For the emerging semiconductor transistors, accurate modeling and simulation of these complex devices remain challenging due to their unique physical mechanisms and nanoscale dimensions. This paper presents an easy-use, multi-scale device modeling framework designed to address the challenges of modeling and circuit-level analysis for advanced nanoscale transistors. By integrating physics-based device modeling, automated parameter extraction, and netlist-based circuit simulation, the framework enables simulation from material properties to system-level performance for nanoscale transistors. A key innovation lies in the integration of the Trust Region Reflective (TRR) algorithm with open-source tools to realize a universal, data-training-free parameter extraction workflow, which fills the gap of user-friendly parameter extraction tools for emerging devices. The framework's reliability and flexibility are validated through three representative device cases: FinFETs, graphene transistors, and molybdenum disulfide $\boldsymbol{(} \mathbf{M o S}_{\mathbf{2}} \boldsymbol{)}$ transistors, achieving Root Mean Square Errors (RMSE) of 3.17%, 4.06%, and 4.97% respectively. Circuit-level verification via 19-stage ring oscillators (8.91 GHz operation) and 8T-SRAM modules further confirms its practicality. This framework facilitates rapid prototyping and optimization of emerging transistors, potentially accelerating the transition from lab-scale devices to commercial integrated circuits.
As a high-speed search engine, ternary contentaddressable memory (TCAM) performs fully parallel search operation which activates all cells within each single cycle. However, this results in huge search power consumption especially in voltage-division-based TCAM. To address this issue, we propose a 2-bit encoding scheme and a dynamic power gating architecture for voltage-division-based TCAM. Hybrid simulation is obtained by utilizing a spin transfer torque based MTJ (STT-MTJ) compact model and a 28 nm CMOS design kit. The simulation results demonstrate that the proposed low-power TCAM reaches at least 48.5% power reduction in the worst case compared with conventional designs.
In this paper, we proposed and analyzed a novel four-layer slot integrated optic biosensor for the measurement of temperature. The four-layer configuration is based on siliconon-insulator technology and consists of an intermediate buffer layer. In a four-layer configuration, a thin silicon nitride buffer layer is inserted between the rail and the substrate to enhance the sensitivity, and the cover layer consisting of a bio-analyte to be measured, silicon, and silicon dioxide. In the case of three layer waveguide, the mode confined in the slot influences the sensitivity. Introduction of silicon nitride layer between the oxide and rail layer reduces the refractive index contrast between the substrate and the rail layer resulting in increased penetration depth we obtain a sensitivity $\mathbf{3. 2} \times \mathbf{1 0}^{-4} /{ }^{\circ} \mathbf{C}$.
With the development of the internet of things (IoT), the traditional von-Neumann architecture faces limitations from memory wall, preventing further chip performance improvements. To solve this problem, computing-in-memory (CIM) has been proposed as an effective solution. However, the static storage of weight data in CIM based on non-volatile technology exposes them to malicious attacks. This paper proposes a magnetoresistive random-access memory (MRAM)based multiply-encryption physical unclonable function (PUF) -CIM (Multi-PUFim) to enhance security by encrypting CIM using PUF for multi-encryption obfuscation. The proposed architecture supports both PUF and CIM modes on the same hardware platform. In PUF mode, the design shows strong resistance to modeling attacks, with a prediction accuracy remaining around 50%. In CIM mode, PUF-based encryption reduces the malicious inference accuracy on VGG-8 (CIFAR-10) to approximately $\mathbf{1 0 \%}$. Moreover, the encrypted CIM mode achieves an energy efficiency of approximately 30.498 \~{} 33.489 TOPS/W, effectively balancing security with high computational efficiency.
Emerging planar computing platforms such as atomic-scale computing and silicon photonics combine gates and interconnects in a single 2D plane, making wiring and crossings dominant bottlenecks. This work reframes differentiable logic network training as one-pass synthesis: BitPlanarNet trains a strictly planar gate network that maps one-to-one to device primitives. Building on differentiable logic gate networks (DL-GNs), each neuron selects a 2-input/2-output primitive and connects only to nearest neighbors, yielding a gate-level layout at discretization. It is demonstrated that this concept is physically viable by successfully fabricating a $33 \times 25 ~\text{nm}^{2}$ representative learned dangling-bond logic layout on a hydrogen-passivated silicon surface using a scanning tunneling microscope. On imageclassification tasks, BitPlanarNet achieves accuracy comparable to unconstrained DLGNs-remaining within approximately 1 percentage point on MNIST and 5 percentage points on CIFAR-10-while guaranteeing planar connectivity, providing a versatile methodology for translating learned tasks directly into realizable gate layouts on emerging planar technologies.
