRef] [PubMed] - Lereu, A.L.; Passian, A.; Farahi, R.H.; van Hulst, N.F.; Ferrell, T.L.; Thundat, T. Thermoplasmonic shift and dispersion in thin metal films. J. Vac. Sci. Technol. A
**2008**, 26, 836–841. [Google Scholar] [CrossRef] - Zipkes, C.; Palzer, S.; Sias, C.; Kohl, M. A trapped single ion inside a Bose-Einstein condensate. Nature
**2010**, 464, 388–391. [Google Scholar] [CrossRef] [PubMed] - Abadillo-Uriel, J.C.; Koiller, B.; Calderon, M.J. Two-dimensional semiconductors pave the way towards dopant-based quantum computing. Beilstein J. Nanotechnol.
**2018**, 9, 2668–2673. [Google Scholar] [CrossRef] [PubMed] - Brandenburg, F.; Nagumo, R.; Saichi, K.; Tahara, K.; Iwasaki, T.; Hatano, M.; Jelezko, F.; Igarashi, R.; Yatsui, T. Improving the electron spin properties of nitrogen-vacancy centres in nanodiamonds by near-field etching. Sci. Rep.
**2018**, 8, 15847. [Google Scholar] [CrossRef] - Dunjko, V.; Briegel, H.J. Machine learning & artificial intelligence in the quantum domain: A review of recent progress. Rep. Prog. Phys.
**2018**, 81, 074001. [Google Scholar] [CrossRef] - Kimble, H.J. The quantum internet. Nature
**2008**, 453, 1023–1030. [Google Scholar] [CrossRef] - Layden, D.; Zhou, S.; Cappellaro, P.; Jiang, L. Ancilla-Free Quantum Error Correction Codes for Quantum Metrology. Phys. Rev. Lett.
**2019**, 122. [Google Scholar] [CrossRef] - Ren, J.G.; Xu, P.; Yong, H.L.; Zhang, L.; Liao, S.K.; Yin, J.; Liu, W.Y.; Cai, W.Q.; Yang, M.; Li, L.; et al. Ground-to-satellite quantum teleportation. Nature
**2017**, 549, 70–73. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Liao, S.K.; Cai, W.Q.; Liu, W.Y.; Zhang, L.; Li, Y.; Ren, J.G.; Yin, J.; Shen, Q.; Cao, Y.; Li, Z.P.; et al. Satellite-to-ground quantum key distribution. Nature
**2017**, 549, 43–47. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Zhuang, Q.T.; Zhang, Z.S.; Shapiro, J.H. Distributed quantum sensing using continuous-variable multipartite entanglement. Phys. Rev. A
**2018**, 97. [Google Scholar] [CrossRef] - Fernandez-Carames, T.M.; Fraga-Lamas, P.; Suarez-Albela, M.; Diaz-Bouza, M.A. A Fog Computing Based Cyber-Physical System for the Automation of Pipe-Related Tasks in the Industry 4.0 Shipyard. Sensors
**2018**, 18, 1961. [Google Scholar] [CrossRef] [PubMed] - Yan, L.; Cao, S.; Gong, Y.; Han, H.; Wei, J.; Zhao, Y.; Yang, S. SatEC: A 5G Satellite Edge Computing Framework Based on Microservice Architecture. Sensors
**2019**, 19, 831. [Google Scholar] [CrossRef] [PubMed] - Cheng, Z.; Rios, C.; Pernice, W.H.P.; Wright, C.D.; Bhaskaran, H. On-chip photonic synapse. Sci. Adv.
**2017**, 3, e1700160. [Google Scholar] [CrossRef] - Olshausen, B.A.; Rozell, C.J. Neuromorphic computation sparse codes from memristor grids. Nat. Nanotechnol.
**2017**, 12, 722–723. [Google Scholar] [CrossRef] - Watson, A. Neuromorphic engineering—Why can’t a computer be more like a brain. Science
**1997**, 277, 1934–1936. [Google Scholar] [CrossRef] - Boybat, I.; Le Gallo, M.; Nandakumar, S.R.; Moraitis, T.; Parnell, T.; Tuma, T.; Rajendran, B.; Leblebici, Y.; Sebastian, A.; Eleftheriou, E. Neuromorphic computing with multi-memristive synapses. Nat. Commun.
**2018**, 9. [Google Scholar] [CrossRef] - Van de Burgt, Y.; Melianas, A.; Keene, S.T.; Malliaras, G.; Salleo, A. Organic electronics for neuromorphic computing. Nat. Electron.
