Non-volatile 2D MoS2/black phosphorus heterojunction photodiodes in the near- to mid-infrared region

Yuyan Zhu, Yang Wang (), Xingchen Pang, Yongbo Jiang, Xiaoxian Liu, Qing Li, Zhen Wang, Chunsen Liu, Weida Hu () and Peng Zhou ()
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Yuyan Zhu: Fudan University
Yang Wang: Fudan University
Xingchen Pang: Fudan University
Yongbo Jiang: Fudan University
Xiaoxian Liu: Fudan University
Qing Li: Chinese Academy of Sciences
Zhen Wang: Chinese Academy of Sciences
Chunsen Liu: Fudan University
Weida Hu: Chinese Academy of Sciences
Peng Zhou: Fudan University

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract Cutting-edge mid-wavelength infrared (MWIR) sensing technologies leverage infrared photodetectors, memory units, and computing units to enhance machine vision. Real-time processing and decision-making challenges emerge with the increasing number of intelligent pixels. However, current operations are limited to in-sensor computing capabilities for near-infrared technology, and high-performance MWIR detectors for multi-state switching functions are lacking. Here, we demonstrate a non-volatile MoS2/black phosphorus (BP) heterojunction MWIR photovoltaic detector featuring a semi-floating gate structure design, integrating near- to mid-infrared photodetection, memory and computing (PMC) functionalities. The PMC device exhibits the property of being able to store a stable responsivity, which varies linearly with the stored conductance state. Significantly, device weights (stable responsivity) can be programmed with power consumption as low as 1.8 fJ, and the blackbody peak responsivity can reach 1.68 A/W for the MWIR band. In the simulation of Faster Region with convolution neural network (CNN) based on the FLIR dataset, the PMC hardware responsivity weights can reach 89% mean Average Precision index of the feature extraction network software weights. This MWIR photovoltaic detector, with its versatile functionalities, holds significant promise for applications in advanced infrared object detection and recognition systems.

Date: 2024
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DOI: 10.1038/s41467-024-50353-6

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