Search Results (69)

The steady-state quantum dynamics of a compound sample consisting of a semiconductor double-quantum-dot (DQD) system, non-linearly coupled with a leaking superconducting transmission line resonator, is theoretically investigated. Particularly, the transition frequency of the DQD is taken to be equal to the doubled resonator frequency, whereas the inter-dot Coulomb interaction is considered weak. As a consequence, the steady-state quantum dynamics of this complex non-linear system exhibit sudden changes in its features, occurring at a critical DQD-cavity coupling strength, suggesting perspectives for designing on-chip microwave quantum switches. Furthermore, we show that, above the threshold, the electrical current through the double-quantum dot follows the mean photon number into the microwave mode inside the resonator. This might not be the case any more below that critical coupling strength. Lastly, the photon quantum correlations vary from super-Poissonian to Poissonian photon statistics, i.e., towards single-qubit lasing phenomena at microwave frequencies. Full article

On-chip optical accelerometers can be a promising alternative to capacitive, piezo-resistive, and piezo-electric accelerometers in some applications due to their immunity to electromagnetic interference and high sensitivity, which allow for robust operation in electromagnetically noisy environments. This paper focuses on the characterization of an easy-to-fabricate tri-axial fiber-free optical MEMS accelerometer, which employs a simple assembly consisting of a light emitting diode (LED), a quadrant photodetector (QPD), and a suspended proof mass, measuring acceleration through light power modulation. This configuration enables simple readout circuitry without the need for complex digital signal processing (DSP). Performance modeling was conducted to simulate the LED’s irradiance profile and its interaction with the proof mass and QPD. Additionally, experimental tests were performed to measure the device’s mechanical sensitivity and validate the mechanical model. Lateral mechanical sensitivity is obtained with acceptable discrepancy from that obtained from FEA simulations. This work consolidates the performance of the design adapted and demonstrates the accelerometer’s feasibility for practical applications. Full article

Microfluidics-based mixing methods have attracted increasing attention due to their great potential in bio-related and material science fields. The combination of acoustics and microfluidics, called acoustofluidics, has been shown to be a promising tool for precise manipulation of microfluids and micro-objects. In general, achieving robust mixing performance in an efficient and simple manner is crucial for microfluidics-based on-chip devices. When surface acoustic waves (SAWs) are introduced into microfluidic devices, the acoustic field can drive highly controllable acoustic streaming flows through acoustofluidic interactions with micro-solid structures, which have the advantages of label-free operation, flexible control, contactless force, fast-response kinetics, and good biocompatibility. Therefore, the design and application of various SAW micromixers have been demonstrated. Herein, we present a comprehensive overview of the latest research and development of SAW-based micromixers. Specifically, we discuss the design principles and underlying physics of SAW-based acoustic micromixing, summarize the distinct types of existing SAW micromixers, and highlight established applications of SAW micromixing technology in chemical synthesis, nanoparticle fabrication, cell culture, biochemical analysis, and cell lysis. Finally, we present current challenges and some perspectives to motivate further research in this area. The purpose of this work is to provide an in-depth understanding of SAW micromixers and inspire readers who are interested in making some innovations in this research field. Full article

Integrated photonic biosensors are revolutionizing lab-on-a-chip technologies by providing highly sensitive, miniaturized, and label-free detection solutions for a wide range of biological and chemical targets. This review explores the foundational principles behind their operation, including the use of resonant photonic structures such as microring and whispering gallery mode resonators, as well as interferometric and photonic crystal-based designs. Special focus is given to the design strategies that optimize light–matter interaction, enhance sensitivity, and enable multiplexed detection. We detail state-of-the-art fabrication approaches compatible with complementary metal-oxide-semiconductor processes, including the use of silicon, silicon nitride, and hybrid material platforms, which facilitate scalable production and seamless integration with microfluidic systems. Recent advancements are highlighted, including the implementation of optofluidic photonic crystal cavities, cascaded microring arrays with subwavelength gratings, and on-chip detector arrays capable of parallel biosensing. These innovations have achieved exceptional performance, with detection limits reaching the parts-per-billion level and real-time operation across various applications such as clinical diagnostics, environmental surveillance, and food quality assessment. Although challenges persist in handling complex biological samples and achieving consistent large-scale fabrication, the emergence of novel materials, advanced nanofabrication methods, and artificial intelligence-driven data analysis is accelerating the development of next-generation photonic biosensing platforms. These technologies are poised to deliver powerful, accessible, and cost-effective diagnostic tools for practical deployment across diverse settings. Full article

