Low-dimensional perovskite optoelectronic synapses for neuromorphic vision

Zhihao Xu; Yingjie Luo; Alson Yau; Yuan Meng; Seung-il Kim; Kyubeen Kim; Sheng Ran; Sang-Hoon Bae

doi:10.1088/1674-4926/26060035

J. Semicond. > Just Accepted

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Low-dimensional perovskite optoelectronic synapses for neuromorphic vision

Zhihao Xu^{1, 4, §}, Yingjie Luo^{2, §}, Alson Yau^{3, §}, Yuan Meng¹, Seung-il Kim¹, Kyubeen Kim¹, Sheng Ran² and Sang-Hoon Bae^{1, 4,}

+ Author Affiliations

1. Department of Mechanical Engineering & Materials Science, Washington University in St. Louis, St. Louis, Missouri 63130, United States
2. Department of Physics, Washington University in St. Louis, St. Louis, MO 63130, USA
3. The Preston M. Green Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
4. Institute of Materials Science and Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
^§Zhihao Xu, Yingjie Luo, and Alson Yau contributed equally to this work and should be considered as co-first authors.

Author Bio:

Zhihao Xu received his bachelor's degree from the School of Precision Instruments and Optoelectronics Engineering at Tianjin University in 2017 and his master's degree from the same institution in 2020. He is currently pursuing his Ph.D. degree at Washington University in St. Louis.

Yingjie Luo received his bachelor's degree from South China University of Technology in 2021 and his master's degree from the same institution in 2024. He is currently pursuing his Ph.D. degree in Physics at Washington University in St. Louis.

Alson Yau is currently an undergraduate student at Washington University in St. Louis under the supervision of Prof. Sang-Hoon Bae.

Sang-Hoon Bae received his bachelor's and master's degrees from Sungkyunkwan University in 2011 and 2013, respectively, and his Ph.D. degree in materials science and engineering from the University of California, Los Angeles, in 2017. He is currently an Assistant Professor at Washington University in St. Louis. His research focuses on advanced materials, heterogeneous integration, and AI hardware.

Corresponding author: Sang-Hoon Bae, sbae22@wustl.edu

DOI: 10.1088/1674-4926/26060035CSTR: 32376.14.1674-4926.26060035

Abstract: Low-dimensional metal-halide perovskites have emerged as promising materials for optoelectronic synapses and neuromorphic vision systems owing to their strong light–matter interactions, tunable optoelectronic properties, and mixed ionic–electronic transport. Compared with three-dimensional counterparts, low-dimensional perovskites exhibit more powerful control over charge transport, ion migration, and optical absorption with enhanced stability. In this review, we outline the composition, dimensionality, and growth strategies of low-dimensional perovskites, emphasizing how structural engineering governs their optical and electrical properties. Their photodetection, memristive, and photo-reservoir behaviors are further discussed to form physical foundation for light-stimulated synaptic functions, including optical sensing, memory retention, and conductance modulation. We review representative optoelectronic synapses based on 0D, 1D, and 2D perovskites according to their device-level mechanisms, highlighting how dimensionality enables distinct visual functions and device architectures. Exemplary neuromorphic algorithms are discussed as well, including artificial neural networks, convolutional neural networks, spiking neural networks, recurrent neural networks, and reservoir computing, which translate perovskite synaptic responses into higher-level capabilities such as image recognition, multimodal perception, and motion detection. Finally, we catalog the remaining challenges including lead toxicity, limited lead-free performance, crystallization and device-to-device variations, ion-migration-induced instability, and options for wafer-scale patterning and addressable integration. By connecting materials design, synaptic device physics, neuromorphic algorithms, and scalable integration strategies, with perspectives on how low-dimensional perovskites can evolve from laboratory-scale optoelectronic synapses into practical platforms for next-generation artificial vision systems.

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Fig. 1. (Color online) Composition, dimensionality, and patterning strategies of metal halide perovskites. (a) Schematic crystal structure of a three-dimensional metal halide perovskite with formula AMX₃. (b) Representative of the dimensional evolution of perovskite from 2D to 3D structures^[12]. Adapted from Kim et al., Advanced Electronic Materials, 2019 (c) Comparison of representative MAPbI₃ nanowire growth strategies. Spin coating produces interconnected nanowire networks with morphology dependent on growth conditions, whereas nanofluidic-channel templating guides crystallization into spatially defined nanowire arrays^{[13, 14]}. Adapted from Im et al., Nano Letters, 2015 and Spina et al., Scientific Reports, 2016.

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Fig. 2. (Color online) Artificial vision systems using LDPs. (a) Schematic of perovskite quantum dots, nanowires, and 2D nanomembranes and artificial optical neurons. (b) Schematic showing the growth of perovskite quantum dots directly on graphene, presenting a hybrid optoelectronic synapse^[29]. Reproduced from Pradhan et al., Science Advances, 2020. (c) The overall structure of a neuromorphic bionic eye based on hemispherical perovskite nanowire arrays with filter-free color vision^[30]. Reproduced from Long et al., Nature Communications, 2023. (d) Schematic diagram of circular polarization-sensitive system based on perovskite nanomembranes and carbon nanotubes heterojunctions^[31]. Adapted from Liu et al., Nature Communications, 2023.

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Fig. 3. (Color online) Representative artificial neural networks and use cases. (a) Conventional Artificial Neural Networks using fully-connected layers for vision perception and classification^[67]. Adapted from Lin et al., npj Flexible Electronics, 2025. (b) Convolutional Neural Network architectures for extracting spatial image features, enabling high-resolution visual recognition. (c) Spiking Neural Network architectures using spike-based signal transmission for dynamic and motion perception.

