In situ training of feed-forward and recurrent convolutional memristor networks

Deep Residual Learning for Image Recognition

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Memristor Crossbar Array with Enhanced Device Yield for In-Memory Vector–Matrix Multiplication

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A biomimetic nociceptor system with tactile perception based on Ni-Al layered double hydroxides/gelatin heterojunction memristor

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Recent Progress on Memristive Convolutional Neural Networks for Edge Intelligence

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Analog Synaptic Devices Based on IGZO Thin‐Film Transistors with a Metal–Ferroelectric–Metal–Insulator–Semiconductor Structure for High‐Performance Neuromorphic Systems

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Demonstration of In‐Memory Biosignal Analysis: Novel High‐Density and Low‐Power 3D Flash Memory Array for Arrhythmia Detection

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Reconfigurable neuromorphic computing block through integration of flash synapse arrays and super-steep neurons

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Towards Efficient In-Memory Computing Hardware for Quantized Neural Networks: State-of-the-Art, Open Challenges and Perspectives

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Convolutional Neural Network Based on Crossbar Arrays of (Co-Fe-B)x(LiNbO3)100−x Nanocomposite Memristors

Matsukatova, A. N., Iliasov, A. I., Nikiruy, K. E., Kukueva, E. V., Vasiliev, A. L., Goncharov, B. V., Ситников, А. В., Занавескин, М. Л., Bugaev, A. S., Demin, V. A., et al. (2022) · Nanomaterials · doi:10.3390/nano12193455 · Cited by 27

Competitive Neural Network Circuit Based on Winner-Take-All Mechanism and Online Hebbian Learning Rule

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Generative complex networks within a dynamic memristor with intrinsic variability

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AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial IntelligencePreprint

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Pruning and quantization algorithm with applications in memristor-based convolutional neural network

Guo, M. L., Sun, Y., Zhu, Y., Han, M., Dou, G., & Wen, S. (2023) · Cognitive Neurodynamics · doi:10.1007/s11571-022-09927-7 · Cited by 25

High-Throughput, Area-Efficient, and Variation-Tolerant 3-D In-Memory Compute System for Deep Convolutional Neural Networks

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A High-Precision Implementation of the Sigmoid Activation Function for Computing-in-Memory Architecture

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MNEMOSENE: Tile Architecture and Simulator for Memristor-based Computation-in-memory

Zahedi, M., Lebdeh, M. A., Bengel, C., Wouters, D. J., Menzel, S., Gallo, M. L., Sebastian, A., Wong, S., & Hamdioui, S. (2022) · ACM Journal on Emerging Technologies in Computing Systems · doi:10.1145/3485824 · Cited by 24

Constructing van der Waals heterostructures by dry-transfer assembly for novel optoelectronic device

Li, H., Xiong, X., Hui, F., Yang, D., Jiang, J., Feng, W., Han, J., Duan, J., Wang, Z., & Sun, L. (2022) · Nanotechnology · doi:10.1088/1361-6528/ac5f96 · Cited by 25

Quantitatively Evaluating the Effect of Read Noise in Memristive Hopfield Network on Solving Traveling Salesman Problem

Lü, J., Wu, Z., Zhang, X., Wei, J., Fang, Y., Shi, T., Liu, Q., Wu, F., & Liu, M. (2020) · IEEE Electron Device Letters · doi:10.1109/led.2020.3021593 · Cited by 24

All‐Ferroelectric Spiking Neural Networks via Morphotropic Phase Boundary Neurons

Kim, J., Park, E. C., Shin, W., Koo, R., Im, J., Han, C., Lee, J., & Kwon, D. (2024) · Advanced Science · doi:10.1002/advs.202407870 · Cited by 24

Memristor-based adaptive analog-to-digital conversion for efficient and accurate compute-in-memory

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Memristive System Based Image Processing Technology: A Review and Perspective

Ji, X., Dong, Z., Zhou, G., Lai, C. S., Yan, Y., & Qi, D. (2021) · Electronics · doi:10.3390/electronics10243176 · Cited by 25

QuantBayes: Weight Optimization for Memristive Neural Networks via Quantization-Aware Bayesian Inference

