Search Results (455)

Resistive random-access memory (RRAM) has emerged as one of the most promising device platforms for neuromorphic, in-memory computing because its two-terminal metal–insulator–metal (MIM) structure can reproduce the weight-update behavior of biological synapses while remaining compatible with mainstream CMOS processing. This review summarizes the current state of RRAM-based neuromorphic technology from four complementary perspectives: device physics, materials, fabrication processes, and packaging. We first describe the operating principles of filamentary and interface-type RRAM, including the forming/set/reset switching sequence, and the two dominant analytical frameworks used to describe the reset transition—the ion-migration model and the thermally driven filament-dissolution model. We then review the switching-layer and electrode materials that have been most widely investigated such as HfO_x, TiO_x, TaO_x, ZnO, ZrO₂, and Cu/Ag-based conductive-bridge systems, together with representative bilayer and doped architectures reported for synaptic devices. The biological functions that RRAM can emulate are discussed alongside the non-ideal characteristics that currently limit on-chip training accuracy, with emphasis on separating device-to-device from cycle-to-cycle variability and on the workload-dependent nature of endurance and retention requirements. We further summarize the process technologies used to integrate RRAM into large-scale, CMOS-compatible arrays, including atomic layer deposition, interfacial oxygen-reservoir engineering, low-thermal-budget back-end-of-line integration, and three-dimensional vertical RRAM patterning, and discuss the advanced packaging strategies such as 2.5D/3D heterogeneous integration, chiplet architectures, thermal-interface materials, and nanostructured underfills required to manage the power density and interconnect demands of large synaptic arrays, distinguishing solutions that have been demonstrated specifically for RRAM neuromorphic arrays from those that remain general advanced-packaging concepts. Finally, RRAM is benchmarked against competing emerging non-volatile memories, and the key research directions such as three-terminal memtransistor architectures, three-dimensional integration with high-performance selectors, and hardware–algorithm co-design that will determine whether RRAM-based neuromorphic hardware can move from laboratory demonstrations to on-device AI, autonomous systems, and large-scale artificial-neural-network accelerators are outlined. Relative to prior reviews that focus primarily on RRAM device physics or on switching-layer materials in isolation, the distinctive contribution of this review is an explicit, cross-layer synthesis that connects device-level non-idealities to their consequences for wafer-scale process integration and for advanced 2.5D/3D packaging—a combination that, to our knowledge, has not been jointly treated in the recent review literature on RRAM-based neuromorphic hardware. Full article

A complementary metal oxide semiconductor (CMOS)-compatible z-axis magnetic field sensor incorporating an additional collector is proposed to enhance magnetic sensing performance. The sensor consists of four identical magnetic sensing elements arranged in a cross-shaped configuration, while shallow trench isolation (STI) and a post-CMOS cavity structure are employed to suppress substrate leakage current and improve electrical isolation. The sensing characteristics were investigated using three-dimensional TCAD simulations to analyze carrier transport and current density distributions under different magnetic fields. The simulation results confirmed that the structure effectively enhances the differential output response and magnetic sensitivity. The device was fabricated using a commercial CMOS process followed by a simple post-CMOS micromachining process. Optical microscope and scanning electron microscope observations verified the successful formation of the sensing structure and the cavity beneath the sensing elements. The sensor was experimentally characterized under magnetic fields ranging from −300 to 300 mT. The measured results exhibited excellent linearity over the entire measurement range. The sensor achieved a measured sensitivity of 120 mV/T. Owing to its high sensitivity, simple fabrication process, and full compatibility with standard CMOS technology, the magnetic field sensor is promising for integrated microsystems, industrial monitoring, and intelligent sensing applications. Full article

