2D (NH4)BiI3 enables non-volatile optoelectronic memories for machine learning

Machine learning is the core of artificial intelligence. Using optical signals for training and converting them into electrical signals for inference, combines the strengths of both, and thus can greatly improve machine learning efficiency. Optoelectronic memories are the hardware foundation for this strategy. However, the existing optoelectronic memories cannot modulate a large number of non-volatile resistive states using ultra-short and ultra-dim light pulses, leading to low training accuracy, slow computing speed and high energy consumption. Here, we synthesized a van der Waals layered photoconductive material, (NH<sub>4</sub>)BiI<sub>3</sub>, with excellent photoconductivity and strong dielectric screening effect. We further employed it as the photosensitive control gate in a floating-gate transistor, replacing the commonly used metal control gate, to construct an optical floating gate transistor which achieves adjustable synaptic weights under ultra-dim light without gate voltage assistance. Moreover, it shows ultra-low training energy consumption to generate a non-volatile state and the largest resistive state numbers among the known non-volatile optoelectronic memories. These exceptional performances enable the construction of one-transistor-one-memory device arrays to achieve ~99% accuracy in Artificial Neural Networks. Moreover, the device arrays can match the performance of GPU in YOLOv8 while greatly reducing energy consumption.

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Publication Details

Journal
Nature Communications
Published
2025-02-13
DOI
https://doi.org/10.1038/s41467-025-56819-5
Citations
12
Primary Topic
Perovskite Materials and Applications
Type
article
Field-Weighted Citation Impact
8.77

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article

2D (NH4)BiI3 enables non-volatile optoelectronic memories for machine learning

Chi Liu, Li‐Hui Chen, Yang Chai, L Li et al.
12 citations
Nature Communications
Perovskite Materials and Applications
8.77
article

2D (NH4)BiI3 enables non-volatile optoelectronic memories for machine learning

Chi Liu, Li‐Hui Chen, Yang Chai, L Li, Wencai Ren, Lai‐Peng Ma, Yuning Wei, Peitao Liu, Jinhua Liang, Bo Tong, Jiangxu Li, Qiang Wang, Jinhong Du, Zhibo Liu, Jiajun Xu, Tengda Du
article en
12 citations

Abstract

Machine learning is the core of artificial intelligence. Using optical signals for training and converting them into electrical signals for inference, combines the strengths of both, and thus can greatly improve machine learning efficiency. Optoelectronic memories are the hardware foundation for this strategy. However, the existing optoelectronic memories cannot modulate a large number of non-volatile resistive states using ultra-short and ultra-dim light pulses, leading to low training accuracy, slow computing speed and high energy consumption. Here, we synthesized a van der Waals layered photoconductive material, (NH<sub>4</sub>)BiI<sub>3</sub>, with excellent photoconductivity and strong dielectric screening effect. We further employed it as the photosensitive control gate in a floating-gate transistor, replacing the commonly used metal control gate, to construct an optical floating gate transistor which achieves adjustable synaptic weights under ultra-dim light without gate voltage assistance. Moreover, it shows ultra-low training energy consumption to generate a non-volatile state and the largest resistive state numbers among the known non-volatile optoelectronic memories. These exceptional performances enable the construction of one-transistor-one-memory device arrays to achieve ~99% accuracy in Artificial Neural Networks. Moreover, the device arrays can match the performance of GPU in YOLOv8 while greatly reducing energy consumption.

Nature CommunicationsVol. 16(1)
University of Science and Technology of China (CN), Hong Kong Polytechnic University (HK), Shenyang University of Technology (CN), Chinese Academy of Sciences (CN), Institute of Metal Research (CN), Affiliated Hospital of Liaoning University of Traditional Chinese Medicine (CN), China Medical University (CN), Northeastern University (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, Huazhong University of Science and Technology, Liaoning Revitalization Talents Program, Institute of Metal Research, Chinese Academy of Sciences, Basic and Applied Basic Research Foundation of Guangdong Province
Affordable and clean energy
Openalex Percentile: Top 2%
Perovskite Materials and Applications
8.77
DOIOpen Access PDFOpenAlex
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