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

Bo Tong, Jiajun Xu, Jinhong Du, Peitao Liu, Tianming Du, Qiang Wang, Langjun Li, Yuning Wei, Jiangxu Li, Jinhua Liang, Chi Liu, Zhibo Liu, Chen Li, Lai-Peng Ma, Yang Chai and Wencai Ren ()
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Bo Tong: Chinese Academy of Sciences
Jiajun Xu: Chinese Academy of Sciences
Jinhong Du: Chinese Academy of Sciences
Peitao Liu: Chinese Academy of Sciences
Tianming Du: Northeastern University
Qiang Wang: Chinese Academy of Sciences
Langjun Li: China Medical University
Yuning Wei: Chinese Academy of Sciences
Jiangxu Li: Chinese Academy of Sciences
Jinhua Liang: Chinese Academy of Sciences
Chi Liu: Chinese Academy of Sciences
Zhibo Liu: Chinese Academy of Sciences
Chen Li: Northeastern University
Lai-Peng Ma: Chinese Academy of Sciences
Yang Chai: The Hong Kong Polytechnic University Kowloon
Wencai Ren: Chinese Academy of Sciences

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: 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, (NH4)BiI3, 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.

Date: 2025
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DOI: 10.1038/s41467-025-56819-5

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