Recent progress on integrated neuromorphic chips

Xiushuo Gu; Lifeng Bian; Linrui Cheng; Chaoyun Song; Yibin Wang; Jianya Zhang; Yukun Zhao

doi:10.59717/j.xinn-inform.2026.100027

The Innovation Informatics > 2026 Vol. 2 > No. 1 > 100027

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Recent progress on integrated neuromorphic chips

1.
State Key Laboratory of Integrated Chips and Systems, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China
2.
School of Nano-Tech and Nano-Bionics, University of Science and Technology of China, Hefei 230026, China
3.
Key Laboratory of Semiconductor Display Materials and Chips, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), CAS, Suzhou 215123, China
4.
Department of Engineering, King’s College London, London WC2R 2LS, United Kingdom
5.
Key Laboratory of Intelligent Optoelectronic Devices and Chips of Jiangsu Higher Education Institutions, School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou 215009, China
6.
Guangdong Institute of Semiconductor Micro-Nano Manufacturing Technology, Foshan 528225, China
7.
These authors contributed equally

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Corresponding authors: chaoyun.song@kcl.ac.uk (C.S.); jyzhang2022@usts.edu.cn (J.Z.); ykzhao2017@sinano.ac.cn (Y.Z.)
Received Date: October 16, 2025

Accepted Date: January 17, 2026

Published Online: January 30, 2026

The Innovation Informatics 2(1), https://doi.org/10.59717/j.xinn-inform.2026.100027 | Cite this article

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PUBLIC SUMMARY

PUBLIC SUMMARY
1. This review overviews integrated neuromorphic chips based on artificial synaptic devices.
  
  It summarizes recent advances in synaptic materials, device structures, and chip integration.
  
  Key progresses in large-scale integration, low power operation, and synaptic plasticity are highlighted.
  
  Future opportunities of neuromorphic chips in AI, sensing, and intelligent robotics are discussed.

ABSTRACT

ABSTRACT

In the post-Moore era, advancing integrated circuits necessitates the development of artificial synaptic devices capable of replicating the functions of human brain with neuromorphic computing, which consists of numerous biological synapses. The neuromorphic chips based on integrated synaptic devices are the promising candidates due to their unique properties, which enable the simulation of human brain and biological synapse plasticity. In recent years, signiﬁcant progress has been made in the development of optoelectronic synaptic devices and integrated neuromorphic chips. This review summarizes the recent advancements in the integrated synaptic devices, focusing on the structures, integrated technologies and their applications of neuromorphic chips. It discusses the two-terminal and three-terminal configurations of synaptic devices, emphasizing their advantages in simulating biological synapse functions. These advantages include a simple structure, low power consumption, high stability, a wide switching ratio and fast programming speed. Furthermore, the discussion covers the key applications of synaptic devices and integrated neuromorphic chips, such as image recognition, brain-like computing, tactile and vision sensors. For the synaptic-device-based neural networks in image recognition, they have great potential in tactile technology and visual bionics. Other potential applications have also been reviewed and discussed, including intelligent robots, electric skin, human-computer interaction and brain-computer interface, etc. Hence, neuromorphic chips have great promising application prospects, which can significantly promote the development of artificial intelligence and human civilization.
- Neuromorphic chip /
- synaptic device /
- integrated technology /
- memristor synapse /
- intelligent robot.