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Fig. 4. (Color online) Lead-free design and patterned integration of halide perovskites for practical optoelectronic synapses. (a) Representative lead-free perovskite compositions and material mechanisms for artificial synapses, highlighting how vacancy formation, band-structure modulation, and ion-migration-related conductance changes can regulate synaptic plasticity^[76]. Adapted from Liu et al., Matter, 2024. (b) Representative lead-free Cs₃Bi₂I₉ nanocrystal-based flexible optoelectronic synapse, showing light-induced synaptic behavior and neuromorphic visual recognition^[77]. Adapted from Li et al., Research, 2024. (c) Top-down lithographic patterning of multicolor hybrid perovskite films using a SU8/PMMA bilayer resist strategy, enabling microscale perovskite patterns and multicolor pixel arrays for optoelectronic integration^[78]. Adapted from Harwell et al., ACS Nano, 2019. (d) Wafer-scale position-controlled growth of perovskite microplate arrays for addressable photodetector and transistor integration, demonstrating how spatially defined crystal growth can bridge perovskite materials synthesis and integrated device arrays^[79]. Adapted from Wang et al., Science Advances, 2015.

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Received: 17 June 2026 Revised: 14 August 2026 Online: Accepted Manuscript: 23 September 2026

Article Navigation > Journal of Semiconductors > 2026 > Accepted Manuscript

Zhihao Xu, Yingjie Luo, Alson Yau, Yuan Meng, Seung-il Kim, Kyubeen Kim, Sheng Ran, Sang-Hoon Bae. Low-dimensional perovskite optoelectronic synapses for neuromorphic vision[J]. Journal of Semiconductors, 2026, In Press. doi: 10.1088/1674-4926/26060035 ****Z H Xu, Y J Luo, A Yau, Y Meng, S Kim, K Kim, S Ran, and S Bae, Low-dimensional perovskite optoelectronic synapses for neuromorphic vision[J]. J. Semicond., 2026, accepted doi: 10.1088/1674-4926/26060035

Citation:

Z H Xu, Y J Luo, A Yau, Y Meng, S Kim, K Kim, S Ran, and S Bae, Low-dimensional perovskite optoelectronic synapses for neuromorphic vision[J]. J. Semicond., 2026, accepted doi: 10.1088/1674-4926/26060035

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Low-dimensional perovskite optoelectronic synapses for neuromorphic vision

DOI: 10.1088/1674-4926/26060035

CSTR: 32376.14.1674-4926.26060035

Zhihao Xu^{1, 4, §},
Yingjie Luo^{2, §},
Alson Yau^{3, §},
Yuan Meng¹,
Seung-il Kim¹,
Kyubeen Kim¹,
Sheng Ran²,
Sang-Hoon Bae^{1, 4,}

1.
Department of Mechanical Engineering & Materials Science, Washington University in St. Louis, St. Louis, Missouri 63130, United States
2.
Department of Physics, Washington University in St. Louis, St. Louis, MO 63130, USA
3.
The Preston M. Green Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States
4.
Institute of Materials Science and Engineering, Washington University in St. Louis, St. Louis, Missouri 63130, United States

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Zhihao Xu received his bachelor's degree from the School of Precision Instruments and Optoelectronics Engineering at Tianjin University in 2017 and his master's degree from the same institution in 2020. He is currently pursuing his Ph.D. degree at Washington University in St. Louis
Yingjie Luo received his bachelor's degree from South China University of Technology in 2021 and his master's degree from the same institution in 2024. He is currently pursuing his Ph.D. degree in Physics at Washington University in St. Louis
Alson Yau is currently an undergraduate student at Washington University in St. Louis under the supervision of Prof. Sang-Hoon Bae
Sang-Hoon Bae received his bachelor's and master's degrees from Sungkyunkwan University in 2011 and 2013, respectively, and his Ph.D. degree in materials science and engineering from the University of California, Los Angeles, in 2017. He is currently an Assistant Professor at Washington University in St. Louis. His research focuses on advanced materials, heterogeneous integration, and AI hardware
Corresponding author: sbae22@wustl.edu
Received Date: 2026-06-17
Revised Date: 2026-08-14

Available Online: 2026-09-23

Abstract

Abstract

Low-dimensional metal-halide perovskites have emerged as promising materials for optoelectronic synapses and neuromorphic vision systems owing to their strong light–matter interactions, tunable optoelectronic properties, and mixed ionic–electronic transport. Compared with three-dimensional counterparts, low-dimensional perovskites exhibit more powerful control over charge transport, ion migration, and optical absorption with enhanced stability. In this review, we outline the composition, dimensionality, and growth strategies of low-dimensional perovskites, emphasizing how structural engineering governs their optical and electrical properties. Their photodetection, memristive, and photo-reservoir behaviors are further discussed to form physical foundation for light-stimulated synaptic functions, including optical sensing, memory retention, and conductance modulation. We review representative optoelectronic synapses based on 0D, 1D, and 2D perovskites according to their device-level mechanisms, highlighting how dimensionality enables distinct visual functions and device architectures. Exemplary neuromorphic algorithms are discussed as well, including artificial neural networks, convolutional neural networks, spiking neural networks, recurrent neural networks, and reservoir computing, which translate perovskite synaptic responses into higher-level capabilities such as image recognition, multimodal perception, and motion detection. Finally, we catalog the remaining challenges including lead toxicity, limited lead-free performance, crystallization and device-to-device variations, ion-migration-induced instability, and options for wafer-scale patterning and addressable integration. By connecting materials design, synaptic device physics, neuromorphic algorithms, and scalable integration strategies, with perspectives on how low-dimensional perovskites can evolve from laboratory-scale optoelectronic synapses into practical platforms for next-generation artificial vision systems.

^§Zhihao Xu, Yingjie Luo, and Alson Yau contributed equally to this work and should be considered as co-first authors.

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