With the increasing demand for efficient deep learning models in resource-constrained environments, Binary Neural Networks (BNNs) have emerged as a promising solution due to their ability to significantly reduce computational complexity while maintaining accuracy. Their integration into embedded and edge computing systems is essential for enabling real-time AI applications in areas such as autonomous systems, industrial automation, and intelligent security. Deploying BNN on FPGA using RISC-V, rather than directly deploying the model on FPGA, sacrifices detection speed but, in general, reduces power consumption and on-chip resource usage. The AI-extended RISC-V core is capable of handling tasks beyond BNN inference, providing greater flexibility. This work utilises the lightweight Zero-Riscy core to deploy a BNN on FPGA. Three custom instructions are proposed for convolution, pooling, and fully connected layers, integrating XNOR, POPCOUNT, and threshold operations. This reduces the number of instructions required per task, thereby decreasing the frequency of interactions between Zero-Riscy and the instruction memory. The proposed solution is evaluated on two case studies: MNIST dataset classification and an intrusion detection system (IDS) for in-vehicle networks. The results show that for MNIST inference, the hardware resources required are only 9% of those used by state-of-the-art solutions, though with a slight reduction in speed. For IDS-based inference, power consumption is reduced to just 13% of the original, while resource usage is only 20% of the original. Although some speed is sacrificed, the system still meets real-time monitoring requirements. Full article

Bound states in the continuum (BICs), characterized by high-Q modes, have demonstrated exceptional capabilities for enhancing light-matter interactions and, when combined with gain media, can enable compact lasers with low threshold power. However, conventional BIC lasers typically rely on the emitting light forming a BIC mode, leading to vertical emission, and often lack mechanisms to enhance pump efficiency. In this work, we propose a photonic crystal laser design that incorporates high-Q modes at both pump and emitting wavelengths. The pump light at 980 nm is designed to form a BIC state near the Γ-point, while the emitting light at 1550 nm is confined within a bandgap-defined cavity mode at the M-point, allowing efficient in-plane emission. This design leads to a compact footprint of 19.7 × 17.1 μm² and predicts a significant reduction in threshold power compared with a laser with a single resonance at the emission wavelength, providing a promising approach for developing compact on-chip lasers with significantly improved efficiency. Full article

The resource-constrained MCU-based platform is unable to use high-performance accelerators such as GPUs or servers due to insufficient resources for ML applications. We define a Micro-Accelerator (MA) that can accelerate ML operations by being connected to the on-chip bus peripheral of the MCU core. ML applications using general-purpose accelerators have a well-equipped SDK environment, making design and verification flow straightforward. In contrast, MA must be connected to the MCU core and on-chip bus interface within the chip. However, evaluating the interaction between the MCU core and an MA is challenging, as it requires the MA to connect with the core and the on-chip bus interface during target software execution. The cost of fabricating physical MA hardware is enormous, compounded by licensing issues with commercial cores. We propose a MA-in-the-loop (MAIL) framework that integrates a custom-designed MA into an emulation platform. This platform enables virtual execution by loading software onto the MCU, allowing observation of hardware-software interactions during ML execution. The proposed framework in this paper is a mixture of software that can emulate the environment in which general ML applications run on the MCU and RTL simulations to profile the acceleration on the MA. To evaluate the flow of ML software execution and performance changes according to the various architectures of MA in the framework, the MA can be reconfigured at runtime to explore the design space. To benchmark our proposed framework, we compared TinyML application profiles to the pure software execution. Experimental results show that the MA-accelerated framework performs comparably to actual MCUs, validating the efficacy of the proposed approach. Full article