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REFERENCES

[1]	Huang W., Jiang S., Lin Z., et al. (2025). All-optical modulation self-powered optoelectronic synaptic devices with monochromatic ultraviolet for inhibitory/excitatory synaptic behaviors. Appl. Phys. Rev. 12:031418. DOI:10.1063/5.0259134 View in Article CrossRef Google Scholar
[2]	Li C., Wang J., Li D., et al. (2022). An oxide-based heterojunction optoelectronic synaptic device with wideband and rapid response performance. Chin. J. Met. Sci. Technol. 123:159−167. DOI:10.1016/j.jmst.2021.11.082 View in Article CrossRef Google Scholar
[3]	Li Q., Wang T., Hu X., et al. (2022). Organic optoelectronic synaptic devices for energy-efficient neuromorphic computing. IEEE Electron Device Lett. 43:1089−1092. DOI:10.1109/LED.2022.3180346 View in Article CrossRef Google Scholar
[4]	Kuzum D., Yu S. and Philip Wong H.S. (2013). Synaptic electronics: Materials, devices and applications. Nanotechnology 24:382001. DOI:10.1088/0957-4484/24/38/382001 View in Article CrossRef Google Scholar
[5]	Indiveri G. and Liu S.C. (2015). Memory and information processing in neuromorphic systems. Proc. IEEE 103:1379−1397. DOI:10.1109/JPROC.2015.2444094 View in Article CrossRef Google Scholar
[6]	Yang S., Li Y., Kang F., et al. (2025). Recent progress in organic cocrystal-based superlattices and their optoelectronic applications. Adv. Funct. Mater. 35:2504976. DOI:10.1002/adfm.202504976 View in Article CrossRef Google Scholar
[7]	Zhang Y., Tan C.M.J., Toepfer C.N., et al. (2024). Microscale droplet assembly enables biocompatible multifunctional modular iontronics. Science 386:1024−1030. DOI:10.1126/science.adr0428 View in Article CrossRef Google Scholar
[8]	Xiong T., Li W., Yu P., et al. (2023). Fluidic memristor: Bringing chemistry to neuromorphic devices. The Innovation 4:100435. DOI:10.1016/j.xinn.2023.100435 View in Article CrossRef Google Scholar
[9]	Mo Y., Luo B., Dong H., et al. (2022). Light-stimulated artificial synapses based on Si-doped GaN thin films. J. Mater. Chem. C 10:13099−13106. DOI:10.1039/D2TC02168D View in Article CrossRef Google Scholar
[10]	Zhang Z., Wang S., Liu C., et al. (2022). All-in-one two-dimensional retinomorphic hardware device for motion detection and recognition. Nat. Nanotechnol. 17:27−32. DOI:10.1038/s41565-021-01003-1 View in Article CrossRef Google Scholar
[11]	Wan W., Kubendran R., Schaefer C., et al. (2022). A compute-in-memory chip based on resistive random-access memory. Nature 608:504−512. DOI:10.1038/s41586-022-04992-8 View in Article CrossRef Google Scholar
[12]	Yao P., Wu H., Gao B., et al. (2020). Fully hardware-implemented memristor convolutional neural network. Nature 577:641−646. DOI:10.1038/s41586-020-1942-4 View in Article CrossRef Google Scholar
[13]	Zhang J., Li J., Yang L., et al. (2025). Electric-stimulated controllable synaptic GaN nanodevice for neuromorphic computing. Chip 4:100149. DOI:10.1016/j.chip.2025.100149 View in Article CrossRef Google Scholar
[14]	Xu Z., Chen S., Pan Y., et al. (2025). Preparation of organic N-fused perylenediimide-MXene hybrid material for robust versatile memristive device. Int. J. Extreme Manuf. 7:025507. DOI:10.1088/2631-7990/ad9bff View in Article CrossRef Google Scholar
[15]	Zhu K., Pazos S., Aguirre F., et al. (2023). Hybrid 2D–CMOS microchips for memristive applications. Nature 618:57−62. DOI:10.1038/s41586-023-05973-1 View in Article CrossRef Google Scholar
[16]	Rao M., Tang H., Wu J., et al. (2023). Thousands of conductance levels in memristors integrated on CMOS. Nature 615:823−829. DOI:10.1038/s41586-023-05759-5 View in Article CrossRef Google Scholar
[17]	Zhang H., Jiang B., Cheng C., et al. (2023). A self-rectifying synaptic memristor array with ultrahigh weight potentiation linearity for a self-organizing-map neural network. Nano Lett. 23:3107−3115. DOI:10.1021/acs.nanolett.2c03624 View in Article CrossRef Google Scholar
[18]	Huang J., Yang S., Tang X., et al. (2023). Flexible, transparent, and wafer-scale artificial synapse array based on TiO/Ti₃C₂t film for neuromorphic computing. Adv. Mater. 35:2303737. DOI:10.1002/adma.202303737 View in Article CrossRef Google Scholar
[19]	Le V.-Q., Do T.-H., Retamal J.R.D., et al. (2019). Van der Waals heteroepitaxial AZO/NiO/AZO/muscovite (ANA/muscovite) transparent flexible memristor. Nano Energy 56:322−329. DOI:10.1016/j.nanoen.2018.10.042 View in Article CrossRef Google Scholar
[20]	Pei J., Deng L., Song S., et al. (2019). Towards artificial general intelligence with hybrid Tianjic chip architecture. Nature 572:106−111. DOI:10.1038/s41586-019-1424-8 View in Article CrossRef Google Scholar
[21]	Chen H., Xue X., Liu C., et al. (2021). Logic gates based on neuristors made from two-dimensional materials. Nat. Electron. 4:399−404. DOI:10.1038/s41928-021-00591-z View in Article CrossRef Google Scholar
[22]	Kwon J.Y., Kim J.E., Kim J.S., et al. (2024). Artificial sensory system based on memristive devices. Exploration 4:20220162. DOI:10.1002/EXP.20220162 View in Article CrossRef Google Scholar
[23]	Kim S.J., Im I.H., Baek J.H., et al. (2025). Linearly programmable two-dimensional halide perovskite memristor arrays for neuromorphic computing. Nat. Nanotechnol. 20:83−92. DOI:10.1038/s41565-024-01790-3 View in Article CrossRef Google Scholar
[24]	Jo Y., Woo D.Y., Noh G., et al. (2024). Hardware implementation of network connectivity relationships using 2D hBn-based artificial neuron and synaptic devices. Adv. Funct. Mater. 34:2309058. DOI:10.1002/adfm.202309058 View in Article CrossRef Google Scholar
[25]	Sun B., Guo T., Zhou G., et al. (2021). Synaptic devices based neuromorphic computing applications in artificial intelligence. Mater. Today Phys. 18:100393. DOI:10.1016/j.mtphys.2021.100393 View in Article CrossRef Google Scholar
[26]	Merces L., Ferro L.M.M., Nawaz A., et al. (2024). Advanced neuromorphic applications enabled by synaptic ion-gating vertical transistors. Adv. Sci. 11:2305611. DOI:10.1002/advs.202305611 View in Article CrossRef Google Scholar
[27]	Sehgal A., Dhull S., Roy S., et al. (2024). Advancements in memory technologies for artificial synapses. J. Mater. Chem. C 12:5274−5298. DOI:10.1039/D3TC04131J View in Article CrossRef Google Scholar
[28]	Seok H., Lee D., Son S., et al. (2024). Beyond von neumann architecture: Brain-inspired artificial neuromorphic devices and integrated computing. Adv. Electron. Mater. 10:2300839. DOI:10.1002/aelm.202300839 View in Article CrossRef Google Scholar
[29]	Tian B., Xie Z., Chen L., et al. (2023). Ultralow-power in-memory computing based on ferroelectric memcapacitor network. Exploration 3:20220126. DOI:10.1002/EXP.20220126 View in Article CrossRef Google Scholar
[30]	Li J., Shen Z., Cao Y., et al. (2022). Artificial synapses enabled neuromorphic computing: From blueprints to reality. Nano Energy 103:107744. DOI:10.1016/j.nanoen.2022.107744 View in Article CrossRef Google Scholar
[31]	Song S., Kim M., Yoo G., et al. (2021). Solution-processed oxide semiconductor-based artificial optoelectronic synapse array for spatiotemporal synaptic integration. J. Alloys Compd. 857:158027. DOI:10.1016/j.jallcom.2020.158027 View in Article CrossRef Google Scholar
[32]	Chen Y., Shi Z., Lv B., et al. (2024). In situ growth of wafer-scale patterned graphene and fabrication of optoelectronic artificial synaptic device array based on Graphene/n-AlGaN heterojunction for visual learning. Small 20:2401150. DOI:10.1002/smll.202401150 View in Article CrossRef Google Scholar
[33]	Xiang D., Liu T., Zhang X., et al. (2021). Dielectric engineered two-dimensional neuromorphic transistors. Nano Lett. 21:3557−3565. DOI:10.1021/acs.nanolett.1c00492 View in Article CrossRef Google Scholar
[34]	Huang M., Ali W., Yang L., et al. (2023). Multifunctional optoelectronic synapses based on arrayed MoS₂ monolayers emulating human association memory. Adv. Sci. 10:2300120. DOI:10.1002/advs.202300120 View in Article CrossRef Google Scholar
