Emerging neuromorphic devices and circuits for bio-inspired electronics

Zifei Gao; Ziye Di; Xiaofan Zhang; Shuiyuan Wang; Peng Zhou

doi:10.1088/1674-4926/26020025

J. Semicond. > Just Accepted

RESEARCH HIGHLIGHTS

Emerging neuromorphic devices and circuits for bio-inspired electronics

Zifei Gao^{1, §}, Ziye Di^{1, §}, Xiaofan Zhang^{1, §}, Shuiyuan Wang^{1, 2,} and Peng Zhou^{1, 2,}

+ Author Affiliations

1. State Key Laboratory of Integrated Chips and Systems (SKLICS), College of Integrated Circuits and Micro-Nano Electronics, Fudan University, Shanghai 200433, China
2. Shaoxin Laboratory, Shaoxing, 312000, Zhejiang, China
^§Zifei Gao, Ziye Di and Xiaofan Zhang contributed equally to this work and should be considered as co-first authors.

Author Bio:

Zifei Gao received her Bachelor's degree from Hefei University of Technology in 2024. She is now a doctoral student in the School of Integrated Circuits and Micro-Nano Electronics Innovation at Fudan University, and her research focuses on neuromorphic devices and computing.

Ziye Di received her Bachelor's degree from Central South University in 2022. She is now a Ph.D. candidate in the School of Integrated Circuits and Micro-Nano Electronics Innovation at Fudan University, and her research focuses on the interdisciplinary application of neuromorphic devices in medicine.

Xiaofan Zhang received her Bachelor's degree from Taiyuan University of Technology in 2023. She is now a master's student in the School of Integrated Circuits and Micro-Nano Electronics Innovation at Fudan University, and her research focuses on neuromorphic computing and brain-computer interfaces.

Shuiyuan Wang received the Ph.D. degree in microelectronics from the state key laboratory of ASIC and system, school of microelectronics, Fudan University. His main research interests are in a variety of novel electronic and optoelectronic devices based on atomic-scale 2D materials, in particular synaptic electronics, neuromorphic devices and computation based on 2D semiconductors, and more recently in bioelectronics.

Peng Zhou is a full professor at the state key laboratory of ASIC and system, school of microelectronics, Fudan University, China. He received his B.S. (2000) and Ph.D. (2005) degrees in Physics from Fudan University, China. Currently, Prof. Zhou is interested in novel high-efficiency and low-power electronic devices based on 2D layered materials, focusing on the application of 2D materials in memory, including 2D ultrafast flash memory, floating gate memory, synaptic and neuromorphic electronics.

Corresponding author: Shuiyuan Wang, sy_wang@fudan.edu.cn; Peng Zhou, pengzhou@fudan.edu.cn