The integration of a photodetector that converts optical signals into electrical signals is essential for scalable integrated lithium niobate photonics. Two-dimensional materials provide a potential high-efficiency on-chip detection capability. Here, we demonstrate an efficient on-chip photodetector based on a few layers of MoTe₂ on a thin film lithium niobate waveguide and integrate it with a microresonator operating in an optical telecommunication band. The lithium-niobate-on-insulator waveguides and micro-ring resonator are fabricated using the femtosecond laser photolithography-assisted chemical–mechanical etching method. The lithium niobate waveguide-integrated MoTe₂ presents an absorption coefficient of 72% and a transmission loss of 0.27 dB µm⁻¹ at 1550 nm. The on-chip photodetector exhibits a responsivity of 1 mA W⁻¹ at a bias voltage of 20 V, a low dark current of 1.6 nA, and a photo–dark current ratio of 10⁸ W⁻¹. Due to effective waveguide coupling and interaction with MoTe₂, the generated photocurrent is approximately 160 times higher than that of free-space light irradiation. Furthermore, we demonstrate a wavelength-selective photonic device by integrating the photodetector and micro-ring resonator with a quality factor of 10⁴ on the same chip, suggesting potential applications in the field of on-chip spectrometers and biosensors. Full article

This paper proposes a highly linear low-noise amplifier (LNA) using a cascode configuration. In the proposed topology, the linearity of the circuit is enhanced through the application of derivative superposition technology. The technology combines an auxiliary transistor operating in the moderate inversion region with a main transistor operating in the strong inversion region, and two degenerative inductors are connected in series at the source nodes of both transistors. The primary objective of this design is to mitigate the negative impacts of second-order and third-order nonlinearities on the third-order input intercept point (IIP3) through their interactions, thereby enhancing the linear performance of the circuit. An on-chip active bias circuit is designed to effectively address fluctuations in the IIP3 during process and temperature variations by stabilizing the transconductance of the common-source transistor, enabling the LNA to operate reliably in complex environments. During post-layout simulation in DongBu High-Tech’s 0.13 μm CMOS process, the circuit’s output third-order intercept point (OIP3) exhibits minimal fluctuations across different process corners and temperature variations. At the typical nmos and typical pmos (TT) process corner and a temperature of 30 °C, it achieves an OIP3 of 33.9 dBm with a power consumption of 42 mW sourced from a 2.8 V power supply. Furthermore, it realizes a relatively flat gain of 16 dB, a noise figure (NF) of 0.91 dB, input return loss less than −8 dB, and output return loss less than −10 dB. Full article

Power management (PM) is cumbersome for today’s computing systems. Attainable performance is bounded by the architecture’s computing efficiency and capped in temperature, current, and power. PM is composed of multiple interacting layers. High-level controllers (HLCs) involve application-level policies, operating system agents (OSPMs), and PM governors and interfaces. The application of high-level control decisions is currently delegated to an on-chip power management unit executing tailored PM firmware routines. The complexity of this structure arises from the scale of the interaction, which pervades the whole system architecture. This paper aims to characterize the cost of the communication backbone between high-level OSPM agents and the on-chip power management unit (PMU) in high performance computing (HPC) processors. For this purpose, we target the System Control and Management Interface (SCMI), which is an open standard proposed by Arm. We enhance a fully open-source, end-to-end FPGA-based HW/SW framework to simulate the interaction between a HLC, a HPC system, and a PMU. This includes the application-level PM policies, the drivers of the operating system-directed configuration and power management (OSPM) governor, and the hardware and firmware of the PMU, allowing us to evaluate the impact of the communication backbone on the overall control scheme. With this framework, we first conduct an in-depth latency study of the communication interface across the whole PM hardware (HW) and software (SW) stack. Finally, we studied the impact of latency in terms of the quality of the end-to-end control, showing that the SCMI protocol can sustain reactive power management policies. Full article

Background/Objectives: The limited translatability of preclinical experimental findings to patients remains an obstacle for successful treatment of brain diseases. Relevant models to elucidate mechanisms behind brain pathogenesis, including cell-specific contributions and cell-cell interactions, and support successful targeting and prediction of drug responses in humans are urgently needed, given the species differences in brain and blood-brain barrier (BBB) functions. Human microphysiological systems (MPS), such as Organ-Chips, are emerging as a promising approach to address these challenges. Here, we examined and advanced a Brain-Chip that recapitulates aspects of the human cortical parenchyma and the BBB in one model. Methods: We utilized human primary astrocytes and pericytes, human induced pluripotent stem cell (hiPSC)-derived cortical neurons, and hiPSC-derived brain microvascular endothelial-like cells and included for the first time on-chip hiPSC-derived microglia. Results: Using Tumor necrosis factor alpha (TNFα) to emulate neuroinflammation, we demonstrate that our model recapitulates in vivo-relevant responses. Importantly, we show microglia-derived responses, highlighting the Brain-Chip’s sensitivity to capture cell-specific contributions in human disease-associated pathology. We then tested BBB crossing of human transferrin receptor antibodies and conjugated adeno-associated viruses. We demonstrate successful in vitro/in vivo correlation in identifying crossing differences, underscoring the model’s capacity as a screening platform for BBB crossing therapeutic strategies and ability to predict in vivo responses. Conclusions: These findings highlight the potential of the Brain-Chip as a reliable and time-efficient model to support therapeutic development and provide mechanistic insights into brain diseases, adding to the growing evidence supporting the value of MPS in translational research and drug discovery. Full article