Micromachined ultrasonic transducers (MUTs) represent a notable advance in miniaturized sensing, enabling compact, low-power, and complementary metal-oxide-semiconductor (CMOS)-integrated platforms that extend ultrasonic capabilities into wearable, implantable, and edge-computing domains. The integration of artificial intelligence (AI) has introduced new approaches for signal interpretation, adaptive control, and data-driven optimization, enhancing performance in specific areas such as compressed sensing, neural beamforming, and learned image enhancement that complement conventional signal processing. Meanwhile, sensor fusion strategies that combine ultrasonic data with complementary modalities have improved robustness, contextual awareness, and diagnostic accuracy across applications ranging from industrial monitoring to clinical diagnostics. This review provides a comprehensive analysis of this active research area, systematically covering transducer hardware platforms, design methodologies, and intelligent signal processing frameworks. While traditional bulk piezoelectric transducers remain the benchmark for high-power applications, capacitive and piezoelectric micromachined variants offer superior acoustic impedance matching and monolithic CMOS compatibility essential for portable systems. We examine the evolution from deterministic analytical and numerical modeling toward AI-powered inverse design, which enables the discovery of non-intuitive, high-performance geometries beyond human intuition. Furthermore, the integration of machine learning (ML) for signal recovery, image enhancement, and multi-modal sensor fusion is discussed as a pathway to compensate for hardware constraints such as limited aperture, sparse sampling, and low signal-to-noise ratio (SNR), while pointing out that AI technology cannot overcome fundamental physical limits including acoustic attenuation, thermal noise floors, and transduction efficiency boundaries. By synthesizing recent advancements, this review demonstrates how the convergence of classical acoustic physics and data-driven intelligence is guiding the development of of intelligent ultrasonic systems. Full article

Memristive devices have attracted considerable attention as promising candidates for overcoming the energy and data-transfer limitations of conventional computing architectures. In particular, their integration with biosensors offers a pathway toward compact and energy-efficient diagnostic systems. This review examines the development of memristive-device-based biosensors from device-level transduction to system-level integration. At the device level, sensing strategies have evolved from direct sensing toward indirect sensing architectures, improving stability and reusability. At the system level, conventional off-chip implementations have progressively shifted toward fully integrated on-chip implementations. Furthermore, this review highlights the emerging paradigm of in-sensor computing, in which sensing, memory, and computation are co-located within a single physical platform. This approach enables reduced data movement and supports energy-efficient operation for point-of-care applications. Finally, key challenges—including CMOS compatibility, device variability, and reliable multi-threshold sensing operation—are discussed as critical factors for the practical realization of memristive-device-based electrochemical biosensing systems. Full article

To address the critical thermal challenges in high-performance computing and three-dimensional integrated circuits, the doping-tunable control of near-field thermal radiation using CMOS-compatible materials offers a highly promising non-contact cooling strategy. In this work, radiative heat transfer between two parallel heavily phosphorus-doped silicon plates separated by a vacuum gap is studied using fluctuational electrodynamics. A doping-dependent Drude model is employed to describe the dielectric response of doped silicon, including carrier concentration, ionization, and mobility effects. The influences of gap width and doping concentration on the total and spectral heat transfer are systematically analyzed. The results show that the heat transfer increases sharply as the gap decreases and is mainly governed by TM-polarized evanescent modes. Under symmetric doping, the spectral peak shifts to higher frequencies as the doping concentration increases from ${10}^{18}$ to ${10}^{21} {\mathrm{cm}}^{-3}$, while the strongest transfer occurs at ${10}^{19} {\mathrm{cm}}^{-3}$ because of favorable surface-plasmon-polariton coupling and impedance matching. These findings provide a theoretical foundation for chip-scale thermal management and on-chip radiative cooling in CMOS-compatible silicon platforms, although practical implementation would require dynamic tuning mechanisms and device-level engineering in future work. Full article