DOI: 10.1088/1674-4926/26020025CSTR: 32376.14.1674-4926.26020025

References

[1]	Indiveri G, Liu S C. Memory and information processing in neuromorphic systems. Proc IEEE, 2015, 103(8): 1379 doi: 10.1109/JPROC.2015.2444094
[2]	Lan G T, Lian H, Qin Z T, et al. Organic photonic synapses for application of bioinspired neuromorphic visual perception and beyond. Adv Funct Mater, 2025: e22513
[3]	Liu X, Dai S L, Jin Y Y, et al. Near-infrared organic photoelectrochemical synaptic transistors by wafer-scale photolithography for neuromorphic visual system. Nat Commun, 2026, 17: 197 doi: 10.1038/s41467-025-66891-6
[4]	Wang W C, Jiang Y W, Zhong D L, et al. Neuromorphic sensorimotor loop embodied by monolithically integrated, low-voltage, soft e-skin. Science, 2023, 380(6646): 735 doi: 10.1126/science.ade0086
[5]	Bao R, Wang S Y, Liu X X, et al. Neuromorphic electro-stimulation based on atomically thin semiconductor for damage-free inflammation inhibition. Nat Commun, 2024, 15: 1327 doi: 10.1038/s41467-024-45590-8
[6]	Gu L L, Poddar S, Lin Y J, et al. A biomimetic eye with a hemispherical perovskite nanowire array retina. Nature, 2020, 581(7808): 278 doi: 10.1038/s41586-020-2285-x
[7]	Chen J W, Zhou Z, Kim B J, et al. Optoelectronic graded neurons for bioinspired in-sensor motion perception. Nat Nanotechnol, 2023, 18(8): 882 doi: 10.1038/s41565-023-01379-2
[8]	Wang Y, Gou S F, Dong X Q, et al. A biologically inspired artificial neuron with intrinsic plasticity based on monolayer molybdenum disulfide. Nat Electron, 2025, 8: 680 doi: 10.1038/s41928-025-01433-y
[9]	Zhang N, Wang Y, Yan Y J, et al. Floating-gate synaptic transistors for energy-efficient neuromorphic computing. Adv Mater, 2026, 38(8): e15605
[10]	Wang S Y, Jiang C Y, Yu Y Y, et al. Tellurium nanowire retinal nanoprosthesis improves vision in models of blindness. Science, 2025, 388: eadu2987 doi: 10.1126/science.adu2987
[11]	Zhou Y, Fu J W, Chen Z R, et al. Computational event-driven vision sensors for in-sensor spiking neural networks. Nat Electron, 2023, 6: 870 doi: 10.1038/s41928-023-01055-2
[12]	Li S J, Wu T, Xu J L, et al. Biomimetic multimodal tactile sensing enables human-like robotic perception. Nat Sens, 2026, 1: 52 doi: 10.1038/s44460-025-00006-y
[13]	Chang J K, Maltby T, Moineddini A, et al. Piezoelectric nanofiber–based intelligent hearing system. Sci Adv, 2025, 11: eadl2741 doi: 10.1126/sciadv.adl2741
[14]	Zhong B W, Qin X K, Xu H, et al. Monolithic cell-on-memristor architecture enables wafer-scale integration of oscillatory chemoreceptors for bio-realistic gustatory chips. Nat Mater, 2026, 25: 275 doi: 10.1038/s41563-025-02436-y
[15]	Merolla P A, Arthur J V, Alvarez-Icaza R, et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 2014, 345: 668 doi: 10.1126/science.1254642
[16]	Yao P, Wu H Q, Gao B, et al. Fully hardware-implemented memristor convolutional neural network. Nature, 2020, 577: 641 doi: 10.1038/s41586-020-1942-4
[17]	Zhang W B, Yao P, Gao B, et al. Edge learning using a fully integrated neuro-inspired memristor chip. Science, 2023, 381: 1205 doi: 10.1126/science.ade3483
[18]	Liu Z W, Mei J, Tang J S, et al. A memristor-based adaptive neuromorphic decoder for brain–computer interfaces. Nat Electron, 2025, 8: 362 doi: 10.1038/s41928-025-01340-2
[19]	Yu R H, Wang Z, Liu Q, et al. A full-stack memristor-based computation-in-memory system with software-hardware co-development. Nat Commun, 2025, 16: 2123 doi: 10.1038/s41467-025-57183-0
[20]	Ning H K, Wen H D, Meng Y, et al. An index-free sparse neural network using two-dimensional semiconductor ferroelectric field-effect transistors. Nat Electron, 2025, 8: 222 doi: 10.1038/s41928-024-01328-4

Fig. 1. (Color online) Biological synapse and representative neuromorphic devices with distinct operating mechanisms. (a) Near-infrared organic photoelectrochemical transistor (OPECT) mechanism: photo-induced charge separation and ion migration modulate channel conductance^[3]. (b) High-k trilayer dielectric structure for low-voltage flexible organic transistors^[4]. (c) Monolayer MoS₂ floating-gate memory architecture enabling non-volatile conductance modulation for synaptic applications^[9]. (d) Wafer-scale integrated artificial neuron module based on MoS₂ with capacitive coupling for simulating neuronal intrinsic plasticity.

DownLoad: Full-Size Img PowerPoint

Fig. 2. (Color online) Bio-inspired sensory systems and neuromorphic computing architectures. (a) Tellurium nanowire-based retinal nanoprosthesis with broadband spectral response (UV to NIR) for vision restoration^[10]. (b) Fully flexible integrated electronic skin system with sensory feedback enabling a complete ‘perception-drive’ closed loop^[4]. (c) TrueNorth neuromorphic chip architecture with 64×64 core array containing 1 million neurons and 256 million synapses^[15]. (d) STELLAR hybrid memristor architecture with 2T2R and 1T1R arrays for edge learning applications^[17].