[35]	Naqi M., Kim T., Cho Y., et al. (2024). Large scale integrated IGZO crossbar memristor array based artificial neural architecture for scalable in-memory computing. Mater. Today Nano 25:100441. DOI:10.1016/j.mtnano.2023.100441 View in Article CrossRef Google Scholar
[36]	Lee K., Jang S., Kim K.L., et al. (2020). Artificially intelligent tactile ferroelectric skin. Adv. Sci. 7:2001662. DOI:10.1002/advs.202001662 View in Article CrossRef Google Scholar
[37]	Zhang T., Fan C., Hu L., et al. (2024). A reconfigurable all-optical-controlled synaptic device for neuromorphic computing applications. ACS Nano 18:16236−16247. DOI:10.1021/acsnano.4c02278 View in Article CrossRef Google Scholar
[38]	Dias C., Castro D., Aroso M., et al. (2022). Memristor-based neuromodulation device for real-time monitoring and adaptive control of neuronal populations. ACS Appl. Electron. Mater. 4:2380−2387. DOI:10.1021/acsaelm.2c00198 View in Article CrossRef Google Scholar
[39]	Xia Z., Sun X., Wang Z., et al. (2025). Low-power memristor for neuromorphic computing: From materials to applications. Nano-Micro Lett. 17:217. DOI:10.1007/s40820-025-01705-4 View in Article CrossRef Google Scholar
[40]	Abdelouahab M.-S., Lozi R. and Chua L. (2014). Memfractance: A mathematical paradigm for circuit elements with memory. Int. J. Bifurcation Chaos 24:1430023. DOI:10.1142/S0218127414300237 View in Article CrossRef Google Scholar
[41]	Neves G., Cooke S.F. and Bliss T.V.P. (2008). Synaptic plasticity, memory and the hippocampus: A neural network approach to causality. Nat. Rev. Neurosci. 9:65−75. DOI:10.1038/nrn2303 View in Article CrossRef Google Scholar
[42]	Xiao Z. and Huang J. (2016). Energy-efficient hybrid perovskite memristors and synaptic devices. Adv. Electron. Mater. 2:1600100. DOI:10.1002/aelm.201600100 View in Article CrossRef Google Scholar
[43]	Campbell A.P. and Smrcka A.V. (2018). Targeting G protein-coupled receptor signalling by blocking G proteins. Nat. Rev. Drug Discovery 17:789−803. DOI:10.1038/nrd.2018.135 View in Article CrossRef Google Scholar
[44]	Li C.-Y., Lu J.-T., Wu C.-P., et al. (2004). Bidirectional modification of presynaptic neuronal excitability accompanying spike timing-dependent synaptic plasticity. Neuron 41:257−268. DOI:10.1016/S0896-6273(03)00847-X View in Article CrossRef Google Scholar
[45]	de la Rica R. and Matsui H. (2010). Applications of peptide and protein-based materials in bionanotechnology. Chem. Soc. Rev. 39:3499−3509. DOI:10.1039/B917574C View in Article CrossRef Google Scholar
[46]	Palaferri D., Todorov Y., Bigioli A., et al. (2018). Room-temperature nine-µm-wavelength photodetectors and GHz-frequency heterodyne receivers. Nature 556:85−88. DOI:10.1038/nature25790 View in Article CrossRef Google Scholar
[47]	Ren Y., Bu X., Wang M., et al. (2022). Synaptic plasticity in self-powered artificial striate cortex for binocular orientation selectivity. Nat. Commun. 13:5585. DOI:10.1038/s41467-022-33393-8 View in Article CrossRef Google Scholar
[48]	Wu J., Zhang L., Chang W., et al. (2025). A biomimetic ionic hydrogel synapse for self-powered tactile-visual fusion perception. Adv. Funct. Mater. 35:2500048. DOI:10.1002/adfm.202500048 View in Article CrossRef Google Scholar
[49]	Zhang J., Xu K., Lu L., et al. (2025). Ferroelectric/antiferroelectric HfZrO_x artificial synapses/neurons for convolutional neural network–spiking neural network neuromorphic computing. Nano Lett. 25:13739−13747. DOI:10.1021/acs.nanolett.5c02889 View in Article CrossRef Google Scholar
[50]	Mishra A.K., Pathak A. and Pandey S.K. (2025). Exploring multilevel properties of GeTe-based phase change memory devices for programmable synaptic activity. ACS Appl. Electron. Mater. 7:714−720. DOI:10.1021/acsaelm.4c01802 View in Article CrossRef Google Scholar
[51]	Han Z., Xing Y., Lin Y., et al. (2025). Artificial synapse with high weight-updating performance based on charge-trapping mechanism. ACS Appl. Mater. Interfaces 17:18645−18654. DOI:10.1021/acsami.5c00738 View in Article CrossRef Google Scholar
[52]	Liu L., Dananjaya P.A., Chee M.Y., et al. (2023). Proton-assisted redox-based three-terminal memristor for synaptic device applications. ACS Appl. Mater. Interfaces 15:29287−29296. DOI:10.1021/acsami.3c03974 View in Article CrossRef Google Scholar
[53]	Boyn S., Grollier J., Lecerf G., et al. (2017). Learning through ferroelectric domain dynamics in solid-state synapses. Nat. Commun. 8:14736. DOI:10.1038/ncomms14736 View in Article CrossRef Google Scholar
[54]	Nayak A., Ohno T., Tsuruoka T., et al. (2012). Controlling the synaptic plasticity of a Cu₂S gap-type atomic switch. Adv. Funct. Mater. 22:3606−3613. DOI:10.1002/adfm.201200640 View in Article CrossRef Google Scholar
[55]	Zhong Y., Li Y., Xu L., et al. (2015). Simple square pulses for implementing spike-timing-dependent plasticity in phase-change memory. Phys. Status Solidi RRL 9:414−419. DOI:10.1002/pssr.201510150 View in Article CrossRef Google Scholar
[56]	Lee M., Lee W., Choi S., et al. (2017). Brain-inspired photonic neuromorphic devices using photodynamic amorphous oxide semiconductors and their persistent photoconductivity. Adv. Mater. 29:1700951. DOI:10.1002/adma.201700951 View in Article CrossRef Google Scholar
[57]	Tan H., Ni Z., Peng W., et al. (2018). Broadband optoelectronic synaptic devices based on silicon nanocrystals for neuromorphic computing. Nano Energy 52:422−430. DOI:10.1016/j.nanoen.2018.08.018 View in Article CrossRef Google Scholar
[58]	Zhou F., Zhou Z., Chen J., et al. (2019). Optoelectronic resistive random access memory for neuromorphic vision sensors. Nat. Nanotechnol. 14:776−782. DOI:10.1038/s41565-019-0501-3 View in Article CrossRef Google Scholar
[59]	Wang Y., Lv Z., Chen J., et al. (2018). Photonic synapses based on inorganic perovskite quantum dots for neuromorphic computing. Adv. Mater. 30:1802883. DOI:10.1002/adma.201802883 View in Article CrossRef Google Scholar
[60]	Ham S., Choi S., Cho H., et al. (2019). Photonic organolead halide perovskite artificial synapse capable of accelerated learning at low power inspired by dopamine-facilitated synaptic activity. Adv. Funct. Mater. 29:1806646. DOI:10.1002/adfm.201806646 View in Article CrossRef Google Scholar
[61]	Park T.J., Deng S., Manna S., et al. (2023). Complex oxides for brain-inspired computing: A review. Adv. Mater. 35:2203352. DOI:10.1002/adma.202203352 View in Article CrossRef Google Scholar
[62]	Wang X., Zong Y., Liu D., et al. (2023). Advanced optoelectronic devices for neuromorphic analog based on low-dimensional semiconductors. Adv. Funct. Mater. 33:2213894. DOI:10.1002/adfm.202213894 View in Article CrossRef Google Scholar
[63]	Huang H., Liang X., Wang Y., et al. (2025). Fully integrated multi-mode optoelectronic memristor array for diversified in-sensor computing. Nat. Nanotechnol. 20:93−103. DOI:10.1038/s41565-024-01794-z View in Article CrossRef Google Scholar
[64]	Zhang W., Gao B., Tang J., et al. (2020). Neuro-inspired computing chips. Nat. Electron. 3:371−382. DOI:10.1038/s41928-020-0435-7 View in Article CrossRef Google Scholar
[65]	Lu X.F., Zhang Y., Wang N., et al. (2021). Exploring low power and ultrafast memristor on p-type van der waals SnS. Nano Lett. 21:8800−8807. DOI:10.1021/acs.nanolett.1c03169 View in Article CrossRef Google Scholar
[66]	Liu F., Peng Y., Liu Y., et al. (2021). Amorphous ZrO₂ tunnel junction memristor with a tunneling electroresistance ratio above 400. IEEE Electron Device Lett. 42:696−699. DOI:10.1109/LED.2021.3069837 View in Article CrossRef Google Scholar
[67]	Zhang X., Wu C., Lv Y., et al. (2022). High-performance flexible polymer memristor based on stable filamentary switching. Nano Lett. 22:7246−7253. DOI:10.1021/acs.nanolett.2c02765 View in Article CrossRef Google Scholar