**2018**, 1, 386–397. [Google Scholar] [CrossRef] - Indiveri, G.; Douglas, F. Robotic vision—Neuromorphic vision sensors. Science
**2000**, 288, 1189–1190. [Google Scholar] [CrossRef] - Neftci, E.; Binas, J.; Rutishauser, U.; Chicca, E.; Indiveri, G.; Douglas, R.J. Synthesizing cognition in neuromorphic electronic systems. Proc. Natl. Acad. Sci. USA
**2013**, 110, E3468–E3476. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Prezioso, M.; Merrikh-Bayat, F.; Hoskins, B.D.; Adam, G.C.; Likharev, K.K.; Strukov, D.B. Training andoperation of an integrated neuromorphic network based on metal-oxide memristors. Nature
**2015**, 521, 61–64. [Google Scholar] [CrossRef] [PubMed] - Esser, S.K.; Merolla, P.A.; Arthur, J.V.; Cassidy, A.S.; Appuswamy, R.; Andreopoulos, A.; Berg, D.J.; McKinstry, J.L.; Melano, T.; Barch, D.R.; et al. Convolutional networks for fast, energy-efficient neuromorphic computing. Proc. Natl. Acad. Sci. USA
**2016**, 113, 11441–11446. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Buckley, S.M.; Chiles, J.; McCaughan, A.N.; Mirin, R.P.; Nam, S.W.; Shainline, J.M. Photonic interconnect with superconducting electronics for large-scale neuromorphic computing (Invited Paper). In Proceedings of the 2017 IEEE Photonics Society Summer Topical Meeting Series (Sum), San Juan, PR, USA, 10–12 July 2017; pp. 51–52. [Google Scholar]
- Choi, S.; Tan, S.H.; Li, Z.F.; Kim, Y.; Choi, C.; Chen, P.Y.; Yeon, H.; Yu, S.M.; Kim, J. SiGe epitaxial memory for neuromorphic computing with reproducible high performance based on engineered dislocations. Nat. Mater.
**2018**, 17, 335–340. [Google Scholar] [CrossRef] [PubMed] - Sarkar, D.; Tao, J.; Wang, W.; Lin, Q.F.; Yeung, M.; Ren, C.H.; Kapadia, R. Mimicking Biological Synaptic Functionality with an Indium Phosphide Synaptic Device on Silicon for Scalable Neuromorphic Computing. ACS Nano
**2018**, 12, 1656–1663. [Google Scholar] [CrossRef] [PubMed] - Cheng, R.; Goteti, U.S.; Hamilton, M.C. Superconducting Neuromorphic Computing Using Quantum Phase-Slip Junctions. IEEE Trans. Appl. Supercond.
**2019**, 29. [Google Scholar] [CrossRef] - Sorger, V.J.; Amin, R.; Khurgin, J.B.; Ma, Z.Z.; Dalir, H.; Khan, S. Scaling vectors of attoJoule per bit modulators. J. Opt.
**2018**, 20. [Google Scholar] [CrossRef] - Laporte, F.; Katumba, A.; Dambre, J.; Bienstman, P. Numerical demonstration of neuromorphic computing with photonic crystal cavities. Opt. Express
**2018**, 26, 7955–7964. [Google Scholar] [CrossRef] - Gong, N.; Ide, T.; Kim, S.; Boybat, I.; Sebastian, A.; Narayanan, V.; Ando, T. Signal and noise extraction from analog memory elements for neuromorphic computing. Nat. Commun.
**2018**, 9. [Google Scholar] [CrossRef] - Wang, Y.; Lv, Z.Y.; Chen, J.R.; Wang, Z.P.; Zhou, Y.; Zhou, L.; Chen, X.L.; Han, S.T. Photonic Synapses Based on Inorganic Perovskite Quantum Dots for Neuromorphic Computing. Adv. Mater.
**2018**, 30. [Google Scholar] [CrossRef] [PubMed] - Wang, Z.R.; Joshi, S.; Savel’ev, S.E.; Jiang, H.; Midya, R.; Lin, P.; Hu, M.; Ge, N.; Strachan, J.P.; Li, Z.Y.; et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. Nat. Mater.
**2017**, 16, 101–108. [Google Scholar] [CrossRef] [PubMed] - Jang, B.C.; Kim, S.; Yang, S.Y.; Park, J.; Cha, J.H.; Oh, J.; Choi, J.; Im, S.G.; Dravid, V.P.; Choi, S.Y. Polymer Analog Memristive Synapse with Atomic-Scale Conductive Filament for Flexible Neuromorphic Computing System. Nano Lett.