Zhou, Y., Hu, X., Wang, L., Zhou, G., & Duan, S. (2021) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2021.3115787 · Cited by 22

Advances of Emerging Memristors for In-Memory Computing Applications

Chen, Q., Lin, L., Meng, J., Xu, M., & Wang, T. (2025) · Research · doi:10.34133/research.0916 · Cited by 22

Long Short-Term Memory Implementation Exploiting Passive RRAM Crossbar Array

Nikam, H., Satyam, S., & Sahay, S. (2021) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2021.3133197 · Cited by 21

In-Sensor Computing Realization Using Fully CMOS-Compatible TiN/HfO x -Based Neuristor Array

Zhang, H., Qiu, P., Lu, Y., Ju, X., Chi, D., Yew, K. S., Zhu, M., Wang, S. H., Wei, R., & Hu, W. (2023) · ACS Sensors · doi:10.1021/acssensors.3c01418 · Cited by 21

A Fast Spatial-temporal Information Compression algorithm for online real-time forecasting of traffic flow with complex nonlinear patterns

Xu, Z., Lv, Z., Chu, B., & Li, J. (2024) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2024.114852 · Cited by 21

Self-Powered Memristive Systems for Storage and Neuromorphic Computing

Shi, J., Wang, Z., Tao, Y., Xu, H., Zhao, X., Lin, Y., & Liu, Y. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.662457 · Cited by 24

Framework for In-Memory Computing Based on Memristor and Memcapacitor for On-Chip Training

Singh, A., & Lee, B.-G. (2023) · IEEE Access · doi:10.1109/access.2023.3324375 · Cited by 19

PowerGAN: A Machine Learning Approach for Power Side‐Channel Attack on Compute‐in‐Memory Accelerators

Wang, Z., Wu, Y., Park, Y., Yoo, S., Wang, X., Eshraghian, J. K., & Lu, W. D. (2023) · Advanced Intelligent Systems · doi:10.1002/aisy.202300313 · Cited by 20

A Hardware and Energy-Efficient Online Learning Neural Network With an RRAM Crossbar Array and Stochastic Neurons

Yeo, I., Gi, S.-G., Wang, G., & Lee, B. (2021) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2020.3032867 · Cited by 21

Current opinions on memristor-accelerated machine learning hardware

Jiang, M., Xu, Y., Li, Z., & Li, C. (2025) · Current Opinion in Solid State and Materials Science · doi:10.1016/j.cossms.2025.101226 · Cited by 20

A Multi-Model Ensemble for Advanced Prediction of Reverse Osmosis Performance in Full-Scale Zero-Liquid Discharge Systems

Ding, H., Hao, N., Cao, Q., Hei, S., Zhong, X., Liang, S., & Huang, X. (2025) · Environmental Science & Technology · doi:10.1021/acs.est.5c06257 · Cited by 17

Entangled quantum memristors

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Memristive Fast-Canny Operation for Edge Detection

Tian, J., Zhou, H., Bao, H., Chen, J., Huang, X., Li, J.-C., Yang, L., Li, Y., & Miao, X. (2022) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2022.3204525 · Cited by 17

Artificial neuromodulator–synapse mimicked by a three-terminal vertical organic ferroelectric barristor for fast and energy-efficient neuromorphic computing

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Memristive Device Characteristics Engineering by Controlling the Crystallinity of Switching Layer Materials

Yang, H., Chen, B., Song, B., Meng, D., Tiwari, S., Krishnamoorthy, A., Yan, X., Liu, Z., Wang, Y., Hu, P., et al. (2020) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.0c00148 · Cited by 17

Large-scale crossbar arrays based on three-terminal MoS2 memtransistors

Schranghamer, T. F., Pannone, A., Kumar, J. M., Baiju, D. K. T., Chen, C., Knight, T. V. M., Tadekawa, S., Haines, E., Ordoñez, R. C., Hayashi, C. K., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64536-2 · Cited by 19

Device‐System End‐to‐End Design of Photonic Neuromorphic Processor Using Reinforcement Learning

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High Robustness Memristor Neural State Machines

Tian, L., Wang, Y., Shi, L., & Zhao, R. J. (2020) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.0c00700 · Cited by 16