[68]	Li B., Xia F., Du B., et al. (2024). 2D halide perovskites for high-performance resistive switching memory and artificial synapse applications. Adv. Sci. 11:2310263. DOI:10.1002/advs.202310263 View in Article CrossRef Google Scholar
[69]	Ling Y., Li J., Luo T., et al. (2023). MoS₂-based memristor: Robust resistive switching behavior and reliable biological synapse emulation. Nanomaterials 13:3117. DOI:10.3390/nano13243117 View in Article CrossRef Google Scholar
[70]	Wu Y., Huang H., Xu C., et al. (2023). The FAPbI₃ perovskite memristor with a PMMA passivation layer as an artificial synapse. Appl. Phys. A 129:364. DOI:10.1007/s00339-023-06632-y View in Article CrossRef Google Scholar
[71]	Song H., Hu S., Liu Y., et al. (2024). Linearity improvement of TiO-based flexible memristor synapses even under bending. Phys. Status Solidi A 221:2300827. DOI:10.1002/pssa.202300827 View in Article CrossRef Google Scholar
[72]	Krishnaprasad A., Dev D., Shawkat M.S., et al. (2023). Graphene/MoS₂/SiO_x memristive synapses for linear weight update. npj 2D Mater. Appl. 7:22. DOI:10.1038/s41699-023-00388-y View in Article Google Scholar
[73]	Chen A., Zhang P., Zheng Y., et al. (2024). Realizing reliable linearity and forming-free property in conductive bridging random access memory synapse by alloy electrode engineering. Appl. Phys. Express 17:036505. DOI:10.35848/1882-0786/ad2f65 View in Article CrossRef Google Scholar
[74]	Yang T.J., Cho J.R., Lee H., et al. (2024). Improvement of the symmetry and linearity of synaptic weight update by combining the InGaZnO synaptic transistor and memristor. IEEE Access 12:28531−28537. DOI:10.1109/ACCESS.2024.3366224 View in Article CrossRef Google Scholar
[75]	Li R., Wang W., Li Y., et al. (2023). Multi-modulated optoelectronic memristor based on Ga₂O₃/MoS₂ heterojunction for bionic synapses and artificial visual system. Nano Energy 111:108398. DOI:10.1016/j.nanoen.2023.108398 View in Article CrossRef Google Scholar
[76]	Pei J., Song L., Liu P., et al. (2025). Scalable synaptic transistor memory from solution-processed carbon nanotubes for high-speed neuromorphic data processing. Adv. Mater. 37:2312783. DOI:10.1002/adma.202312783 View in Article CrossRef Google Scholar
[77]	Yang Y. and Calakos N. (2013). Presynaptic long-term plasticity. Front. Synaptic Neurosci. 5:8. DOI:10.3389/fnsyn.2013.00008 View in Article CrossRef Google Scholar
[78]	Sun L., Wang W. and Yang H. (2020). Recent progress in synaptic devices based on 2D materials. Adv. Int. Sys. 2:1900167. DOI:10.1002/aisy.201900167 View in Article CrossRef Google Scholar
[79]	Kwon K.C., Baek J.H., Hong K., et al. (2022). Memristive devices based on two-dimensional transition metal chalcogenides for neuromorphic computing. Nano-Micro Lett. 14:58. DOI:10.1007/s40820-021-00784-3 View in Article CrossRef Google Scholar
[80]	Liu Z., Mei J., Tang J., et al. (2025). A memristor-based adaptive neuromorphic decoder for brain–computer interfaces. Nat. Electron. 8:362−372. DOI:10.1038/s41928-025-01340-2 View in Article CrossRef Google Scholar
[81]	Dang Z., Guo F., Zhao Y., et al. (2024). Ferroelectric modulation of ReS₂-based multifunctional optoelectronic neuromorphic devices for wavelength-selective artificial visual system. Adv. Funct. Mater. 34:2400105. DOI:10.1002/adfm.202400105 View in Article CrossRef Google Scholar
[82]	Zhang W., Li J., Cheng L., et al. (2023). Synaptic transistor arrays based on pva/lignin composite electrolyte films. IEEE Trans. Electron Devices 70:3245−3250. DOI:10.1109/TED.2023.3265940 View in Article CrossRef Google Scholar
[83]	Vu Q.A., Shin Y.S., Kim Y.R., et al. (2016). Two-terminal floating-gate memory with van der Waals heterostructures for ultrahigh on/off ratio. Nat. Commun. 7:12725. DOI:10.1038/ncomms12725 View in Article CrossRef Google Scholar
[84]	Cao G., Meng P., Chen J., et al. (2021). 2D material based synaptic devices for neuromorphic computing. Adv. Funct. Mater. 31:2005443. DOI:10.1002/adfm.202005443 View in Article CrossRef Google Scholar
[85]	Zhou F., Chen J., Tao X., et al. (2019). 2D materials based optoelectronic memory: Convergence of electronic memory and optical sensor. Research 2019:9490413. DOI:10.34133/2019/9490413 View in Article CrossRef Google Scholar
[86]	He C., Tang J., Shang D.-S., et al. (2020). Artificial synapse based on van der waals heterostructures with tunable synaptic functions for neuromorphic computing. ACS Appl. Mater. Interfaces 12:11945−11954. DOI:10.1021/acsami.9b21747 View in Article CrossRef Google Scholar
[87]	Jo S.H., Chang T., Ebong I., et al. (2010). Nanoscale memristor device as synapse in neuromorphic systems. Nano Lett. 10:1297−1301. DOI:10.1021/nl904092h View in Article CrossRef Google Scholar
[88]	Ang K.W., Yu M.B., Zhu S.Y., et al. (2008). Novel NiGe MSM photodetector featuring asymmetrical schottky barriers using sulfur co-implantation and segregation. IEEE Electron Device Lett. 29:708−710. DOI:10.1109/LED.2008.923541 View in Article CrossRef Google Scholar
[89]	Li G., Xie D., Zhong H., et al. (2022). Photo-induced non-volatile VO₂ phase transition for neuromorphic ultraviolet sensors. Nat. Commun. 13:1729. DOI:10.1038/s41467-022-29456-5 View in Article Google Scholar
[90]	Hu G., An H., Xi J., et al. (2021). A ZnO micro/nanowire-based photonic synapse with piezo-phototronic modulation. Nano Energy 89:106282. DOI:10.1016/j.nanoen.2021.106282 View in Article Google Scholar
[91]	Subramanian Periyal S., Jagadeeswararao M., Ng S.E., et al. (2020). Halide perovskite quantum dots photosensitized-amorphous oxide transistors for multimodal synapses. Adv. Mater. Technol. 5:2000514. DOI:10.1002/admt.202000514 View in Article Google Scholar
[92]	Gao S., Liu G., Yang H., et al. (2019). An oxide schottky junction artificial optoelectronic synapse. ACS Nano 13:2634−2642. DOI:10.1021/acsnano.9b00340 View in Article CrossRef Google Scholar
[93]	Meng J., Wang T., Zhu H., et al. (2022). Integrated in-sensor computing optoelectronic device for environment-adaptable artificial retina perception application. Nano Lett. 22:81−89. DOI:10.1021/acs.nanolett.1c03240 View in Article CrossRef Google Scholar
[94]	Wang D., Zhao S., Li L., et al. (2022). All-flexible artificial reflex arc based on threshold-switching memristor. Adv. Funct. Mater. 32:2200241. DOI:10.1002/adfm.202200241 View in Article Google Scholar
[95]	Sun T., Feng B., Huo J., et al. (2023). Artificial intelligence meets flexible sensors: Emerging smart flexible sensing systems driven by machine learning and artificial synapses. Nano-Micro Lett. 16:14. DOI:10.1007/s40820-023-01235-x View in Article CrossRef Google Scholar
[96]	Zhang F., Li C., Li Z., et al. (2023). Recent progress in three-terminal artificial synapses based on 2D materials: From mechanisms to applications. Microsyst. Nanoeng. 9:16. DOI:10.1038/s41378-023-00487-2 View in Article CrossRef Google Scholar
[97]	Zhou M., Zhao Y., Gu X., et al. (2023). Realize low-power artificial photonic synapse based on (Al,Ga)N nanowire/graphene heterojunction for neuromorphic computing. APL Photonics 8:076107. DOI:10.1063/5.0152156 View in Article CrossRef Google Scholar
[98]	Wu G., Tang L., Deng G., et al. (2022). Transparent dual-band ultraviolet photodetector based on graphene/p-GaN/AlGaN heterojunction. Opt. Express 30:21349−21361. DOI:10.1364/OE.460151 View in Article CrossRef Google Scholar
[99]	Meng J., Wang T., He Z., et al. (2022). A high-speed 2D optoelectronic in-memory computing device with 6-bit storage and pattern recognition capabilities. Nano Res. 15:2472−2478. DOI:10.1007/s12274-021-3729-9 View in Article CrossRef Google Scholar
[100]	Asadi K., Li M., Blom P.W.M., et al. (2011). Organic ferroelectric opto-electronic memories. Mater. Today 14:592−599. DOI:10.1016/S1369-7021(11)70300-5 View in Article CrossRef Google Scholar