**2019**, 19, 839–849. [Google Scholar] [CrossRef] [PubMed] - Waser, R.; Dittmann, R.; Menzel, S.; Noll, T. Introduction to new memory paradigms: Memristive phenomena and neuromorphic applications. Faraday Discuss.
**2019**, 213, 11–27. [Google Scholar] [CrossRef] [PubMed] - Prando, G. Neuromorphic computation Lowering dimensions. Nat. Nanotechnol.
**2017**, 12, 449. [Google Scholar] - Torrejon, J.; Riou, M.; Araujo, F.A.; Tsunegi, S.; Khalsa, G.; Querlioz, D.; Bortolotti, P.; Cros, V.; Yakushiji, K.; Fukushima, A.; et al. Neuromorphic computing with nanoscale spintronic oscillators. Nature
**2017**, 547, 428–431. [Google Scholar] [CrossRef] [PubMed] - Van de Burgt, Y.; Lubberman, E.; Fuller, E.J.; Keene, S.T.; Faria, G.C.; Agarwal, S.; Marinella, M.J.; Talin, A.A.; Salleo, A. A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing. Nat. Mater.
**2017**, 16, 414–418. [Google Scholar] [CrossRef] - Bartolozzi, C. Neuromorphic circuits impart a sense of touch. Science
**2018**, 360, 966–967. [Google Scholar] [CrossRef] - Zhu, X.J.; Li, D.; Liang, X.G.; Lu, W.D. Ionic modulation and ionic coupling effects in MoS
_{2}devices for neuromorphic computing. Nat. Mater.**2019**, 18, 141–148. [Google Scholar] [CrossRef] - Kumar, M.; Abbas, S.; Kim, J. All-Oxide-Based Highly Transparent Photonic Synapse for Neuromorphic Computing. ACS Appl. Mater. Interfaces
**2018**, 10, 34370–34376. [Google Scholar] [CrossRef] - Russek, S.E.; Donnelly, C.A.; Schneider, M.L.; Baek, B.; Pufall, M.R.; Rippard, W.H.; Hopkins, P.F.; Dresselhaus, P.D.; Benz, S.P. Stochastic Single Flux Quantum Neuromorphic Computing using Magnetically Tunable Josephson Junctions. In Proceedings of the 2016 IEEE International Conference on Rebooting Computing (ICRC), San Diego, CA, USA, 17–19 October 2016. [Google Scholar]
- Esqueda, I.S.; Yan, X.D.; Rutherglen, C.; Kane, A.; Cain, T.; Marsh, P.; Liu, Q.Z.; Galatsis, K.; Wang, H.; Zhou, C.W. Aligned Carbon Nanotube Synaptic Transistors for Large-Scale Neuromorphic Computing. ACS Nano
**2018**, 12, 7352–7361. [Google Scholar] [CrossRef] [PubMed] - Wu, H.; Yao, P.; Gao, B.; Qian, H. Multiplication on the edge. Nat. Electron.
**2018**, 1, 8–9. [Google Scholar] [CrossRef] [Green Version] - Li, C.; Hu, M.; Li, Y.; Jiang, H.; Ge, N.; Montgomery, E.; Zhang, J.; Song, W.; Dávila, N.; Graves, C.E.; et al. Analogue signal and image processing with large memristor crossbars. Nat. Electron.
**2018**, 1, 52–59. [Google Scholar] [CrossRef] - Green, S.; Aimone, J.B. Memristors learn to play. Nat. Electron.
**2019**, 2, 96–97. [Google Scholar] [CrossRef] - Merolla, P.A.; Arthur, J.V.; Alvarez-Icaza, R.; Cassidy, A.S.; Sawada, J.; Akopyan, F.; Jackson, B.L.; Imam, N.; Guo, C.; Nakamura, Y.; et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science
**2014**, 345, 668–673. [Google Scholar] [CrossRef] - Lin, C.K.; Wild, A.; Chinya, G.N.; Lin, T.H.; Davies, M.; Wang, H. Mapping Spiking Neural Networks onto a Manycore Neuromorphic Architecture. ACM Sigplan Not.
**2018**, 53, 78–89. [Google Scholar] [CrossRef] - Davies, M.; Srinivasa, N.; Lin, T.H.; Chinya, G.; Cao, Y.Q.; Choday, S.H.; Dimou, G.; Joshi, P.; Imam, N.; Jain, S.; et al. Loihi: A Neuromorphic Manycore Processor with On-Chip Learning. IEEE Micro
**2018**, 38, 82–99. [Google Scholar] [CrossRef]

