Application of memristors for efficient neuromorphic computing in tactile sensing

Xintong Liu; Rongrong Bao; Caofeng Pan

doi:10.1088/1674-4926/26050022

J. Semicond. > Just Accepted

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Application of memristors for efficient neuromorphic computing in tactile sensing

Xintong Liu¹, Rongrong Bao^2, and Caofeng Pan²

+ Author Affiliations

Corresponding author: Rongrong Bao, baorongrong@ucas.ac.cn

DOI: 10.1088/1674-4926/26050022CSTR: 32376.14.1674-4926.26050022

Abstract: With the rapid development of the Internet of Things (IoT) and wearable electronics, tactile sensors play an indispensable role in intelligent sensing systems. However, traditional tactile sensing systems follow the von Neumann architecture, where sensors and processing units are physically separated. This leads to frequent data transfer of large raw data volumes, causing high latency and energy consumption. Such bottlenecks cannot meet the requirements of real-time closed-loop control and edge intelligence. Inspired by the highly integrated “perception-storage-computation” mechanism of biological sensory systems, memristor-based neuromorphic computing offers a groundbreaking solution beyond conventional approaches. Memristors combine non-volatile storage with tunable resistance. They enable in-situ emulation of synaptic plasticity, in-memory computing, and brain-inspired processing, thereby holding the potential to significantly improve the energy efficiency and response speed of tactile systems. This review systematically discusses the physical mechanisms of mainstream memristors, highlights recent progress in memristor-based neuromorphic computing for tactile sensing, and outlines key challenges and future directions for neuromorphic tactile perception systems.

Keywords: memristor, flexible tactile sensing, artificial synapse, artificial neuron

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Fig. 1. (Color online) Memristors can mimic human tactile sensing systems and are applicable to artificial neurons and artificial synapses. (a) Human tactile sensing system. (b) Different types of neural networks architectures.These neural networks can be mapped to the intersection matrix of artificial synapses and neurons. Copyright 2019, Wiley. Copyright 2020, Nature Publishing Group.

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Fig. 3. (Color online) LIF neuron model with time encoding mechanism. (a–c) Realization of single-device LIF neuronal functionality in the MgTi₂O₄ system^[45]. Copyright 2026, Wiley. (a) Threshold-driven resistance switching at different magnesium vacancy concentrations; (b) Schematic diagram of the driving mechanism for resistance transition phenomena; (c) Electric field-induced Jahn-Teller polarons jumping from Ti³⁺ sites to Ti⁴⁺ sites, followed by polarization filament formation along the electric field direction, resulting in resistance switching. (d) Spike-element neural systems with reconfigurable properties^[59]. Copyright 2026, Wiley.

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Fig. 4. (Color online) LIF neuron model with spatiotemporal encoding mechanism^[60]. (a) Spatial Summation. (b) AND logic and temporal regulation of spatial summation. (c) XOR logic and spatiotemporal summation. Copyright 2020, Springer Nature.

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Fig. 5. (Color online) (a) Memristive synapses dependent on local learning rules such as spike-timing-dependent plasticity (STDP). Pulse-time-varying plasticity of Ta-S and TT-D devices; Fitting parameters of STDP for Ta-S and TT-D devices; Ratio of STDP fitting parameters for Ta-S and TT-D devices^[65]. Copyright 2026, Royal Society of Chemistry. (b–c) Memristive synapses via backpropagation algorithm. (b) Utilizing PCM resistance drift as a learning mechanism to enhance supervised learning performance^[37]. Comparison of classification accuracy between traditional neural networks and drifting neural networks. Copyright 2021, Springer Nature. (c) Enhancing synaptic plasticity of memristors in supervised learning tasks through interface modification. The conductivity gradients of the three devices under bipolar pulse sequences and the nonlinearity and symmetry of LTP/LTD curves were compared. Copyright 2026, Elsevier.

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Fig. 6. (Color online) (a–c) Exploits the short-term (volatile) plasticity of the device to enhance the performance of deep neural networks in dynamic environments^[74]. Copyright 2024, Springer Nature. (a) The average reward curve under different parameter ranges (Λ). (b) Comparison of synaptic energy consumption, cumulative energy consumption curves for pure GPU implementation versus the memristor-based implementation described in this paper. (c) Total energy histogram. (d–f) Achieving ultra-high energy efficiency and low latency in end-to-end systems through high-precision encoding of volatile devices and high-reliability computation of non-volatile devices^[76]. Copyright 2026, Springer Nature. (d)Conceptual diagram of all-memory-resistive single-pulse HMI. (e) Confusion matrix based on software-based classification. (f) Confusion matrix of hardware-based classification, demonstrating the minimal accuracy degradation. (g–j) Enhancing the learning capability of SNNs in complex spatiotemporal information through fatigue dynamics of volatile devices^[77]. Copyright 2026, Springer Nature. (g) Displaying the evolution of resistance weight (W) for presynaptic spikes. (h) Fatigue STDP can clearly separate correlated and uncorrelated synapses, enhancing the former while inhibiting the latter. The comparison between the accuracy of (i) STDP testing and (j) fatigue STDP learning at input frequencies ranging from 10 Hz to 500 kHz demonstrates that traditional STDP exhibits frequency sensitivity, whereas fatigue STDP remains stable.