[101]	Lee D., Hwang E., Lee Y., et al. (2016). Multibit MoS₂ photoelectronic memory with ultrahigh sensitivity. Adv. Mater. 28:9196−9202. DOI:10.1002/adma.201603571 View in Article CrossRef Google Scholar
[102]	Hou Y.-X., Li Y., Zhang Z.-C., et al. (2021). Large-scale and flexible optical synapses for neuromorphic computing and integrated visible information sensing memory processing. ACS Nano 15:1497−1508. DOI:10.1021/acsnano.0c08921 View in Article CrossRef Google Scholar
[103]	Cho S.W., Kwon S.M., Kim Y.-H., et al. (2021). Recent progress in transistor-based optoelectronic synapses: From neuromorphic computing to artificial sensory system. Adv. Int. Sys. 3:2000162. DOI:10.1002/aisy.202000162 View in Article CrossRef Google Scholar
[104]	Lan S., Zhong J., Chen J., et al. (2021). An optoelectronic synaptic transistor with efficient dual modulation by light illumination. J. Mater. Chem. C 9:3412−3420. DOI:10.1039/D0TC05738J View in Article CrossRef Google Scholar
[105]	Sun Y., Ding Y., Xie D., et al. (2021). Optically stimulated synaptic transistor based on MoS₂/quantum dots mixed-dimensional heterostructure with gate-tunable plasticity. Opt. Lett. 46:1748−1751. DOI:10.1364/OL.414820 View in Article CrossRef Google Scholar
[106]	Zhang M., Tang Z., Liu X., et al. (2020). Electronic neural interfaces. Nat. Electron. 3:191−200. DOI:10.1038/s41928-020-0390-3 View in Article CrossRef Google Scholar
[107]	Zhang X., Zhuo Y., Luo Q., et al. (2020). An artificial spiking afferent nerve based on Mott memristors for neurorobotics. Nat. Commun. 11:51. DOI:10.1038/s41467-019-13827-6 View in Article CrossRef Google Scholar
[108]	Liu C., Chen H., Wang S., et al. (2020). Two-dimensional materials for next-generation computing technologies. Nat. Nanotechnol. 15:545−557. DOI:10.1038/s41565-020-0724-3 View in Article CrossRef Google Scholar
[109]	Wang S., Pan X., Lyu L., et al. (2022). Nonvolatile van der waals heterostructure phototransistor for encrypted optoelectronic logic circuit. ACS Nano 16:4528−4535. DOI:10.1021/acsnano.1c10978 View in Article CrossRef Google Scholar
[110]	Xia F., Xia T., Xiang L., et al. (2022). Carbon nanotube-based flexible ferroelectric synaptic transistors for neuromorphic computing. ACS Appl. Mater. Interfaces 14:30124−30132. DOI:10.1021/acsami.2c07825 View in Article CrossRef Google Scholar
[111]	Shi J., Jie J., Deng W., et al. (2022). A fully solution-printed photosynaptic transistor array with ultralow energy consumption for artificial-vision neural networks. Adv. Mater. 34:2200380. DOI:10.1002/adma.202200380 View in Article CrossRef Google Scholar
[112]	Li T., Guo W., Ma L., et al. (2021). Epitaxial growth of wafer-scale molybdenum disulfide semiconductor single crystals on sapphire. Nat. Nanotechnol. 16:1201−1207. DOI:10.1038/s41565-021-00963-8 View in Article CrossRef Google Scholar
[113]	Li F., Gao S., Lu Y., et al. (2021). Bio-inspired multi-mode pain-perceptual system (MMPPS) with noxious stimuli warning, damage localization, and enhanced damage protection. Adv. Sci. 8:2004208. DOI:10.1002/advs.202004208 View in Article CrossRef Google Scholar
[114]	Yang C., Qian J., Jiang S., et al. (2020). An optically modulated organic schottky-barrier planar-diode-based artificial synapse. Adv. Opt. Mater. 8:2000153. DOI:10.1002/adom.202000153 View in Article CrossRef Google Scholar
[115]	Yang X., Xiong Z., Chen Y., et al. (2020). A self-powered artificial retina perception system for image preprocessing based on photovoltaic devices and memristive arrays. Nano Energy 78:105246. DOI:10.1016/j.nanoen.2020.105246 View in Article CrossRef Google Scholar
[116]	Kim D., Jin B., Kim S.-A., et al. (2022). An ultrasensitive silicon-based electrolyte-gated transistor for the detection of peanut allergens. Biosensors 12:4. DOI:10.3390/bios12010024 View in Article CrossRef Google Scholar
[117]	Yang L. and Cao B.-Y. (2021). Thermal transport of amorphous phase change memory materials using population-coherence theory: A first-principles study. J. Phys. D: Appl. Phys. 54:505302. DOI:10.1088/1361-6463/ac1ec3 View in Article CrossRef Google Scholar
[118]	He Y., Jiang S., Chen C., et al. (2021). Electrolyte-gated neuromorphic transistors for brain-like dynamic computing. J. Appl. Phys. 130:190904. DOI:10.1063/5.0069456 View in Article CrossRef Google Scholar
[119]	Wang C., Li Y., Wang Y., et al. (2021). Thin-film transistors for emerging neuromorphic electronics: Fundamentals, materials, and pattern recognition. J. Mater. Chem. C 9:11464−11483. DOI:10.1039/D1TC01660A View in Article CrossRef Google Scholar
[120]	Li Y., Xuan Z., Lu J., et al. (2021). One transistor one electrolyte-gated transistor based spiking neural network for power-efficient neuromorphic computing system. Adv. Funct. Mater. 31:2100042. DOI:10.1002/adfm.202100042 View in Article CrossRef Google Scholar
[121]	Li J., Lei Y., Wang Z., et al. (2024). High-density artificial synapse array consisting of homogeneous electrolyte-gated transistors. Adv. Sci. 11:2305430. DOI:10.1002/advs.202305430 View in Article CrossRef Google Scholar
[122]	Ni Y., Liu L., Liu J., et al. (2022). A high-strength neuromuscular system that implements reflexes as controlled by a multiquadrant artificial efferent nerve. ACS Nano 16:20294−20304. DOI:10.1021/acsnano.2c06122 View in Article CrossRef Google Scholar
[123]	Liu J., Gong J., Wei H., et al. (2022). A bioinspired flexible neuromuscular system based thermal-annealing-free perovskite with passivation. Nat. Commun. 13:7427. DOI:10.1038/s41467-022-35092-w View in Article CrossRef Google Scholar
[124]	Zhao Y., Wang L., Zhou Y., et al. (2021). Solid polymer electrolytes with high conductivity and transference number of Li ions for Li-based rechargeable batteries. Adv. Sci. 8:2003675. DOI:10.1002/advs.202003675 View in Article CrossRef Google Scholar
[125]	Tian X., Zhao T., Li J., et al. (2022). Coplanar-gate synaptic transistor array with organic electrolyte using lithographic process. IEEE Trans. Electron Devices 69:2325−2330. DOI:10.1109/TED.2022.3154668 View in Article CrossRef Google Scholar
[126]	Liu G., Li Q., Shi W., et al. (2022). Ultralow-power and multisensory artificial synapse based on electrolyte-gated vertical organic transistors. Adv. Funct. Mater. 32:2200959. DOI:10.1002/adfm.202200959 View in Article CrossRef Google Scholar
[127]	Roe D.G., Kim S., Choi Y.Y., et al. (2021). Biologically plausible artificial synaptic array: Replicating ebbinghaus’ memory curve with selective attention. Adv. Mater. 33:2007782. DOI:10.1002/adma.202007782 View in Article CrossRef Google Scholar
[128]	Wang Y., Gou S., Dong X., et al. (2025). A biologically inspired artificial neuron with intrinsic plasticity based on monolayer molybdenum disulfide. Nat. Electron. 8:680−688. DOI:10.1038/s41928-025-01433-y View in Article CrossRef Google Scholar
[129]	Deng W., Yu Y., Yan X., et al. (2025). Linearly programmable oxygen-doped MoS₂ memtransistor for neuromorphic computing. ACS Nano 19:27526−27537. DOI:10.1021/acsnano.5c06688 View in Article CrossRef Google Scholar
[130]	Ghosh A., Vinzons L.U., Šlechta A., et al. (2025). Versatile dual-gate 2D transistor for logic-in-memory and neuromodulation applications. Small 07:2503991. DOI:10.1002/smll.202503991 View in Article CrossRef Google Scholar
[131]	Kim S.-G., Lee S.-H., Yang I.S., et al. (2022). Effect of fluorine substitution in a hole dopant on the photovoltaic performance of perovskite solar cells. ACS Energy Letters 7:741−748. DOI:10.1021/acsenergylett.1c02807 View in Article CrossRef Google Scholar
[132]	Li R., Gong Y., Huang H., et al. (2025). Photonics for neuromorphic computing: Fundamentals, devices, and opportunities. Adv. Mater. 37:2312825. DOI:10.1002/adma.202312825 View in Article CrossRef Google Scholar
