Programmable black phosphorus image sensor for broadband optoelectronic edge computing

TL;DR

This paper introduces a programmable black phosphorus infrared image sensor capable of in-sensor CNN inference, enabling efficient broadband multispectral imaging and recognition with high accuracy for edge computing applications.

Contribution

The work presents a novel black phosphorous-based programmable phototransistor array that integrates sensing and computing, advancing in-sensor neural network implementation.

Findings

Achieved 92% image recognition accuracy.

Demonstrated broadband infrared imaging and in-sensor CNN processing.

Enabled programmable and scalable multispectral sensing.

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 are uniquely advantageous in realizing such intelligent visionary 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 high 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 multispectral infrared imaging and in-sensor computing with the black phosphorous optoelectronic sensor array can be scaled up to build a more complex visionary neural network, which will find many promising applications for distributed and remote multispectral sensing.