In-sensor dynamic computing for intelligent machine vision

Accurate detection and tracking of targets in low-light and complex scenarios is essential for the development of intelligent machine vision. However, such capabilities are difficult to achieve using conventional static optoelectronic convolutional processing. Here we show that in-sensor dynamic computing can be used for accurate detection and robust tracking of dim targets. The approach uses multiple-terminal mixed-dimensional graphene–germanium heterostructure device arrays and relies on the dynamic correlation of adjacent optoelectronic devices in the array. The photoresponse of the devices can range from positive to negative depending on the drain–source voltage polarity and can be further tailored using the back-gate and top-gate voltage. The correlation characteristic of the device array can be used to selectively amplify small differences in light intensity and to accurately extract edge features of dim targets. We show that the approach can provide robust tracking of dim targets in complex environments.