In this study, we introduce a novel concept of the transport properties based on the self-consistent charge density functional tight-binding (DFTB) method, and we apply it in three graphite FETs. The Projected Density of States (PDOS) calculations indicate a configuration-dependent electronic response of N-doped graphite: graphitic N increases the density of states near the Fermi level and shifts the local minimum below the Fermi level, consistent with $\boldsymbol{n}$-type behavior, whereas defectassociated pyridinic N shifts the local minimum above the Fermi level, consistent with $\boldsymbol{p}$-type behavior. The novelty of this work is therefore not the general observation that nitrogen can modify the carrier character of graphitic materials, but the comparative demonstration that different N configurations produce distinct DOS features and transport responses when implemented in the same graphite-based FET architecture. The outcomes demonstrate that strongly nonlinear I-V can be achieved in defective systems compared to pure ones. Furthermore, these findings show that the doped FETs exhibit similar current values between them in the positive low-voltage zone, and these values are higher than those of the undoped system in the higher voltage range. The N-doping had the effect of increasing the transmission at the Fermi level. Moreover, the presence of graphitic N increased the current significantly more than the other doped system in the high positive voltage area.
Traditional intelligent vision systems suffer from severe latency and energy bottlenecks due to massive data transmission and analog-to-digital (A/D) conversion requirements. This paper proposes a highly energy-efficient intelligent visual recognition system based on the Sensing-with-Computing (Senputing) architecture. By integrating computing circuits directly into the pixels, the proposed system executes Binarized Neural Network (BNN) convolution operations utilizing photocurrents. This approach effectively circumvents the additional overhead associated with photocurrent-to-voltage (I-to-V) conversion, analog data storage, and A/D conversion. Simultaneously, an efficient two-dimensional (2D) kernel scheduling method significantly enhances computation parallelism across the focal plane, thereby improving the efficiency of feature map construction. Fabricated using a 65-nm CMOS process, the prototype chip achieves a recognition accuracy of 98.1% on the MNIST dataset. It consumes only $2.14 \mu \mathrm{W}$ at 120 frames per second (fps), yielding a peak energy efficiency of 11.49 TOPS/W, providing an ideal ultra-low-power solution for always-on edge intelligent devices. Furthermore, the compact system-level design paves the way for broader application-level development of the Senputing architecture on edge devices.
Wurtzite-based ferroelectric nitrides are promising for high-performance non-volatile applications, yet AlN-based systems are often limited by bandgap narrowing and associated leakage risks. In this study, using first-principles calculations combined with HSE06 hybrid functionals and nudged elastic band (NEB) analyses, the effects of Sc, Y, and B doping in AlN and GaN lattices are systematically compared. A fundamental divergence in electronic evolution is revealed: while doped AlN undergoes monotonic bandgap reduction, GaN-based systems exhibit a unique bandgap enhancement trend. Mechanistically, Sc and Y doping maintain consistent collective switching characteristics across both host lattices. A key highlight of this work is the prediction of a dual-pathway coexistence in AIGaN and GaBN, where collective and individual switching mechanisms compete. By establishing estimated feasible doping windows based on simplified dielectric breakdown and thermal stability limits, we demonstrate that GaN's higher energy tolerance significantly may broadens the theoretical design space for polarization switching with improved tolerance against electronic degradation.
Millions of individuals with motor disabilities and speech impairments face significant barriers when interacting with modern smart devices, which predominantly rely on voice or touch-based input. Silent speech interfaces provide a promising alternative by decoding articulatory or physiological signals without producing audible sound. However, many existing SSI systems rely on complex, high-cost instrumentation or invasive sensing approaches, limiting their practicality for everyday assistive use. Developing a lightweight, low-cost, and noninvasive wearable solution for reliable silent speech interaction therefore remains a critical challenge. Here, we present a multimodal wearable bioelectronic system that recognizes a small command vocabulary from non-acoustic articulatory signals. The system fuses two complementary modalities in a compact, anatomically guided form factor: a chin-mounted inertial measurement unit for mandibular kinematics and an ultrathin graphene-based laryngeal strain sensor for throat deformation. Signals are synchronously sampled at 100 Hz and streamed via Bluetooth for real-time processing. A nine-class dataset (eight silent commands plus rest) was collected from five subjects (1,215 samples). A support vector machine using a radial basis function kernel achieved the best generalization performance, reaching a test accuracy of 83.56%. These results indicate that our multimodal wearable bioelectronic system enables practical, privacy-preserving, hands-free laryngeal human-computer interaction.
