Programmable black phosphorus image sensor for broadband optoelectronic edge computing

Seokhyeong Lee, Ruoming Peng (), Changming Wu and Mo Li ()
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Seokhyeong Lee: University of Washington
Ruoming Peng: University of Washington
Changming Wu: University of Washington
Mo Li: University of Washington

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: Abstract Image sensors with internal computing capability enable in-sensor computing that can significantly reduce the communication latency and power consumption for machine vision in distributed systems and robotics. Two-dimensional semiconductors have many advantages in realizing such intelligent vision sensors because of their tunable electrical and optical properties and amenability for heterogeneous integration. Here, we report a multifunctional infrared image sensor based on an array of black phosphorous programmable phototransistors (bP-PPT). By controlling the stored charges in the gate dielectric layers electrically and optically, the bP-PPT’s electrical conductance and photoresponsivity can be locally or remotely programmed with 5-bit precision to implement an in-sensor convolutional neural network (CNN). The sensor array can receive optical images transmitted over a broad spectral range in the infrared and perform inference computation to process and recognize the images with 92% accuracy. The demonstrated bP image sensor array can be scaled up to build a more complex vision-sensory neural network, which will find many promising applications for distributed and remote multispectral sensing.

Date: 2022
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DOI: 10.1038/s41467-022-29171-1

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