arXiv subjects

Chenyu Wen

Publications and source records attributed to Chenyu Wen.

8 recordsLinked to original sources

arXiv2026

Memristive Behavior and Mechanism in Solid-State Nanopores

Nanofluidic memristors whose conductance evolves through history-dependent ionic transport and dynamic interfacial processes are promising building blocks for ionic neuromorphic applications. However, most existing designs rely on biological nanopores, polymers, and two-dimensional materials, which limit scalable fabrication and poses challenges to integration of ionic computing circuits and systems. Here, we report memristive behaviors of silicon-based solid-state nanopores (SSNPs) fabricated based on wafer-scale semiconductor processes. The SSNPs exhibit hysteretic current-voltage characteristics with a dependence on voltage sweeping frequency, electrolyte concentration, and nanopore geometry. To investigate the physical origin of their memory feature, the measured current of the SSNPs is decomposed into resistive, capacitive, and memristive components. An ion adsorption-desorption kinetics is developed to explain and predict the memristive behavior. A dynamical system analysis further reveals that the memristive behavior arises from delayed relaxation, thereby linking the measured hysteresis to the observed adaptive ionic response. Together, these findings establish native SSNPs as scalable ionic memristive elements and provide a generalized electrokinetic mechanism for memristive behavior under nanoconfinement. The resulting analytical framework connects device characterization with the underlying dynamics, deepens mechanism understanding, and guides the design of ionic neuromorphic devices.

physics.app-ph↗

arXiv2026

Cross-Modal Generation: From Commodity WiFi to High-Fidelity mmWave and RFID Sensing

AIGC has shown remarkable success in CV and NLP, and has recently demonstrated promising potential in the wireless domain. However, significant data imbalance exists across RF modalities, with abundant WiFi data but scarce mmWave and RFID data due to high acquisition cost. This makes it difficult to train high-quality generative models for these data-scarce modalities. In this work, we propose RF-CMG, a diffusion-based cross-modal generative method that leverages data-rich WiFi signals to synthesize high-fidelity RF data for scarce modalities including mmWave and RFID. The key insight of RF-CMG is to decouple cross-modal generation into high-frequency guidance and low-frequency constraint, which respectively learn high-frequency distribution from limited target modality data and preserve the underlying physical structure via low-frequency constraints during generation. On this basis, we introduce a Modality-Guided Embedding (MGE) module to steer the reverse diffusion trajectory toward the target high-frequency distribution, and a Low-Frequency Modality Consistency (LFMC) module to progressively enforce low-frequency constraints to suppress the accumulation of source-modality structural biases during inference, enabling high-quality target-modality generation. Performance comparison with several prevalent generative models demonstrates that RF-CMG achieves superior performance in synthesizing RFID and mmWave signals. We further showcase the effectiveness of the data generated by RF-CMG in gesture recognition tasks, and analyze the impact of the proportion of synthetic data on downstream performance.

cs.LG↗

arXiv2026

Denoise Stepwise Signals by Diffusion Model Based Approach

Stepwise signals are ubiquitous in single-molecule detections, where abrupt changes in signal levels typically correspond to molecular conformational changes or state transitions. However, these features are inevitably obscured by noise, leading to uncertainty in estimating both signal levels and transition points. Traditional frequency-domain filtering is ineffective for denoising stepwise signals, as edge-related high-frequency components strongly overlap with noise. Although Hidden Markov Model-based approaches are widely used, they rely on stationarity assumptions and are not specifically designed for signal denoising. Here, we propose a diffusion model-based algorithm for stepwise signal denoising, named the Stepwise Signal Diffusion Model (SSDM). During training, SSDM learns the statistical structure of stepwise signals via a forward diffusion process that progressively adds noise. In the following reverse process, the model reconstructs clean signals from noisy observations, integrating a multi-scale convolutional network with an attention mechanism. Training data are generated by simulating stepwise signals through a Markov process with additive Gaussian noise. Across a broad range of signal-to-noise ratios, SSDM consistently outperforms traditional methods in both signal level reconstruction and transition point detection. Its effectiveness is further demonstrated on experimental data from single-molecule Forster Resonance Energy Transfer and nanopore DNA translocation measurements. Overall, SSDM provides a general and robust framework for recovering stepwise signals in various single-molecule detections and other physical systems exhibiting discrete state transitions.

eess.SP↗

arXiv2026

Bifurcation Analysis Framework of Spiking Neuron Models

Neuromorphic computing targets energy-efficient event-driven information processing by placing artificial spiking-neurons at its core. Artificial neuron devices and circuits have multiple operating modes and produce region-dependent nonlinear dynamics that are not captured by system analysis methods for linear systems, such as transfer functions, Fourier/Laplace transform, Bode diagram, etc. Thus, new tools are needed to evaluate the nonlinear behavior of neurons and to guide the design and optimization of artificial neuron implementations. Here we present a generalized bifurcation analysis framework based on nonlinear dynamical systems theory. A CMOS axon-hillock neuron, memristor neuron, and the FitzHugh-Nagumo biological neuron model are selected for demonstration. We evaluate Hopf bifurcation conditions to define the rest and firing domains in parameter space of the system, and predict the near-onset firing rate. The results are further compared with numerical simulations. The framework standardizes the analysis for various neuron models and physical realizations. It yields practical design and optimization guidelines for artificial neurons for neuromorphic computing systems, including parameter combinations to make a neuron fire/rest and to control the corresponding firing properties, e.g., firing rate and amplitude.