Lead-free bismuth-based perovskites, such as methylammonium bismuth bromide $\left(\mathbf{M A}_{\mathbf{3}} \mathbf{B i}_{\mathbf{2}} \mathbf{B r}_{\mathbf{9}}, \mathbf{M B B}\right)$, are investigated as sustainable alternatives to toxic lead-halide materials for ultrafast photonic applications. While the stoichiometric growth of high-quality MBB single crystals was successfully optimized in previous work using a 3:2 precursor molar ratio, the mechanistic role of solvent polarity during integration into polymer matrices is identified as a critical manufacturing bottleneck. In this study, the fundamental chemical interactions, specifically dielectric dissociation and hydrogen bonding, are characterized as the factors dictating the structural properties and successful encapsulation of MBB/polydimethylsiloxane (PDMS) composites. Lattice degradation and the dissolution of organic $\mathbf{C H}_{\mathbf{3}} \mathbf{N H}_{\mathbf{3}}{}^{+}$ cations are observed in polar solvents such as isopropanol (ISO) and acetone (ACE) due to high dielectric constants and hydrogen bonding with bromide ions. Conversely, the organic-inorganic vibrational modes and optical bandgap (2.62-2.66 eV) are effectively preserved by non-polar solvents such as hexane (HEX) and toluene (TOL).
The integration of large-scale perovskite films with CMOS technology is crucial to unlock the full potential of broadband image sensing. Despite significant advances, challenges remain in achieving high functional pixel density, uniform film quality, and seamless integration with standard semiconductor processes. In this work, we present a breakthrough approach for depositing exceptionally uniform $\text{ s}_{0.05}(\text{FA}_{0.98} \text{MA}_{0.02})_{0.95} \text{Pb}(\mathrm{I}_{0.98} \text{Br}_{0.02})_{3}$ perovskite films onto CMOS chips using vacuum quenching. The resulting 640 × 512 pixel sensor achieves 99.997% of functional pixel density, with broadband ultraviolet-visible-near infrared (UV-Vis-NIR, 300nm-1000nm) response, attributed to excellent uniformity of perovskite films. Additionally, an on-chip broadband image fusion algorithm optimally merges data from the three spectral bands, improving image quality and detail extraction. This monolithic perovskite-CMOS integration approach provides a cost-effective solution for high-performance broadband imaging.
Restoring natural hearing through fully implantable systems remains a key challenge for cochlear implant technology. Current half-implantable devices rely on bulk external components that limit comfort and convenience, especially for Alzheimer's patients. Advances in surface micromachining enable the development of compact, biocompatible acoustic sensors suitable for full implantation. This research is the first to report the design, fabrication, and evaluation of Capacitive Micromachined Acoustic Receivers (CMARs) utilizing a polyimide‐2610 (PI-2610) diaphragm, demonstrating low-frequency acoustic sensing performance and potential for implantable cochlear implant applications. Experimental results from simulations, eigenfrequency measurements, and simulated applications based on using real human sounds to evaluate the properties and performance of CMARs, showing an eigenfrequency of 169 kHz and successfully collecting human sounds with a sensitivity of 9 mV/Pa. The devices replace traditional silicon-based diaphragms with polymer PI-2610 to achieve a lower eigenfrequency at comparable dimensions, and their outstanding performance and stable operation validate polymer-based CMARs as promising candidates for acoustic front-end components in fully implantable cochlear implant systems.
In-memory computing (IMC) enables highthroughput, energy-efficient deep neural network inference, but scalable multi-bit weight representation remains constrained by the difficulty of reliably writing multiple device states, often leading to binary weight-slicing implementations. In this work, we investigate lining up multiple magnetic tunnel junction (MTJ) pillars on the same track to realize compact multilevel cell (MLC) storage. Selective programming is enabled by voltage-gated spin-orbit torque (VGSOT) switching, through voltage-controlled magnetic anisotropy (VCMA) effect. We propose to use multi-pillar SOT-MRAM bitcells to encode up to 7 signed levels. We design an AiMC architecture tailored to multi-pillar VGSOT-MRAM with a time-to-digital converter (TDC) sensing scheme. We show that, compared to binary weight-slicing crossbars, the proposed approach achieves $2.8 \times$ better energy efficiency with negligible accuracy degradation on MNIST, CIFAR-10, CIFAR-100, and ImageNet.
Nanomaterials, such as silver nanoparticles (AgNPs), are continuously being studied due to their distinct size-dependent properties enabling them to have multirole applications such as antimicrobial agents, catalysts, and colorimetric sensors. In this study, the green synthesis of silver nanoparticles using lanzones (Lansium domesticum) leaves extract as a natural reducing agent and stabilizing agent was explored. The phytochemical rich extracts allowed the reduction of silver ions (Ag+) to metallic silver (Ag0) nanoparticles monitored using its surface plasmon resonance between 400 to 450 nm via UV-Vis spectroscopy. The synthesized AgNPs showed spherical morphology with an average size of 14.13 ± 6.96 nm as shown in SEM and TEM. FTIR spectroscopy confirms the presence of organic functional groups capping the AgNPs. This result was supported by the elemental analysis via EDS showing elemental C and O present in the dried AgNP powder. The hydrodynamic radius of the AgNP is 73.03 nm with a polydispersity index (PDI) of 0.192, indicating uniform size distribution. The multifunctional performance of the green-synthesized AgNP was explored in three different applications: antimicrobial activity, catalytic reduction of 4-nitrophenol, and colorimetric sensing of Hg2+ ions. These properties exhibited by the as-prepared AgNP can be potentially utilized in various environmental remediation applications.