[133]	He Q., Wang H., Zhang Y., et al. (2025). Two-dimensional materials based two-transistor-two-resistor synaptic kernel for efficient neuromorphic computing. Nat. Commun. 16:4340. DOI:10.1038/s41467-025-59815-x View in Article CrossRef Google Scholar
[134]	Kim S.J., Lee H.-J., Lee C.-H., et al. (2024). 2D materials-based 3D integration for neuromorphic hardware. npj 2D Mater. Appl. 8:70. DOI:10.1038/s41699-024-00509-1 View in Article Google Scholar
[135]	Jeon Y.-R., Seo D., Lee Y., et al. (2024). The 3D monolithically integrated hardware based neural system with enhanced memory window of the volatile and non-volatile devices. Adv. Sci. 11:2402667. DOI:10.1002/advs.202402667 View in Article CrossRef Google Scholar
[136]	Wang Y., Zhou T., Cui Y., et al. (2024). Reconfigurable sensing-memory-processing and logical integration within 2D ferroelectric optoelectronic transistor for CMOS-compatible bionic vision. Adv. Funct. Mater. 34:2400039. DOI:10.1002/adfm.202400039 View in Article CrossRef Google Scholar
[137]	Zhang W., Hejda M., Al-Taai Q.R.A., et al. (2024). Photonic-electronic spiking neuron with multi-modal and multi-wavelength excitatory and inhibitory operation for high-speed neuromorphic sensing and computing. Neuromorphic Comput. Eng. 4:044006. DOI:10.1088/2634-4386/ad8df8 View in Article CrossRef Google Scholar
[138]	Lin J., You T., Wang M., et al. (2018). Efficient ion-slicing of InP thin film for Si-based hetero-integration. Nanotechnology 29:504002. DOI:10.1088/1361-6528/aae281 View in Article CrossRef Google Scholar
[139]	Shi H., Huang K., Mu F., et al. (2020). Realization of wafer-scale single-crystalline GaN film on CMOS-compatible Si(100) substrate by ion-cutting technique. Semicond. Sci. Technol. 35:125004. DOI:10.1088/1361-6641/abb073 View in Article CrossRef Google Scholar
[140]	Wu L.-S., Zhao Y., Shen H.-C., et al. (2016). Heterogeneous integration of GaAs pHEMT and Si CMOS on the same chip. Chin. Phys. B 25:067306. DOI:10.1088/1674-1056/25/6/067306 View in Article CrossRef Google Scholar
[141]	Gutierrez-Aitken A., Scott D., Sato K., et al. (2017). Diverse accessible heterogeneous integration (Dahi) foundry at northrop grumman aerospace systems (NGAS). ECS Trans. 80:125. DOI:10.1149/08004.0125ecst View in Article CrossRef Google Scholar
[142]	Choi Y., Jin P., Lee S., et al. (2025). All-printed chip-less wearable neuromorphic system for multimodal physicochemical health monitoring. Nat. Commun. 16:5689. DOI:10.1038/s41467-025-60854-7 View in Article CrossRef Google Scholar
[143]	Tang W., Cho S.G., Hoang T.T., et al. (2024). Arvon: A heterogeneous system-in-package integrating FPGA and Dsp chiplets for versatile workload acceleration. IEEE J. Solid-State Circuits 59:1235−1245. DOI:10.1109/JSSC.2023.3343457 View in Article CrossRef Google Scholar
[144]	Li T., Hou J., Yan J., et al. (2020). Chiplet heterogeneous integration technology—status and challenges. Electronics 9:670. DOI:10.3390/electronics9040670 View in Article CrossRef Google Scholar
[145]	Gambino J.P., Adderly S.A. and Knickerbocker J.U. (2015). An overview of through-silicon-via technology and manufacturing challenges. Microelectron. Eng. 135:73−106. DOI:10.1016/j.mee.2014.10.019 View in Article CrossRef Google Scholar
[146]	Liao K., Lian Y., Yu M., et al. (2025). Hetero-integrated perovskite/Si₃N₄ on-chip photonic system. Nat. Photonics 19:358−368. DOI:10.1038/s41566-024-01603-y View in Article CrossRef Google Scholar
[147]	Smit M., Williams K. and van der Tol J. (2019). Past, present, and future of InP-based photonic integration. APL Photonics 4:050901. DOI:10.1063/1.5087862 View in Article CrossRef Google Scholar
[148]	Hoefler G.E., Zhou Y., Anagnosti M., et al. (2019). Foundry development of system-on-chip InP-based photonic integrated circuits. IEEE J. Sel. Top. Quantum Electron. 25:1−17. DOI:10.1109/JSTQE.2019.2906270 View in Article CrossRef Google Scholar
[149]	Gupta S. and Xavier J. (2025). Neuromorphic photonic on-chip computing. Chips 4:34. DOI:10.3390/chips4030034 View in Article CrossRef Google Scholar
[150]	Zhou W., Shen X., Yang X., et al. (2024). Fabrication and integration of photonic devices for phase-change memory and neuromorphic computing. Int. J. Extreme Manuf. 6:022001. DOI:10.1088/2631-7990/ad1575 View in Article CrossRef Google Scholar
[151]	Zhang D., Yu S.-Q., Salamo G.J., et al. (2024). Modeling study of Si₃N₄ waveguides on a sapphire platform for photonic integration applications. Materials 17:4148. DOI:10.3390/ma17164148 View in Article CrossRef Google Scholar
[152]	Chen X., Milosevic M.M., Stanković S., et al. (2018). The emergence of silicon photonics as a flexible technology platform. Proc. IEEE 106:2101−2116. DOI:10.1109/JPROC.2018.2854372 View in Article CrossRef Google Scholar
[153]	Hodassman S., Vardi R., Tugendhaft Y., et al. (2022). Efficient dendritic learning as an alternative to synaptic plasticity hypothesis. Sci. Rep. 12:6571. DOI:10.1038/s41598-022-10466-8 View in Article CrossRef Google Scholar
[154]	Miehl C. and Gjorgjieva J. (2022). Stability and learning in excitatory synapses by nonlinear inhibitory plasticity. PLoS Comput. Biol. 18:1010682. DOI:10.1371/journal.pcbi.1010682 View in Article CrossRef Google Scholar
[155]	Reifenstein E.T., Bin Khalid I. and Kempter R. (2021). Synaptic learning rules for sequence learning. eLife 10:67171. DOI:10.7554/eLife.67171 View in Article CrossRef Google Scholar
[156]	Rakic P., Bourgeois J.-P. and Goldman-Rakic P.S. (1994). Synaptic development of the cerebral cortex: Implications for learning, memory, and mental illness. Prog. Brain Res. 102:227−243. DOI:10.1016/S0079-6123(08)60543-9 View in Article CrossRef Google Scholar
[157]	Wei H., Han H., Guo K., et al. (2021). Artificial synapses that exploit ionic modulation for perception and integration. Mater. Today Phys. 18:100329. DOI:10.1016/j.mtphys.2020.100329 View in Article CrossRef Google Scholar
[158]	Kennedy M.B. (2013). Synaptic signaling in learning and memory. Cold Spring Harb. Perspect. Biol. 8:016824. DOI:10.1101/cshperspect.a016824 View in Article CrossRef Google Scholar
[159]	Zhao J., Zhou Z., Zhang Y., et al. (2019). An electronic synapse memristor device with conductance linearity using quantized conduction for neuroinspired computing. J. Mater. Chem. C 7:1298−1306. DOI:10.1039/C8TC04395G View in Article CrossRef Google Scholar
[160]	Jang J., Gi S., Yeo I., et al. (2022). A learning-rate modulable and reliable tio memristor array for robust, fast, and accurate neuromorphic computing. Adv. Sci. 9:2201117. DOI:10.1002/advs.202201117 View in Article CrossRef Google Scholar
[161]	Schuman C.D., Kay B., Date P., et al. (2021). Sparse binary matrix-vector multiplication on neuromorphic computers. IEEE Int. Parallel Distrib. Process. Symp. Workshops 2021:308−311. DOI:10.1109/IPDPSW52791.2021.00054 View in Article CrossRef Google Scholar
[162]	Tian C., Wei L., Li Y., et al. (2021). Recent progress on two-dimensional neuromorphic devices and artificial neural network. Curr. Appl. Phys. 31:182−198. DOI:10.1016/j.cap.2021.08.014 View in Article CrossRef Google Scholar
[163]	Agatonovic-Kustrin S. and Beresford R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J. Pharm. Biomed. Anal. 22:717−727. DOI:10.1016/S0731-7085(99)00272-1 View in Article CrossRef Google Scholar
[164]	Schuman C.D., Kulkarni S.R., Parsa M., et al. (2022). Opportunities for neuromorphic computing algorithms and applications. Nat. Comput. Sci. 2:10−19. DOI:10.1038/s43588-021-00184-y View in Article CrossRef Google Scholar
[165]	Liu X., Mao M., Liu B., et al. (2016). Harmonica: A framework of heterogeneous computing systems with memristor-based neuromorphic computing accelerators. IEEE Trans. Circuits Syst. 63:617−628. DOI:10.1109/TCSI.2016.2529279 View in Article CrossRef Google Scholar
