In-sensor computing using a MoS2 photodetector with programmable spectral responsivity

Dohyun Kwak, Dmitry K. Polyushkin and Thomas Mueller ()
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Dohyun Kwak: Vienna University of Technology, Institute of Photonics
Dmitry K. Polyushkin: Vienna University of Technology, Institute of Photonics
Thomas Mueller: Vienna University of Technology, Institute of Photonics

Nature Communications, 2023, vol. 14, issue 1, 1-8

Abstract: Abstract Optical spectroscopy is an indispensable technique in almost all areas of scientific research and industrial applications. After its acquisition, an optical spectrum is usually further processed using a mathematical algorithm to classify or quantify the measurement results. Here we present the design and realization of a smart photodetector that provides such information directly without the need to explicitly record a spectrum. This is achieved by tailoring the spectral responsivity of the device to a specific purpose. In-sensor computation is performed at the lowest possible level of the sensor system hierarchy – the physical level of photon detection – and does not require any external processing of the measurement data. The device can be programmed to cover different types of spectral regression or classification tasks. We present the analysis of spectral mixtures as an example, but the scheme can also be applied to any other algorithm that can be represented by a linear operator. Our prototype physical implementation utilizes an ensemble of optical cavity-enhanced MoS2 photodetectors with different center wavelengths and individually adjustable peak responsivities. This spectroscopy method represents a significant advance in miniaturized and energy-efficient optical sensing.

Date: 2023
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DOI: 10.1038/s41467-023-40055-w

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