Recent progress in optoelectronic memristive devices for in-sensor computing

Shan, X., Wang, Z., Xie, J., Zheng, J., Xu, H., & Liu, Y. (2022) · Acta Physica Sinica · doi:10.7498/aps.71.20220350 · Cited by 15

Resistive switching kinetics of electrolyte-gated polyaniline-based memristive devices

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2D‐SnS‐Embedded Schottky Device with Neurotransmitter‐Like Functionality Produced Using Proximity Vapor Transfer Method for Photonic Neurocomputing

Kumar, N., Patel, M., Nguyen, T. T., Lee, J., Choi, C., Bhatnagar, P. K., & Kim, J. (2024) · Advanced Materials · doi:10.1002/adma.202411420 · Cited by 15

Investigating the effect of electrical and thermal transport properties on oxide-based memristors performance and reliability

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A ReRAM-Based Convolutional Neural Network Accelerator Using the Analog Layer Normalization Technique

Gi, S.-G., Lee, H., Jang, J., & Lee, B. (2022) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2022.3190876 · Cited by 15

ZnO photoconductive synaptic devices for neuromorphic computing

Shang, Q., Peng, W., Song, T., Li, Z., Li, F., & He, Y. (2023) · Materials Science in Semiconductor Processing · doi:10.1016/j.mssp.2023.107489 · Cited by 14

Bio-inspired artificial heterosynapse based on carbon nanotubes memtransistor with dynamically tunable analog switching behavior

Sun, Y., Wang, Y., Yuan, Q., & Li, B. (2023) · Materials Today Nano · doi:10.1016/j.mtnano.2023.100398 · Cited by 14

DS-CIM: A 40nm Asynchronous Dual-Spike Driven, MRAM Compute-In-Memory Macro for Spiking Neural Network

Fu, H., Huang, Y., Chen, T., Fu, C., Ren, H., Zhou, Y., Peng, S., Zong, Z., Pan, B., & Cheng, B. (2024) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2024.3352729 · Cited by 16

Exploiting Non-idealities of Resistive Switching Memories for Efficient Machine Learning

Yon, V., Amirsoleimani, A., Alibart, F., Melko, R. G., Drouin, D., & Beilliard, Y. (2022) · Frontiers in Electronics · doi:10.3389/felec.2022.825077 · Cited by 14

Vertical Organic Ferroelectric Synaptic Transistor for Temporal Information Processing

Sun, Y., Wang, Y., Yuan, Q., He, N., & Wen, D. (2022) · Advanced Materials Interfaces · doi:10.1002/admi.202201421 · Cited by 14

Brain-like training of a pre-sensor optical neural network with a backpropagation-free algorithm

Huang, Z., Wang, C., Zhang, C., Shi, W., Wu, S., Yang, S., & Chen, H. (2025) · Photonics Research · doi:10.1364/prj.539630 · Cited by 13

Low-time-complexity document clustering using memristive dot product engine

Zhou, H., Li, Y., & Miao, X. (2022) · Science China Information Sciences · doi:10.1007/s11432-021-3316-x · Cited by 12

Optimizing Memristor-Based Synaptic Devices for Enhanced Energy Efficiency and Accuracy in Neuromorphic Machine Learning

Gökgöz, B., Aydın, T., & Gül, F. (2024) · IEEE Access · doi:10.1109/access.2024.3482110 · Cited by 16

Emerging Materials and Computing Paradigms for Temporal Signal Analysis

Zhang, T., Woźniak, S., Syed, G. S., Mannocci, P., Farronato, M., Ielmini, D., Sebastian, A., & Yang, Y. (2025) · Advanced Materials · doi:10.1002/adma.202408566 · Cited by 14

Self-Powered Organic Optoelectronic Synapses with Binarized Weights for Noise-Suppressed Visual Perception and High-Robustness Inference

Jiang, S., Peng, L., Hao, Z., Du, X., Gu, J., Su, J., Guo, H., Gu, D., Zhang, H., Wang, Q., et al. (2023) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.3c00698 · Cited by 13

A Quantized Convolutional Neural Network Implemented With Memristor for Image Denoising and Recognition