**Figure 1.**Article statistics showing the histogram of edge computing over publication years, a compilation from [54]. Inset: distribution by discipline [54]. Notable are the intensified research, the multidisciplinary character of the field, and the largest contributing disciplines. The number of other contributing disciplines (not shown) is also increasing rapidly.

**Figure 2.**Some basic elements of a device in the edge computing paradigm. The edge device does not necessarily require a connection with a centralized cloud. Many challenges lie ahead regarding energy efficiency, data quality and reliability, data and device security, computing performance level, etc., stimulating exploration for novel nanosystems and processor architectures, rapid communication, and related components.

**Figure 3.**Multi-tier computing networks. The integration of various technologies to achieve intelligent applications and services.

**Figure 4.**Example of a current paradigm based on cloud computing. Sensors generate raw signals, which are submitted to the cloud directly (route II) or are acquired and observed and then either communicated to the cloud or are evaluated and submitted to the cloud.

**Figure 5.**A simplified edge computing approach. A sensor generates raw data, which is locally processed and evaluated. If the outcome meets certain criteria, the data is then communicated to the cloud for further processing/computing and storage.

**Figure 6.**Envisioning a potential variation of the interconnectivity of edge sensors. The nested growth of edge devices may form a system of a coupled dynamical system with fractal self-similarity. The sensor output S can be processed to H and communicated as N with a final output of f for node i located at R

_{i}relative to data center O

_{s}.

**Figure 7.**Comparison of the number of RISC chips (ARM, ARC, Tensilica, or MIPS ISAs) versus CISC architecture (Intel’s 80 × 86). RISC, reduced instruction set computer; CISC, complex instruction set computer; ISA, instruction set architecture; ARM, advanced RISC machine.

**Figure 8.**The process loop for the development of new processors. Examples of computational approaches including DFT (density functional theory), finite elements (FE), finite difference time domain (FDTD), and direct simulation Monte Carlo (DSMC) are given only generically.

**Table 1.**Novel nanomaterials and nanostructures of importance in the research and development of the next-generation computing systems.

Nanosystem | Typical Excitation | Application | References |
---|---|---|---|

Carbon nanotubes | Electron-hole transport | Transistor channel, cooling, vias, connectors | [216,217,218,219,220,221] |

Nanowires and nanoantenna | Plasmons | Interconnect, connector, qubit | [146,207,222,223,224,225,226,227] |

Quantum dots (doped, undoped Si, GaAs, etc.) | Excitons | Qubit | [226,228,229,230,231,232,233,234,235] |

Silicon photonics components | Donor, electron, hole charge and spin states | Transistor material, qubits, quantum computing | [176,210,211] |

Nanophotonics components | Photons, polaritons, plasmons | Transistor material, qubits, quantum computing | [204,216,231,233] |

Organic compounds | Charge | Transistor material | [147] |

Nitrogen vacancy | Spin states | Qubits, quantum computing | [205,236,237] |

Trapped ions | Qubits, quantum computing | [131,238] | |

Nano- and micro-electromechanical systems (NEMS and MEMS) | Phonons | Readout | [193] |

Superconductors | Electron | Qubits, quantum computing | [222,226] |

Metamaterials and metasurfaces | Photons, phonons, plasmons | Transistor material, frequency conversion | [94,239,240,241] |

Topological materials | Photons, phonons, plasmons, electrons | Interconnect, connector, qubit, transistor material | [170,171,172,173,174,176,222,242] |

Metal-oxide-semiconductor and -metal tunnel junctions | Electron, plasmon, photon | Transistor material | [232,234] |

Multilayers | Bloch surface waves | Interconnect | [94,135] |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Passian, A.; Imam, N.
Nanosystems, Edge Computing, and the Next Generation Computing Systems. *Sensors* **2019**, *19*, 4048.
https://doi.org/10.3390/s19184048

**AMA Style**

Passian A, Imam N.
Nanosystems, Edge Computing, and the Next Generation Computing Systems. *Sensors*. 2019; 19(18):4048.
https://doi.org/10.3390/s19184048

**Chicago/Turabian Style**

Passian, Ali, and Neena Imam.
2019. "Nanosystems, Edge Computing, and the Next Generation Computing Systems" *Sensors* 19, no. 18: 4048.
https://doi.org/10.3390/s19184048