DownLoad: Full-Size Img PowerPoint

[1]	Indiveri G, Liu S C. Memory and information processing in neuromorphic systems. Proc IEEE, 2015, 103(8): 1379 doi: 10.1109/JPROC.2015.2444094
[2]	Lan G T, Lian H, Qin Z T, et al. Organic photonic synapses for application of bioinspired neuromorphic visual perception and beyond. Adv Funct Mater, 2025: e22513
[3]	Liu X, Dai S L, Jin Y Y, et al. Near-infrared organic photoelectrochemical synaptic transistors by wafer-scale photolithography for neuromorphic visual system. Nat Commun, 2026, 17: 197 doi: 10.1038/s41467-025-66891-6
[4]	Wang W C, Jiang Y W, Zhong D L, et al. Neuromorphic sensorimotor loop embodied by monolithically integrated, low-voltage, soft e-skin. Science, 2023, 380(6646): 735 doi: 10.1126/science.ade0086
[5]	Bao R, Wang S Y, Liu X X, et al. Neuromorphic electro-stimulation based on atomically thin semiconductor for damage-free inflammation inhibition. Nat Commun, 2024, 15: 1327 doi: 10.1038/s41467-024-45590-8
[6]	Gu L L, Poddar S, Lin Y J, et al. A biomimetic eye with a hemispherical perovskite nanowire array retina. Nature, 2020, 581(7808): 278 doi: 10.1038/s41586-020-2285-x
[7]	Chen J W, Zhou Z, Kim B J, et al. Optoelectronic graded neurons for bioinspired in-sensor motion perception. Nat Nanotechnol, 2023, 18(8): 882 doi: 10.1038/s41565-023-01379-2
[8]	Wang Y, Gou S F, Dong X Q, et al. A biologically inspired artificial neuron with intrinsic plasticity based on monolayer molybdenum disulfide. Nat Electron, 2025, 8: 680 doi: 10.1038/s41928-025-01433-y
[9]	Zhang N, Wang Y, Yan Y J, et al. Floating-gate synaptic transistors for energy-efficient neuromorphic computing. Adv Mater, 2026, 38(8): e15605
[10]	Wang S Y, Jiang C Y, Yu Y Y, et al. Tellurium nanowire retinal nanoprosthesis improves vision in models of blindness. Science, 2025, 388: eadu2987 doi: 10.1126/science.adu2987
[11]	Zhou Y, Fu J W, Chen Z R, et al. Computational event-driven vision sensors for in-sensor spiking neural networks. Nat Electron, 2023, 6: 870 doi: 10.1038/s41928-023-01055-2
[12]	Li S J, Wu T, Xu J L, et al. Biomimetic multimodal tactile sensing enables human-like robotic perception. Nat Sens, 2026, 1: 52 doi: 10.1038/s44460-025-00006-y
[13]	Chang J K, Maltby T, Moineddini A, et al. Piezoelectric nanofiber–based intelligent hearing system. Sci Adv, 2025, 11: eadl2741 doi: 10.1126/sciadv.adl2741
[14]	Zhong B W, Qin X K, Xu H, et al. Monolithic cell-on-memristor architecture enables wafer-scale integration of oscillatory chemoreceptors for bio-realistic gustatory chips. Nat Mater, 2026, 25: 275 doi: 10.1038/s41563-025-02436-y
[15]	Merolla P A, Arthur J V, Alvarez-Icaza R, et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science, 2014, 345: 668 doi: 10.1126/science.1254642
[16]	Yao P, Wu H Q, Gao B, et al. Fully hardware-implemented memristor convolutional neural network. Nature, 2020, 577: 641 doi: 10.1038/s41586-020-1942-4
[17]	Zhang W B, Yao P, Gao B, et al. Edge learning using a fully integrated neuro-inspired memristor chip. Science, 2023, 381: 1205 doi: 10.1126/science.ade3483
[18]	Liu Z W, Mei J, Tang J S, et al. A memristor-based adaptive neuromorphic decoder for brain–computer interfaces. Nat Electron, 2025, 8: 362 doi: 10.1038/s41928-025-01340-2
[19]	Yu R H, Wang Z, Liu Q, et al. A full-stack memristor-based computation-in-memory system with software-hardware co-development. Nat Commun, 2025, 16: 2123 doi: 10.1038/s41467-025-57183-0
[20]	Ning H K, Wen H D, Meng Y, et al. An index-free sparse neural network using two-dimensional semiconductor ferroelectric field-effect transistors. Nat Electron, 2025, 8: 222 doi: 10.1038/s41928-024-01328-4