Zhang, Y., Wu, Z., Liu, S., Guo, Z., Chen, Q., Gao, P., Wang, P., & Liu, G. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.717222 · Cited by 12

Brain-Inspired Recognition System Based on Multimodal In-Memory Computing Framework for Edge AI

Xiao, H., Xie, D., Hu, X., Zhou, Y., Zhou, G., & Duan, S. (2023) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2023.3337532 · Cited by 12

Topology optimization of random memristors for input-aware dynamic SNN

Wang, B., Zhang, X., Wang, S., Lin, N., Li, Y., Yu, Y., Zhang, Y., Yang, J., Wu, X., He, Y., et al. (2025) · Science Advances · doi:10.1126/sciadv.ads5340 · Cited by 12

Configurable Kernel Map Implementation in Memristor Crossbar for Convolution Neural Network

Kim, G., Youn, S., Park, J., & Kim, H. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202500032 · Cited by 12

E-Synapse Based on Lead-Free Organic Halide Perovskite (CH3NH3)3Sb2Cl9 for Neuromorphic Computing

Ni, Z., Zhu, Y., Ju, S., Xu, Z., Tian, F., Hu, H., Guo, T., & Li, F. (2021) · IEEE Transactions on Electron Devices · doi:10.1109/ted.2021.3095434 · Cited by 12

Predicting Blood Glucose Levels with Organic Neuromorphic Micro‐Networks

Kurt, I., Krauhausen, I., Spolaor, S., & van de Burgt, Y. (2024) · Advanced Science · doi:10.1002/advs.202308261 · Cited by 12

Multilevel Logic Operation and Artificial Synaptic Plasticity Based on Pectin Transient Memristor

Liu, M., Yuan, Q., Li, B., & Sun, Y. (2024) · ACS Applied Electronic Materials · doi:10.1021/acsaelm.3c01611 · Cited by 11

A Low-Power DNN Accelerator Enabled by a Novel Staircase RRAM Array

Veluri, H., Chand, U., Li, Y., Tang, B., & Thean, A. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3118451 · Cited by 11

Physical neural networks with self-learning capabilities

Yu, W., Guo, H., Xiao, J., & Shen, J. (2024) · Science China Physics Mechanics and Astronomy · doi:10.1007/s11433-024-2403-x · Cited by 11

Patterning, morphing, and coding of gel composites by direct ink writing

Sui, Y., Li, C., Feng, S., Ling, Y., Li, C., Wu, X., Shen, J., Song, J. J., Peng, H., & Huang, W. (2021) · Journal of Materials Chemistry A · doi:10.1039/d0ta12275k · Cited by 11

Error-aware probabilistic training for memristive neural networks

Liu, J., Lü, J., Tang, S., Zhou, R., Ma, H., Lyu, B., Tian, Y., Shi, T., & Liu, Q. (2025) · Nature Communications · doi:10.1038/s41467-025-66240-7 · Cited by 11

A 40-nm SONOS Digital CIM Using Simplified LUT Multiplier and Continuous Sample-Hold Sense Amplifier for AI Edge Inference

Hu, H., Wang, X., Wang, Z. X., Zhou, H., Dong, D., Yue, J., Pang, W., Xu, X., & Dou, C. (2023) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2023.3305530 · Cited by 10

Non-spike timing-dependent plasticity learning mechanism for memristive neural networks

Tang, Z., Chen, Y., Wang, Z., Hu, R., & Wu, E. Q. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01985-w · Cited by 10

Application of mathematical morphology operation with memristor-based computation-in-memory architecture for detecting manufacturing defects

Zhou, Y., Gao, B., Zhang, Q., Yao, P., Geng, Y., Li, X., Sun, W. B., Zhao, M., Xi, Y., Tang, J., et al. (2021) · Fundamental Research · doi:10.1016/j.fmre.2021.06.020 · Cited by 10

CODEX: Stochastic Encoding Method to Relax Resistive Crossbar Accelerator Design Requirements

Liu, T., Amirsoleimani, A., Xu, J., Alibart, F., Beilliard, Y., Ecoffey, S., Drouin, D., & Genov, R. (2022) · IEEE Transactions on Circuits & Systems II Express Briefs · doi:10.1109/tcsii.2022.3157789 · Cited by 10