[166]	Pazos S., Zhu K., Villena M.A., et al. (2025). Synaptic and neural behaviours in a standard silicon transistor. Nature 640:69−76. DOI:10.1038/s41586-025-08742-4 View in Article CrossRef Google Scholar
[167]	Choi Y., Kim J.-H., Qian C., et al. (2020). Gate-tunable synaptic dynamics of ferroelectric-coupled carbon-nanotube transistors. ACS Appl. Mater. Interfaces 12:4707−4714. DOI:10.1021/acsami.9b17742 View in Article CrossRef Google Scholar
[168]	Fang Y., Li Q., Meng J., et al. (2023). Photonic synapses for image recognition and high density integration of simplified artificial neural networks. Adv. Electron. Mater. 9:2300120. DOI:10.1002/aelm.202300120 View in Article CrossRef Google Scholar
[169]	Rosenfeld B., Simeone O. and Rajendran B. (2022). Spiking generative adversarial networks with a neural network discriminator: Local training, bayesian models, and continual meta-learning. IEEE Trans. Comput. 71:2778−2791. DOI:10.1109/TC.2022.3191738 View in Article CrossRef Google Scholar
[170]	Tandale S.B. and Stoffel M. (2023). Spiking recurrent neural networks for neuromorphic computing in nonlinear structural mechanics. Comput. Meth. Appl. Mech. Eng. 412:116095. DOI:10.1016/j.cma.2023.116095 View in Article CrossRef Google Scholar
[171]	Sozos K., Bogris A., Bienstman P., et al. (2022). High-speed photonic neuromorphic computing using recurrent optical spectrum slicing neural networks. Commun. Eng. 1:24. DOI:10.1038/s44172-022-00024-5 View in Article CrossRef Google Scholar
[172]	Fahimi F., Dosen S., Ang K.K., et al. (2021). Generative adversarial networks-based data augmentation for brain–computer interface. IEEE Trans. Neural Netw. Learn. Syst. 32:4039−4051. DOI:10.1109/TNNLS.2020.3016666 View in Article CrossRef Google Scholar
[173]	Hu X., Feng G., Duan S., et al. (2017). A memristive multilayer cellular neural network with applications to image processing. IEEE Trans. Neural Netw. Learn. Syst. 28:1889−1901. DOI:10.1109/TNNLS.2016.2552640 View in Article CrossRef Google Scholar
[174]	Seo S., Lee J.-J., Lee R.-G., et al. (2021). An optogenetics-inspired flexible van der waals optoelectronic synapse and its application to a convolutional neural network. Adv. Mater. 33:2102980. DOI:10.1002/adma.202102980 View in Article CrossRef Google Scholar
[175]	Kulkarni S.R. and Rajendran B. (2018). Spiking neural networks for handwritten digit recognition—supervised learning and network optimization. Neural Netw. 103:118−127. DOI:10.1016/j.neunet.2018.03.019 View in Article CrossRef Google Scholar
[176]	Anwani N. and Rajendran B. (2020). Training multi-layer spiking neural networks using NormAD based spatio-temporal error backpropagation. Neurocomputing 380:67−77. DOI:10.1016/j.neucom.2019.10.104 View in Article CrossRef Google Scholar
[177]	Velichko A. and Boriskov P. (2020). Concept of lIF neuron circuit for rate coding in spike neural networks. IEEE Trans. Circuits Syst. II, Exp. Briefs 67:3477−3481. DOI:10.1109/TCSII.2020.2997117 View in Article CrossRef Google Scholar
[178]	Wu S., Li X. and Ding Y. (2021). Saturated impulsive control for synchronization of coupled delayed neural networks. Neural Netw. 141:261−269. DOI:10.1016/j.neunet.2021.04.012 View in Article CrossRef Google Scholar
[179]	Taherkhani A., Belatreche A., Li Y., et al. (2020). A review of learning in biologically plausible spiking neural networks. Neural Netw. 122:253−272. DOI:10.1016/j.neunet.2019.09.036 View in Article CrossRef Google Scholar
[180]	Ori H., Marder E. and Marom S. (2018). Cellular function given parametric variation in the Hodgkin and Huxley model of excitability. Proc. Natl. Acad. Sci. USA 115:8211−8218. DOI:10.1073/pnas.1808552115 View in Article CrossRef Google Scholar
[181]	Yang R., Huang H.-M., Hong Q.-H., et al. (2018). Synaptic suppression triplet-stdp learning rule realized in second-order memristors. Adv. Funct. Mater. 28:1704455. DOI:10.1002/adfm.201704455 View in Article CrossRef Google Scholar
[182]	Muni S.S., Rajagopal K., Karthikeyan A., et al. (2022). Discrete hybrid Izhikevich neuron model: Nodal and network behaviours considering electromagnetic flux coupling. Chaos, Solitons & Fractals 155:111759. DOI:10.1016/j.chaos.2021.111759 View in Article CrossRef Google Scholar
[183]	Han C.Y., Fang S.L., Cui Y.L., et al. (2023). Configurable NbO_x memristors as artificial synapses or neurons achieved by regulating the forming compliance current for the spiking neural network. Adv. Electron. Mater. 9:2300018. DOI:10.1002/aelm.202300018 View in Article CrossRef Google Scholar
[184]	Quintino Palhares J.H., Garg N., Mouny P.-A., et al. (2024). 28 nm FDSOI embedded PCM exhibiting near zero drift at 12 K for cryogenic SNNs. npj Unconventional Computing 1:8. DOI:10.1038/s44335-024-00008-y View in Article CrossRef Google Scholar
[185]	Cheong W.H., Jeon J.B., In J.H., et al. (2022). Demonstration of neuromodulation-inspired stashing system for energy-efficient learning of spiking neural network using a self-rectifying memristor array. Adv. Funct. Mater. 32:2200337. DOI:10.1002/adfm.202200337 View in Article CrossRef Google Scholar
[186]	Xu J., Jiang B., Wang W., et al. (2025). High-order dynamics in an ultra-adaptive neuromorphic vision device.Nat. Nanotechnol. 20:1419–1430. DOI:10.1038/s41565-025-01984-3 View in Article Google Scholar
[187]	Birkoben T., Winterfeld H., Fichtner S., et al. (2020). A spiking and adapting tactile sensor for neuromorphic applications. Sci. Rep. 10:17260. DOI:10.1038/s41598-020-74219-1 View in Article CrossRef Google Scholar
[188]	Hua Q., Cui X., Liu H., et al. (2020). Piezotronic synapse based on a single GaN microwire for artificial sensory systems. Nano Lett. 20:3761−3768. DOI:10.1021/acs.nanolett.0c00733 View in Article CrossRef Google Scholar
[189]	Kamm G.B., Boffi J.C., Zuza K., et al. (2021). A synaptic temperature sensor for body cooling. Neuron 109:3283−3297.e11. DOI:10.1016/j.neuron.2021.10.001 View in Article CrossRef Google Scholar
[190]	Ye H., Liu Z., Han H., et al. (2022). Lead-free AgBiI₄ perovskite artificial synapses for a tactile sensory neuron system with information preprocessing function. Mater. Adv. 3:7248−7256. DOI:10.1039/D2MA00675H View in Article CrossRef Google Scholar
[191]	Wang M., Tu J., Huang Z., et al. (2022). Tactile near-sensor analogue computing for ultrafast responsive artificial skin. Adv. Mater. 34:2201962. DOI:10.1002/adma.202201962 View in Article CrossRef Google Scholar
[192]	Yoon J.H., Wang Z., Kim K.M., et al. (2018). An artificial nociceptor based on a diffusive memristor. Nat. Commun. 9:417. DOI:10.1038/s41467-017-02572-3 View in Article CrossRef Google Scholar
[193]	Liu Y., Zhong J., Li E., et al. (2019). Self-powered artificial synapses actuated by triboelectric nanogenerator. Nano Energy 60:377−384. DOI:10.1016/j.nanoen.2019.03.079 View in Article CrossRef Google Scholar
[194]	Zhu P., Mu S., Huang W., et al. (2024). Soft multifunctional neurological electronic skin through intrinsically stretchable synaptic transistor. Nano Res. 17:6550−6559. DOI:10.1007/s12274-024-6566-8 View in Article CrossRef Google Scholar
[195]	Sun J., Oh S., Choi Y., et al. (2018). Optoelectronic synapse based on IGZO-alkylated graphene oxide hybrid structure. Adv. Funct. Mater. 28:1804397. DOI:10.1002/adfm.201804397 View in Article CrossRef Google Scholar
[196]	Yang C.-M., Chen T.-C., Verma D., et al. (2020). Bidirectional all-optical synapses based on a 2D Bi₂O₂se/graphene hybrid structure for multifunctional optoelectronics. Adv. Funct. Mater. 30:2001598. DOI:10.1002/adfm.202001598 View in Article CrossRef Google Scholar
[197]	Gao S., Liu G., Yang H., et al. (2019). An oxide schottky junction artificial optoelectronic synapse. ACS Nano 13:2634−2642. DOI:10.1021/acsnano.9b00340 View in Article CrossRef Google Scholar
[198]	Jiang J., Xiao W., Li X., et al. (2024). Hardware-level image recognition system based on ZnO photo-synapse array with the self-denoising function. Adv. Funct. Mater. 34:2313507. DOI:10.1002/adfm.202313507 View in Article CrossRef Google Scholar
