Tunable Bipolar Photothermoelectric Response from Mott Activation for In‐Sensor Image Preprocessing
Bowen Li, Ning Lin, Zhaowu Wang, Zhaowu Wang, Baojie Chen, Changyong Lan, Xiaocui Li, You Meng, Weijun Wang, Mingqi Ding, Pengshan Xie, Yuxuan Zhang, Zenghui Wu, Dengji Li, Fu‐Rong Chen, Chi Hou Chan, Zhongrui Wang, Zhongrui Wang, Johnny C. Ho
Abstract
In-sensor image preprocessing, a subset of edge computing, offers a solution to mitigate frequent analog-digital conversions and the von Neumann bottleneck in conventional digital hardware. However, an efficient in-sensor device array with large-scale integration capability for high-density and low-power sensory processing is still lacking and highly desirable. This work introduces an adjustable broadband photothermoelectric detector based on a phase-change vanadium dioxide thin-film transistor. This transistor employs a vanadium dioxide/gallium nitride three-terminal structure with a gate-tunable phase transition at the gate-source junctions. This design allows for modulable photothermoelectric responsivities and alteration of the short-circuit photocurrent's polarities. The devices exhibit linear gate dependence for the broadband photoresponse and linear light-intensity dependence for both positive and negative photoresponsivities. The device's energy consumption is as low as 8 pJ per spike, which is one order of magnitude lower than that of previous Mott materials-based in-sensor preprocessing devices. A wafer-scale bipolar phototransistor array has also been fabricated by standard micro-/nano-fabrication techniques, exhibiting excellent stability and endurance (over 5000 cycles). More importantly, an integrated in-sensor convolutional network is successfully designed for simultaneous broadband image classification, medical image denoising, and retinal vessel segmentation, delivering exceptional performance and paving the way for future smart edge sensors.