The growing demand for wearable and Internet of Medical Things (IoMT) devices is driving the development of compact, low-power platforms for continuous physiological monitoring. Bioimpedance analysis represents a versatile non-invasive technique for the assessment of tissue properties, body composition, and respiratory dynamics. This work presents a comprehensive experimental validation of a compact bioimpedance measurement platform based on the SENSIPLUS chip, a CMOS sensor interface integrating a frequency-programmable lock-in amplifier for Electrochemical Impedance Spectroscopy in the 10 kHz–1 MHz range. The platform was validated at three complementary levels: (i) electrical characterization on Debye tissue-equivalent circuits using a three-point bilinear calibration, with analysis of the electrode–skin contribution and repeatability assessment; (ii) in vivo multi-frequency bioimpedance spectroscopy (BIS) with Cole–Cole model fitting and hook-effect correction; and (iii) single-frequency thoracic impedance plethysmography for respiratory monitoring. Results were compared against an Agilent E4980A precision Inductance (L), Capacitance (C), and Resistance (R) meter and a calibrated spirometer. The presented device achieved a maximum resistance error below 5.7% and reactance deviation under 6 $\mathsf{\Omega }$ across the investigated frequency range, Cole–Cole parameters consistent with reference values, and strong linear correlation ($R^2=0.97$) between thoracic impedance variations and tidal volume, with respiratory rate estimation errors below 2% across the ten sessions, specifically 1.43% during normal breathing and 1.96% during deep breathing. These results demonstrate that the SENSIPLUS-based platform achieves metrological performance compatible with the requirements of wearable IoMT applications, here demonstrated in a single-subject proof-of-concept study, while relying for all critical analog functions on a compact ($1.5\times 1.5$) mm² system-on-chip with low power consumption (1.5 mW). Full article

Data movement has become a dominant bottleneck in modern artificial intelligence hardware, making in-memory computing a critical direction for energy-efficient and memory-centric architectures. Ferroelectric hafnium oxide provides a distinctive materials platform for this transition because field-driven polarization switching, non-volatility, CMOS compatibility, and nanoscale thickness scalability can be combined within a process-relevant oxide system. This review establishes a device-to-system perspective on HfO₂-based and Hf_0.5Zr_0.5O₂-based ferroelectric memories for in-memory computing. Instead of treating ferroelectric materials, memory devices, circuit primitives, and computing architectures as separate research topics, we examine how their mutual constraints define the achievable efficiency, precision, reliability, and scalability of hafnia-based computing systems. The discussion connects polarization engineering and defect control with charge-domain computation, threshold-state logic, associative search, analog weight representation, neuromorphic plasticity, and sensor-side processing. Particular emphasis is placed on the translation of ferroelectric functionality from individual devices to arrays, macros, and system-level accelerators. We identify variability, fatigue, charge trapping, multilevel-state uncertainty, peripheral overhead, and benchmarking inconsistency as the central barriers that prevent device-level advantages from directly becoming system-level gains. Finally, we outline a cross-layer roadmap in which ferroelectric stack engineering, variability-tolerant arrays, precision-scalable architectures, and SoC-level integration are co-optimized to enable reliable HZO-based memory-centric computing. Full article

This article reviews the reactive magnetron sputtering of piezoelectric Aluminium Nitride (AlN) thin films, with a focus on process parameter optimisation and system-level deposition challenges. AlN is a leading material for MEMS and RF applications owing to its c-axis (002) orientation, high acoustic velocity, wide bandgap (∼6.2 eV), and CMOS compatibility. We review the influence of sputtering power, nitrogen flow ratio, substrate temperature, and target-to-substrate distance on crystallographic quality and document practical hardware challenges, including vacuum leakage, grounding faults, target erosion, and mass flow controller drift, that critically affect reproducibility but are systematically underreported in the literature. A perspective is provided on emerging application domains where optimised AlN films address current performance gaps, including next-generation RF/telecom systems towards 6G and Future Networks, harsh environment sensing and actuation, biomedical ultrasound, and IoT energy harvesting. The complementarity between AlN and Silicon Carbide (SiC) is discussed for high-temperature, high-power, and radiation-hard MEMS, where AlN/SiC heterostructures combine the piezoelectric activity of AlN with the mechanical and chemical robustness of SiC. It also incorporates a discussion of dopant- and heteroepitaxy-based AlN engineering, AlN deposition on a wider range of substrates, the role of seed and electrode underlayers, and pulsed-DC sputtering as a third power supply mode alongside RF and conventional DC. Full article