Raman microlasers form on-chip versatile light sources by optical pumping, enabling numerical applications ranging from telecommunications to biological detection. Stimulated Raman scattering (SRS) lasing has been demonstrated in optical microresonators, leveraging high Q factors and small mode volume to generate downconverted photons based on the interaction of light with the Stokes vibrational mode. Unlike redshifted SRS, stimulated anti-Stokes Raman scattering (SARS) further involves the interplay between the pump photon and the SRS photon to generate an upconverted photon, depending on a highly efficient SRS signal as an essential prerequisite. Therefore, achieving SARS in microresonators is challenging due to the low lasing efficiencies of integrated Raman lasers caused by intrinsically low Raman gain. In this work, high-Q whispering gallery microresonators were fabricated by femtosecond laser photolithography assisted chemo-mechanical etching on thin-film lithium niobate (TFLN), which is a strong Raman-gain photonic platform. The high Q factor reached 4.42 × 10⁶, which dramatically increased the circulating light intensity within a small volume. And a strong Stokes vibrational frequency of 264 cm⁻¹ of lithium niobate was selectively excited, leading to a highly efficient SRS lasing signal with a conversion efficiency of 40.6%. And the threshold for SRS was only 0.33 mW, which is about half the best record previously reported on a TFLN platform. The combination of high Q factors, a small cavity size of 120 μm, and the excitation of a strong Raman mode allowed the formation of SARS lasing with only a 0.46 mW pump threshold. Full article

Mode-division multiplexing (MDM) is a promising multiplexing technique to further improve the transmission capacity of optical communication and on-chip optical interconnection systems. Furthermore, the multimode optical switch is of great importance in the MDM system, since it makes the MDM system more flexible by directly switching multiple spatial signals simultaneously. In this paper, we proposed a mode-independent optical switch based on the graphene–polymer hybrid waveguide platform that could process the TE₁₁, TE₁₂, TE₂₁ and TE₂₂ modes in a few-mode waveguide. The presented switch is independent of the four guided modes, optimizing the buried position of graphene capacitors in the polymer waveguide to regulate the coplanar interaction between the graphene capacitors and spatial modes. The TE₁₁, TE₁₂, TE₂₁ and TE₂₂ modes can be regulated simultaneously by changing the chemical potential of graphene capacitors in a straight waveguide. Our presented switch can enable the independent management of the spatial modes to be more flexible and efficient and has wide application in the MDM transmission systems. Full article

Microfluidic methods have proven to be effective in separation and isolation of cells for a wide range of biomedical applications. Among these methods, physical trapping is a label-free isolation approach that relies on cell size as the selective phenotype to retain target cells on-chip for follow-up analysis and imaging. In silico models have been used to optimize the design of such hydrodynamic traps and to investigate cancer cell transmigration through narrow constrictions. While most studies focus on computational fluid dynamics (CFD) analysis of flow over cells and/or pillar traps, a quantitative analysis of mechanical interaction between cells and trapping units is missing. The existing literature centers on longitudinally extended geometries (e.g., micro-vessels) to understand the biological phenomenon rather than designing an effective cell trap. In this work, we aim to make an experimentally informed prediction of the critical pressure for a cell to pass through a trapping unit as a function of cell morphology and trapping unit geometry. Our findings show that a hyperelastic material model accurately captures the stress-related softening behavior observed in cancer cells passing through micro-constrictions. These findings are used to develop a model capable of predicting and extrapolating critical pressure values. The validity of the model is assessed with experimental data. Regression analysis is used to derive a mathematical framework for critical pressure. Coupled with CFD analysis, one can use this formulation to design efficient microfluidic devices for cell trapping and potentially perform downstream analysis of trapped cells. Full article