An infrared-sensing memory device based on Se 0.3 Te 0.7 /CuInP 2 S 6 heterostructure for convolutionalneural network computing

The advent of the big data and the internet of things has created an urgent demand for novel devices capable of simultaneously realizing in-sensor and in-memory computing. Although the introduction of van der Waals (vdW) materials has provided corresponding solutions, their applications in in-sensor computing operating at the infrared (IR) band remains less explored. In this work, we developed an IR-sensing memory device (ISMD) based on Se 0.3 Te 0.7 /CuInP 2 S 6 (CIPS) vdW heterostructure, which combines Se 0.3 Te 0.7 semiconductor with considerable IR photoresponse as the channel and CIPS as the ferroelectric layer. Leveraging plasma-induced interfacial charge trapping as the dominant mechanism, which is further stabilized by the ferroelectric polarization effect of CIPS, the device exhibits multi-bit non-volatile storage characteristics under electrical control. In addition, benefiting from the superior IR photoresponse of Se 0.3 Te 0.7 , the device demonstrates an instantaneous response to 1550 nm IR light, with the responsivity controllably modulated by electrically tuning the channel conductance state. We further simulated a convolutional neural network system based on ISMD. The frontend of the architecture utilizes the optoelectronic properties of ISMD as convolution kernel parameters, enabling selective responses to edges in different directions. The backend is a fully connected classifier constrained by actual conductance values. Using this system, we perform handwritten digit classification on the Modified National Institute of Standards and Technology (MNIST) dataset, achieving an accuracy of over 90%. The ISMD provides new materials and structural options for preparing memory devices capable of sensing IR bands and offers a new paradigm for realizing integrated in-sensor and in-memory computing.