HfO₂ based thin-film ferroelectrics are metastable at room temperature and transited to the dielectric monoclinic phase upon heating. The thermal stability of such ferroelectrics increases when thin-film oxides are buried (BOX) in silicon–ferroelectric–silicon (SFS) structures formed by SmartCut^®, where thin ferroelectric layers are stabilized by oxygen vacancies and tensile stresses in the BOX, which is similar to silicon-on-insulator (SOI) structures. The main characteristics of the ferroelectrics in MFS and SFS structures are residual polarization P_r and coercive field E_c, which are determined by the fraction of the metastable ferroelectric phases that are also stabilized due to the inserted Al impurity in HfO₂:Al₂O₃ 10:1 (HAO) and (HfO₂:ZrO₂):Al₂O₃ (1:1)₅:1 (HZAO) nanolaminates. SFS structures and SFS CMOS ICs were tested after all thermal treatments at temperatures 900–1000 °C with t_BOX = 10–20 nm (or equivalent oxide thickness EOT = 1–2 nm) in an industrial process as gate insulators for CMOS and dual-gate DG SFS transistors. Their characteristics simulated in TCAD Sentaurus and analytic models in LTspice are investigated for an analog content addressable memory (ACAM). Full article

We report a CMOS-compatible ferroelectric memristor based on a TiN/AlScN/n⁺ Si metal ferroelectric semiconductor (MFS) structure, fabricated entirely via low-temperature sputtering processes. The ultrathin AlScN film exhibits robust ferroelectricity with a high remanent polarization (2P_r ≈ 80.91 μC/cm²) and excellent endurance over 10⁵ cycles, while maintaining uniform switching across cells. Notably, the use of a heavily doped silicon bottom electrode enables full compatibility with conventional back-end-of-line (BEOL) CMOS processes and facilitates integration with silicon-based circuits. Beyond stable memory performance, the device demonstrates volatile short-term memory (STM) behavior originating from depolarization field-induced polarization relaxation, which is essential for neuromorphic dynamics. Leveraging this STM feature, the device was implemented as a physical reservoir in a reservoir computing (RC) framework, achieving 97.64% classification accuracy on the MNIST dataset using temporally coded inputs. These results highlight the potential of AlScN-based ferroelectric memristors as dynamic CMOS-compatible building blocks for in-memory and neuromorphic computing. Full article

Terahertz (THz) radiation generated through ultrafast transient photocurrent mechanisms has become a cornerstone of modern THz photonics, enabling broadband coherent emission with sub-picosecond temporal resolution. This review provides a comprehensive and mechanism-driven analysis of THz pulse generation via photo-Dember diffusion currents, surface depletion field acceleration, and biased photoconductive antenna architectures. We present a comprehensive comparative analysis of wide- and low-bandgap material platforms, including III–V, II–VI, and group IV semiconductors, as well as two-dimensional materials, topological insulators, and Weyl semimetals, highlighting how their intrinsic properties, such as band structure, carrier mobility, recombination dynamics, doping, and dielectric response, govern their THz emission efficiency, bandwidth, and spectral tunability. Special emphasis is placed on germanium (Ge), which has re-emerged as a highly promising THz source material owing to its high carrier mobility, long diffusion lengths, strain-tunable band structure, and CMOS compatibility. We highlight the roles of doping, strain-induced direct transitions, and several fabrication techniques in controlling the nonlinear photoexcited charge-carrier dynamics in Ge, thereby unlocking enhanced broadband THz performance. Finally, we explore the emerging application prospects of THz radiation, ranging from non-invasive security screening to biochemical sensing and archeological preservation. By bridging fundamental material science with scalable device architectures, this review outlines current challenges, highlights evolving opportunities in novel materials, and charts future directions towards integrated THz technologies. Full article

Advances in microelectronics have driven the miniaturization of endoscopic systems, leading to increasingly compact devices with enhanced optical and electronic performance. However, the current state-of-the-art commercial endoscopes—typically ranging from several millimeters to over 1 cm in diameter—still face significant limitations when accessing delicate anatomical structures such as the Fallopian tubes, urethra, and other narrow cavities. In this paper, we introduce an ultracompact endoscope based on the use of a CMOS image sensor from ams OSRAM (NanEyeM) and a single micro-LED (LTW-FC03DCD5). We achieved a total diameter of 1.8 mm, compatible with ultra-narrow cavities of the human body. Optically, a minimal correlated color temperature of 10,343 K and a maximal color rendering index of 73.6 were achieved. These values allow for the detection of structures of small dimensions by morphological and vascular contrast. The optical system demonstrated the ability to resolve up to 83 lp/mm, with a diagonal field-of-view of 115.0° ± 2.4°, measured at different working distances, suitable for working distances encountered in narrow anatomical cavities. This work represents a first prototype for direct visual imaging in human organs not previously accessible by endoscopy, assisting healthcare professionals in clinical assessment and supporting the diagnosis of diseases. Full article