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Fig. 7. (Color online) (a) A piezoresistive pressure sensor and silk fibroin-based composite memristor are connected in series to simulate the human tactile system. Copyright 2024, Science China Press. (b) Schematic diagram of neural encoding and feedback artificial CSSN within multimodal sensors, along with a circuit diagram of artificial peak temperature sensing neurons. Copyright 2024, Springer Nature. (c) Dynamic switching of sensing modes for single-material systems. Multimodal receptors and hierarchical assembly of core-shell nanowires (CSNWs). The equivalent circuit representation enables the acquisition of multimodal information through a single-channel architecture. Copyright 2025, Springer Nature.

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Received: 13 May 2026 Revised: 12 June 2026 Online: Accepted Manuscript: 14 July 2026

Article Navigation > Journal of Semiconductors > 2026 > Accepted Manuscript

Xintong Liu, Rongrong Bao, Caofeng Pan. Application of memristors for efficient neuromorphic computing in tactile sensing[J]. Journal of Semiconductors, 2026, In Press. doi: 10.1088/1674-4926/26050022 ****X T Liu, R R Bao, and C F Pan, Application of memristors for efficient neuromorphic computing in tactile sensing[J]. J. Semicond., 2026, accepted doi: 10.1088/1674-4926/26050022

Citation:

Xintong Liu, Rongrong Bao, Caofeng Pan. Application of memristors for efficient neuromorphic computing in tactile sensing[J]. Journal of Semiconductors, 2026, In Press. doi: 10.1088/1674-4926/26050022 ****

X T Liu, R R Bao, and C F Pan, Application of memristors for efficient neuromorphic computing in tactile sensing[J]. J. Semicond., 2026, accepted doi: 10.1088/1674-4926/26050022

Citation:

X T Liu, R R Bao, and C F Pan, Application of memristors for efficient neuromorphic computing in tactile sensing[J]. J. Semicond., 2026, accepted doi: 10.1088/1674-4926/26050022

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Application of memristors for efficient neuromorphic computing in tactile sensing

DOI: 10.1088/1674-4926/26050022

CSTR: 32376.14.1674-4926.26050022

1.
School of Materials Science and Engineering, Beihang University, Beijing 100191, China
2.
Institute of Atomic Manufacturing, Beihang University, Beijing 100191, China

More Information

Xintong Liu received her BE degree from SouthWest JiaoTong University in 2025. She is currently pursuing her MS degree at Beihang University. Her research focuses on flexible sensors, flexible electronics and intelligent sensing systems
Rongrong Bao received her B.S. degree from Tianjin University in 2007 and Ph.D. degree from Technical Institute of Physics and Chemistry, Chinese Academy of Science (CAS) in 2012. She was a postdoc fellow in the same institute. She has been a Professor in the group of Prof. Caofeng Pan at Beihang University. Her main research interests focus on the fields of the production and characterization of organic-inorganic composite nanodevices and flexible pressure sensors
Corresponding author: baorongrong@ucas.ac.cn
Received Date: 2026-05-13
Revised Date: 2026-06-12

Available Online: 2026-07-14

Abstract

Abstract

With the rapid development of the Internet of Things (IoT) and wearable electronics, tactile sensors play an indispensable role in intelligent sensing systems. However, traditional tactile sensing systems follow the von Neumann architecture, where sensors and processing units are physically separated. This leads to frequent data transfer of large raw data volumes, causing high latency and energy consumption. Such bottlenecks cannot meet the requirements of real-time closed-loop control and edge intelligence. Inspired by the highly integrated “perception-storage-computation” mechanism of biological sensory systems, memristor-based neuromorphic computing offers a groundbreaking solution beyond conventional approaches. Memristors combine non-volatile storage with tunable resistance. They enable in-situ emulation of synaptic plasticity, in-memory computing, and brain-inspired processing, thereby holding the potential to significantly improve the energy efficiency and response speed of tactile systems. This review systematically discusses the physical mechanisms of mainstream memristors, highlights recent progress in memristor-based neuromorphic computing for tactile sensing, and outlines key challenges and future directions for neuromorphic tactile perception systems.
- memristor,
- flexible tactile sensing,
- artificial synapse,
- artificial neuron

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