Study of current conduction mechanism and resistive switching stability in the PVdF-HFP-based memristor

Krishnan, K., & Vijayaraghavan, S. (2023) · Journal of Materials Science Materials in Electronics · doi:10.1007/s10854-022-09697-2 · Cited by 10

AI learns how to learn with TCAMs

Huang, P. C., Han, R., & Kang, J. (2019) · Nature Electronics · doi:10.1038/s41928-019-0328-9 · Cited by 10

Lateral Migration‐based Flash‐like Synaptic Device for Hybrid Off‐chip/On‐chip Training

Park, M., Hwang, J., Lee, K. M., Woo, S. Y., Kim, J., Bae, J., & Lee, J. (2024) · Advanced Electronic Materials · doi:10.1002/aelm.202300866 · Cited by 9

Recent Progress in Memristor Array‐Based Neuromorphic Computing for on‐Chip Vector‐Matrix Multiplication

Jang, J., & Gi, S.-G. (2025) · Advanced Materials Technologies · doi:10.1002/admt.202500588 · Cited by 12

Cycle-to-cycle Variation Enabled Energy Efficient Privacy Preserving Technology in ANN

Fu, J., Liao, Z., & Wang, J. (2020) · IEEE International System-on-Chip Conference (SOCC) · doi:10.1109/socc49529.2020.9524794 · Cited by 9

Towards Energy-Efficient Computing Hardware Based on Memristive Nanodevices

Huang, Y., Ravichandran, V., Zhao, W., & Xia, Q. (2023) · IEEE Nanotechnology Magazine · doi:10.1109/mnano.2023.3297106 · Cited by 8

A Hybrid Weight Quantization Strategy for Memristive Neural Networks

Shen, S., Duan, S., & Wang, L. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126778 · Cited by 8

Memristor Based on a Lotus Root/Lactalbumin Bilayer as a Biocompatible Artificial Synapse for Neuromorphic Electronics

Sun, Y., Yuan, Q., Wang, Y., Li, B., & Wen, D. (2023) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.3c05027 · Cited by 8

Gradient Decomposition Methods for Training Neural Networks With Non-ideal Synaptic Devices

Zhao, J., Huang, S., Yousuf, O., Gao, Y., Hoskins, B. D., & Adam, G. C. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.749811 · Cited by 8

A Low-Latency DNN Accelerator Enabled by DFT-Based Convolution Execution Within Crossbar Arrays

Veluri, H., Chand, U., Chen, C.-K., & Thean, A. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3327122 · Cited by 9

A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems

Guo, Y., Zou, X., Hu, Y., Yang, Y., Wang, X., He, Y., Kong, R., Guo, Y., Li, G., Zhang, W., et al. (2021) · Advanced Intelligent Systems · doi:10.1002/aisy.202100054 · Cited by 14

Artificial pain-perceptual nociceptor emulation based on graphene oxide synaptic transistors

Sun, Y., Meng, X., & Qin, G. (2024) · Chemical Engineering Journal · doi:10.1016/j.cej.2024.155571 · Cited by 7

Phosphorylation Enables Nano‐Graphene for Tunable Artificial Synapses

Zhang, Z., Qu, Y., Chen, S., Ke, S., Hao, M., Xiao, Y., Zhang, S., Cheng, Z., Xiao, J., Huang, H., et al. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202416794 · Cited by 7

Progresses and outlook in neuromorphic devices

Wang, Y., Liu, C., Huang, R., & Yang, Y. (2019) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/tb-2019-0739 · Cited by 6

Today’s computing challenges: opportunities for computer hardware design

Bae, W. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.420 · Cited by 7

Ferroelectric Polarized in Transistor Channel Polarity Modulation for Reward-Modulated Spike-Time-Dependent Plasticity Application

Sun, Y., He, N., Yuan, Q., Wang, Y., Dong, Y. H., & Wen, D. (2022) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.2c03007 · Cited by 7

Resistive switching of two-dimensional NiAl-layered double hydroxides and memory logical functions

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Towards Efficient RRAM-based Quantized Neural Networks Hardware: State-of-the-art and Open Issues

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