physics.app-ph↗

arXiv2025

Amorphous phase-change memory alloy with no resistance drift

Spontaneous structural relaxation is intrinsic to glassy materials due to their metastable nature. For phase-change materials (PCMs), the resultant temporal change in electrical resistance seriously hamper in-memory computing (IMC) applications. Here, we report an ab-initio-calculation-informed design of amorphous PCM composed of robust "molecule-like" motifs with minimal Peierls distortion, depriving the amorphous alloy of structural ingredients that would gradually evolve upon aging to entail resistance drift. We demonstrate amorphous CrTe3 thin films that display practically no resistance drift at any working temperature from -200 to 165 degree C. We achieve multilevel programming of CrTe3 through both step-wise crystallization and step-wise amorphization using a hybrid opto-electronic device at various temperatures. Moreover, the application potential of CrTe3 in neuromorphic computing is testified by its incorporation in a vehicle with automatic path-tracking function. Our work opens a new avenue to achieving IMC-requisite properties via judicious design of the composition and atomic-level structure of disordered PCM alloys.

cond-mat.mtrl-sci↗

arXiv2025

Role of seed layer in growing atomically flat TiTe2/Sb2Te3 heterostructure thin films at the wafer scale

Chalcogenide phase-change materials (PCMs) are a leading candidate for advanced memory and computing applications. Epitaxial-like growth of chalcogenide thin films at the wafer scale is important to guarantee the homogeneity of the thin film but is challenging with magnetron sputtering, particularly for the growth of phase-change heterostructure (PCH), such as TiTe2/Sb2Te3. In this work, we report how to obtain highly textured TiTe2/Sb2Te3 heterostructure thin films with atomically sharp interfaces on standard silicon substrates. By combining atomic-scale characterization and ab initio simulations, we reveal the critical role of the Sb2Te3 seed layer in forming a continuous Si-Sb-Te mixed transition layer, which provides a wafer-scale flat surface for the subsequent epitaxial-like growth of TiTe2/Sb2Te3 thin film. By gradually reducing the thickness of the seed layer, we determine its critical limit to be ~2 nm. Non-negligible in-plane tensile strain was observed in the TiTe2 slabs due to the lattice mismatch with the adjacent Sb2Te3 ones, suggesting that the chemical interaction across the structural gaps in the heterostructure is stronger than a pure van der Waals interaction. Finally, we outline the potential choices of chalcogenides for atomically flat seed layers on standard silicon substrates, which can be used for wafer-scale synthesis of other high-quality PCM or PCH thin films.

cond-mat.mtrl-sci↗

arXiv2021

Deep learning of nanopore sensing signals using a bi-path network

Temporary changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because empirical amplitude thresholds are user-defined to single out the pulses from the noisy background. Here, we use deep learning for feature extraction based on a bi-path network (B-Net). After training, the B-Net acquires the prototypical pulses and the ability of both pulse recognition and feature extraction without a priori assigned parameters. The B-Net performance is evaluated on generated datasets and further applied to experimental data of DNA and protein translocation. The B-Net results show remarkably small relative errors and stable trends. The B-Net is further shown capable of processing data with a signal-to-noise ratio equal to one, an impossibility for threshold-based algorithms. The developed B-Net is generic for pulse-like signals beyond pulsed nanopore currents.

eess.SP↗

arXiv2017

A zero-depth nanopore capillary for the analysis of translocating biomolecules

High-fidelity analysis of translocating biomolecules through nanopores demands shortening the nanocapillary length to a minimal value. Existing nanopores and capillaries, however, inherit a finite length from the parent membranes. Here, we form nanocapillaries of zero depth by dissolving two superimposed and crossing metallic nanorods, thereby opening two overlapping nanofluidic channels molded in a polymeric resin. In an electrolyte, the interface shared by the crossing fluidic channels is mathematically of zero thickness and defines the narrowest constriction in the stream of ions through the nanopore device. This novel architecture provides the possibility to design nanopore fluidic channels, particularly with a robust 3D architecture maintaining the ultimate zero thickness geometry independently of the thickness of the fluidic channels. With orders of magnitude reduced biomolecule translocation speed, and lowered electronic and ionic noise compared to nanopores in 2D materials, our findings establish interfacial nanopores as a scalable platform for realizing nanofluidic systems, capable of single-molecule detection.

physics.bio-ph↗