With the ongoing progress of quantum computing, there is increasing interest in bringing quantum technologies into real-world applications, making practically motivated NP-hard problems particularly important to study. In this work, we solve the traveling salesperson problem (TSP) on real quantum hardware to demonstrate and analyze the performance of different quantum chips, and we benchmark these results against simulations. This comparison provides evidence supporting the feasibility of deploying quantum computation toward practical applications.
Numerical simulations were performed to calculate the fine-structure splitting (FSS) in InGaN/GaN quantum dots (QDs) oriented in the non-polar $\boldsymbol{m}$-plane. These devices are excellent candidates for entangled photon pair generation (EPPG) and the FSS must be as small as possible to ensure proper entanglement. The many-body excitonic energy is calculated by coupling a full configuration interaction (FCI) method with an atomistic multi-band tight-binding (TB) model. We report that FSS in these nanostructures is highly sensitive to the overall geometry as well as to the underlying atomicity of the active light-emitting region.
Preserving quantum information for a reasonably long time is the fundamental requirement that must be fulfilled to realize memory units. Here we computationally explore how electron spins in the Silicon (Si) quantum dot (QD) platform, which is actively employed for implementation of Si quantum processors, can be electrically manipulated to store quantum states. Using the Heisenberg spin model characterized with our multi-scale device simulations conducted against a realistically Si double QD (DQD) platform, we secure the control signal that is needed to store arbitrary quantum states and show that the time-driven loss in fidelity of stored states can be refreshed in a known way. Results that we deliver in this work can be served as a preliminary guideline for designs of quantum memory units.
By mapping combinatorial optimization problems and executing an energy minimization process, Ising Machines provide a promising paradigm for finding sub-optimal solutions for NP problems. However, most existing Ising annealers for combinatorial optimization problems only support problem space exploration with binary variables. There exists a gap between the existing designs of Ising annealers and the requirement for solving combinatorial optimization problems with multiple labels. In this paper, we present a general-purpose framework built upon a reconfigurable and parallel probabilistic processor to deploy multi-state graphical models efficiently. By decoupling Ising model specification from hardware implementation, the proposed framework bridges the gap between specialized Ising hardware and general combinatorial optimization workloads. By using Gibbs dynamics instead of Glauber dynamics to evolve the model, we achieve shorter time-to-solution compared with other evolution algorithms. Our general-purpose framework is compatible with multiple hardware implementations for computing modules and stochastic modules. The hardware simulation of our digital prototype shows 70\~{}5940 computing speed acceleration and $\mathbf{1 0}^{\mathbf{5}} \boldsymbol{\sim} \mathbf{1 0}^{\mathbf{6}} \times$ energy efficiency compared with common CPU/GPU computing platforms.
AlGaN/GaN FinFET high-electron-mobility transistors (HEMTs) offer improved electrostatic control and scalability compared to planar architectures; however, their performance and reliability are strongly influenced by device geometry and atomistic effects. This work investigates enhancement-mode operation and defect-related degradation mechanisms in AlGaN/GaN FinFET HEMTs using numerical simulations. The effects of fin geometry, Schottky sidewall contacts, atomicity, and trapped charges in the channel and barrier layer are examined. The results highlight the sensitivity of threshold voltage, channel mobility, and drive current to defect location and density, emphasizing key design trade-offs for reliable operation of nanoscale GaN FinFETs.
Transition metal chalcogenides (TMDs) exhibit excellent optical and electrical properties, making them promising robust resistive switching layer materials for two-dimensional memristor. However, the controllability of their resistive switching behavior via defect engineering remains a critical challenge. This work proposes a high-performance two-dimensional $\mathbf{M o T e}_{\mathbf{2}}$ memristor based on defect engineering regulation. By regulating the size of the active area, the defect quantity is controlled, and the electrical performance of the memristor is further regulated. When the active area is $400 \text{um}^{2}$, the overall performance of the memristor is the best. At the same time, the influence of working temperature on the performance of the memristor is studied. As the temperature rises, the switching voltage of the memristor decreases, while the switching ratio remains unchanged. This study clarifies the regulation mechanism of defects on conductive filaments through the strategy of combining temperature and size regulation, providing new ideas and experimental basis for the design of high-performance two-dimensional memristors.