Single-crystal scintillators are used to convert ionizing radiation into optical photons in various medical imaging applications. A promising material is cerium (Ce)-doped gadolinium aluminum gallium garnet (GAGG:Ce) inorganic scintillator. Three GAGG:Ce 10 × 10 × 10 mm³ crystals of different light yield (LY) were exposed in X-ray tube voltage range of 50–140 kVp. Their absolute luminescence efficiency (AE) was experimentally calculated. A theoretical model was employed to simulate the propagation of photons traversing through the crystal mass. The model was utilized to estimate the detector quantum gain (DQG) and the percentage of transmission of the optical photons per elementary thickness $\mathrm{k}$. Their suitability with various optical photodetectors was evaluated by means of the spectral matching factor (SMF). GAGG:Ce presented AE values reaching 60.72 E.U. (where 1 E.U. = 1 μWm⁻²/(mRs⁻¹)) at 140 kVp. The parameter $\mathrm{k}$ ranged from 0.99973 to 0.99980. GAGG:Ce emission spectrum is highly compatible with charged-coupled devices (CCD), complementary metal-oxide semiconductors (CMOS) and silicon photomultipliers (SiPM). These findings may further consolidate the use of GAGG:Ce and could contribute to the future optimization of this inorganic scintillator when applied in X-ray imaging modalities, or as a radiation detector. Full article

Short-wavelength infrared (SWIR) image sensors are of increasing interest for space applications, where ionizing radiation can significantly impact device performance. PbS colloidal quantum dot (CQD)-based thin-film photodiodes (TFPDs) are promising candidates due to their spectral tunability and compatibility with CMOS integration. However, their radiation response remains insufficiently understood. We investigated the effects of X-ray irradiation on PbS CQD-based SWIR TFPDs and image sensors up to a total ionizing dose of 220 krad. The results suggest that X-ray irradiation induces ligand-dependent modulation of the trap-state in PbS CQD films, leading to reduced recombination and enhanced carrier lifetime. Consequently, the TFPDs exhibit decreased dark current and improved external quantum efficiency (EQE), reaching 44.2% at 1420 nm. PbS CQD-based SWIR image sensors maintain stable operation after irradiation until 220 krad, achieving an EQE of 33.1%. These results provide an initial assessment of PbS CQD-based SWIR image sensors under X-ray total ionizing dose (TID) exposure, highlighting the importance of ligand-dependent CQD surface chemistry towards SWIR photodetectors in space applications. Full article

Microscopic magnetic stimulation (MSTI) induces electric fields without direct charge injection and can shape localized field gradients with asymmetric micro-coils. Most demonstrations still rely on external drivers, off-chip hardware, or separated coil validation, so the CMOS integration boundary remains poorly characterized. This work presents a fabricated $2\times 1 {\mathrm{mm}}^2$ $0.18 \mathsf{\mu }\mathrm{m}$ CMOS magnetic-stimulation SoC that co-integrates ASK-compatible command decoding, FSM and register-based parameter control, a programmable current–voltage–current triangular driver, and a bent top-metal micro-coil, and it characterizes the on-chip driver-to-coil path together with a substrate-aware field model. Sensing-load reconstruction confirms command-to-waveform programmability, including duration-window decoding, burst-count control, and polarity reversal, with measured slew targets that give a peak current of $I_{\mathrm{pk}}=3.72$–$21.6 \mathrm{mA}$. A quantitative comparison contrasts the current-mode triangular driver with conventional electrode stimulators, a coil-impedance measurement shows the coil stays resistive across 1 to 10 MHz, and the measured total SoC power is about 41 mW. Substrate-aware simulation at a $15 \mathsf{\mu }\mathrm{m}$ target plane shows that the grounded p-substrate retains $35.1$–$40.5\%$ of the no-substrate peak x-directed field-gradient metric. The prototype establishes this electrical programmability and the substrate-aware gradient-transfer loss as a compact design-margin metric for CMOS-integrated magnetic stimulation. Direct biological activation is not claimed and is left to future in vitro validation. Full article