[199]	Sun B., Zhou G., Yu T., et al. (2022). Multi-factor-controlled ReRAM devices and their applications. J. Mater. Chem. C 10:8895−8921. DOI:10.1039/D1TC06005H View in Article CrossRef Google Scholar
[200]	Qin X., Hu J., Liu H., et al. (2023). Performance regulation of a ZnO/WO_x-based memristor and its application in an emotion circuit. J. Phys. Chem. Lett. 14:3039−3046. DOI:10.1021/acs.jpclett.3c00063 View in Article CrossRef Google Scholar
[201]	Mao S., Sun B., Ke C., et al. (2023). Evolution between CRS and NRS behaviors in MnO₂/TiO₂ nanocomposite based memristor for multi-factors-regulated memory applications. Nano Energy 107:108117. DOI:10.1016/j.nanoen.2022.108117 View in Article CrossRef Google Scholar
[202]	Zhang X., Lu J., Wang Z., et al. (2021). Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks. Sci. Bull. 66:1624−1633. DOI:10.1016/j.scib.2021.04.014 View in Article CrossRef Google Scholar
[203]	Li R., Song M., Guo Z., et al. (2022). In-memory mathematical operations with spin-orbit torque devices. Adv. Sci. 9:2202478. DOI:10.1002/advs.202202478 View in Article CrossRef Google Scholar
[204]	James A.P. (2019). A hybrid memristor–CMOS chip for AI. Nat. Electron. 2:268−269. DOI:10.1038/s41928-019-0274-6 View in Article CrossRef Google Scholar
[205]	Yuan P., Dong D., Zheng X., et al. (2022). Reflow soldering capability improvement by utilizing tan interfacial layer in 1Mbit RRAM chip. Micromachines 13:567. DOI:10.3390/mi13040567 View in Article CrossRef Google Scholar
[206]	Cai F., Correll J.M., Lee S.H., et al. (2019). A fully integrated reprogrammable memristor–CMOS system for efficient multiply–accumulate operations. Nat. Electron. 2:290−299. DOI:10.1038/s41928-019-0270-x View in Article CrossRef Google Scholar
[207]	Gao B., Lin B., Pang Y., et al. (2022). Concealable physically unclonable function chip with a memristor array. Sci. Adv. 8:eabn7753. DOI:10.1126/sciadv.abn7753 View in Article CrossRef Google Scholar
[208]	Harabi K.-E., Hirtzlin T., Turck C., et al. (2023). A memristor-based Bayesian machine. Nat. Electron. 6:52−63. DOI:10.1038/s41928-022-00886-9 View in Article CrossRef Google Scholar
[209]	Yang Y., Pan C., Li Y., et al. (2024). In-sensor dynamic computing for intelligent machine vision. Nat. Electron. 7:225−233. DOI:10.1038/s41928-024-01124-0 View in Article CrossRef Google Scholar
[210]	Krauhausen I., Koutsouras D.A., Melianas A., et al. (2021). Organic neuromorphic electronics for sensorimotor integration and learning in robotics. Sci. Adv. 7:5068. DOI:10.1126/sciadv.abl5068 View in Article CrossRef Google Scholar
[211]	He K., Liu Y., Yu J., et al. (2022). Artificial neural pathway based on a memristor synapse for optically mediated motion learning. ACS Nano 16:9691−9700. DOI:10.1021/acsnano.2c03100 View in Article CrossRef Google Scholar
[212]	Yu R., Zhang X., Gao C., et al. (2022). Low-voltage solution-processed artificial optoelectronic hybrid-integrated neuron based on 2D MXene for multi-task spiking neural network. Nano Energy 99:107418. DOI:10.1016/j.nanoen.2022.107418 View in Article CrossRef Google Scholar
[213]	Wang C., Liang S.-J., Wang C.-Y., et al. (2021). Scalable massively parallel computing using continuous-time data representation in nanoscale crossbar array. Nat. Nanotechnol. 16:1079−1085. DOI:10.1038/s41565-021-00943-y View in Article CrossRef Google Scholar
[214]	Park M., Yang J.Y., Yeom M.J., et al. (2023). An artificial neuromuscular junction for enhanced reflexes and oculomotor dynamics based on a ferroelectric CuInP₂S₆/GaN HEMT. Sci. Adv. 9:eadh9889. DOI:10.1126/sciadv.adh9889 View in Article CrossRef Google Scholar
[215]	Chen B., Yang H., Song B., et al. (2020). A memristor-based hybrid analog-digital computing platform for mobile robotics. Sci. Robot. 5:eabb6938. DOI:10.1126/scirobotics.abb6938 View in Article CrossRef Google Scholar
[216]	Zhang J., Li J., Xu R., et al. (2025). A self-driven Ga₂O₃ memristor synapse for humanoid robot learning. Small Methods 9:2400989. DOI:10.1002/smtd.202400989 View in Article CrossRef Google Scholar
[217]	Jiang M., Zhao Y., Liu T., et al. (2025). A dual-mode transparent device for 360° quasi-omnidirectional self-driven photodetection and efficient ultralow-power neuromorphic computing. Light:Sci. Appl. 14:273. DOI:10.1038/s41377-025-01991-y View in Article CrossRef Google Scholar
[218]	Ma S., Pei J., Zhang W., et al. (2022). Neuromorphic computing chip with spatiotemporal elasticity for multi-intelligent-tasking robots. Sci. Robot. 7:eabk2948. DOI:10.1126/scirobotics.abk2948 View in Article CrossRef Google Scholar
[219]	Yang Y., Bartolozzi C., Zhang H.H., et al. (2023). Neuromorphic electronics for robotic perception, navigation and control: A survey. Eng. Appl. Artif. Intell. 126:106838. DOI:10.1016/j.engappai.2023.106838 View in Article CrossRef Google Scholar
[220]	Li M., Liu H., Zhao R., et al. (2023). Imperfection-enabled memristive switching in van der Waals materials. Nat. Electron. 6:491−505. DOI:10.1038/s41928-023-00984-2 View in Article CrossRef Google Scholar
[221]	Xia Q. and Yang J.J. (2019). Memristive crossbar arrays for brain-inspired computing. Nat. Mater. 18:309−323. DOI:10.1038/s41563-019-0291-x View in Article CrossRef Google Scholar
[222]	Ghahramani Z. (2015). Probabilistic machine learning and artificial intelligence. Nature 521:452−459. DOI:10.1038/nature14541 View in Article CrossRef Google Scholar
[223]	Zenke F. and Neftci E.O. (2021). Brain-inspired learning on neuromorphic substrates. Proc. IEEE 109:935−950. DOI:10.1109/JPROC.2020.3045625 View in Article CrossRef Google Scholar
[224]	Davies M., Srinivasa N., Lin T.H., et al. (2018). Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro 38:82−99. DOI:10.1109/MM.2018.112130359 View in Article CrossRef Google Scholar
[225]	Lee D., Park M., Baek Y., et al. (2022). In-sensor image memorization and encoding via optical neurons for bio-stimulus domain reduction toward visual cognitive processing. Nat. Commun. 13:5223. DOI:10.1038/s41467-022-32790-3 View in Article CrossRef Google Scholar
[226]	Merolla P.A., Arthur J.V., Alvarez-Icaza R., et al. (2014). A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345:668−673. DOI:10.1126/science.1254642 View in Article CrossRef Google Scholar
[227]	Sun B., Chen Y., Zhou G., et al. (2024). Memristor-based artificial chips. ACS Nano 18:14−27. DOI:10.1021/acsnano.3c07384 View in Article CrossRef Google Scholar
[228]	Wang K., Ren S., Jia Y., et al. (2025). Neuromorphic chips for biomedical engineering. Mechanobiol. Med. 3:100133. DOI:10.1016/j.mbm.2025.100133 View in Article CrossRef Google Scholar
[229]	Liu Z., Tang J., Gao B., et al. (2020). Neural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces. Nat. Commun. 11:4234. DOI:10.1038/s41467-020-18105-4 View in Article CrossRef Google Scholar
[230]	Bao C., Kim T.-H., Hassanpoor Kalhori A., et al. (2022). A 3D-printed neuromorphic humanoid hand for grasping unknown objects. iScience 25:104119. DOI:10.1016/j.isci.2022.104119 View in Article CrossRef Google Scholar
[231]	Paredes-Vallés F., Hagenaars J.J., Dupeyroux J., et al. (2024). Fully neuromorphic vision and control for autonomous drone flight. Sci. Robot. 9:0591. DOI:10.1126/scirobotics.adi0591 View in Article CrossRef Google Scholar

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Gu X., Bian L., Cheng L., et al. (2026). Recent progress on integrated neuromorphic chips. The Innovation Informatics 2:100027. https://doi.org/10.59717/j.xinn-inform.2026.100027

Gu X., Bian L., Cheng L., et al. (2026). Recent progress on integrated neuromorphic chips. The Innovation Informatics 2:100027. https://doi.org/10.59717/j.xinn-inform.2026.100027

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