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Received: 09 February 2026 Revised: 02 March 2026 Online: Accepted Manuscript: 01 April 2026

Article Navigation > Journal of Semiconductors > 2026 > Accepted Manuscript

Zifei Gao, Ziye Di, Xiaofan Zhang, Shuiyuan Wang, Peng Zhou. Emerging neuromorphic devices and circuits for bio-inspired electronics[J]. Journal of Semiconductors, 2026, In Press. doi: 10.1088/1674-4926/26020025 ****Z F Gao, Z Y Di, X F Zhang, S Y Wang, and P Zhou, Emerging neuromorphic devices and circuits for bio-inspired electronics[J]. J. Semicond., 2026, accepted doi: 10.1088/1674-4926/26020025

Citation:

Zifei Gao, Ziye Di, Xiaofan Zhang, Shuiyuan Wang, Peng Zhou. Emerging neuromorphic devices and circuits for bio-inspired electronics[J]. Journal of Semiconductors, 2026, In Press. doi: 10.1088/1674-4926/26020025 ****

Z F Gao, Z Y Di, X F Zhang, S Y Wang, and P Zhou, Emerging neuromorphic devices and circuits for bio-inspired electronics[J]. J. Semicond., 2026, accepted doi: 10.1088/1674-4926/26020025

Citation:

Z F Gao, Z Y Di, X F Zhang, S Y Wang, and P Zhou, Emerging neuromorphic devices and circuits for bio-inspired electronics[J]. J. Semicond., 2026, accepted doi: 10.1088/1674-4926/26020025

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Emerging neuromorphic devices and circuits for bio-inspired electronics

DOI: 10.1088/1674-4926/26020025

CSTR: 32376.14.1674-4926.26020025

Zifei Gao^{1, §},
Ziye Di^{1, §},
Xiaofan Zhang^{1, §},
Shuiyuan Wang^{1, 2,},
Peng Zhou^{1, 2,}

1.
State Key Laboratory of Integrated Chips and Systems (SKLICS), College of Integrated Circuits and Micro-Nano Electronics, Fudan University, Shanghai 200433, China
2.
Shaoxin Laboratory, Shaoxing, 312000, Zhejiang, China

More Information

Zifei Gao received her Bachelor's degree from Hefei University of Technology in 2024. She is now a doctoral student in the School of Integrated Circuits and Micro-Nano Electronics Innovation at Fudan University, and her research focuses on neuromorphic devices and computing
Ziye Di received her Bachelor's degree from Central South University in 2022. She is now a Ph.D. candidate in the School of Integrated Circuits and Micro-Nano Electronics Innovation at Fudan University, and her research focuses on the interdisciplinary application of neuromorphic devices in medicine
Xiaofan Zhang received her Bachelor's degree from Taiyuan University of Technology in 2023. She is now a master's student in the School of Integrated Circuits and Micro-Nano Electronics Innovation at Fudan University, and her research focuses on neuromorphic computing and brain-computer interfaces
Shuiyuan Wang received the Ph.D. degree in microelectronics from the state key laboratory of ASIC and system, school of microelectronics, Fudan University. His main research interests are in a variety of novel electronic and optoelectronic devices based on atomic-scale 2D materials, in particular synaptic electronics, neuromorphic devices and computation based on 2D semiconductors, and more recently in bioelectronics
Peng Zhou is a full professor at the state key laboratory of ASIC and system, school of microelectronics, Fudan University, China. He received his B.S. (2000) and Ph.D. (2005) degrees in Physics from Fudan University, China. Currently, Prof. Zhou is interested in novel high-efficiency and low-power electronic devices based on 2D layered materials, focusing on the application of 2D materials in memory, including 2D ultrafast flash memory, floating gate memory, synaptic and neuromorphic electronics
Corresponding author: sy_wang@fudan.edu.cn; pengzhou@fudan.edu.cn
Received Date: 2026-02-09
Revised Date: 2026-03-02

Available Online: 2026-04-01

Abstract

^§Zifei Gao, Ziye Di and Xiaofan Zhang contributed equally to this work and should be considered as co-first authors.

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