Defect-Engineered Flexible ZnO Synaptic TFTs for Adaptive Visual Preprocessing and In-Sensor Reservoir Computing

Abstract Reliable visual perception under extreme illumination (underexposure/overexposure) and the trade-off between device operation and photoresponse tunability remain challenges for wearable-compatible vision systems. Inspired by the adaptive gain-control mechanism of the vertebrate retina, we report a hardware-algorithm codesign framework based on defect-engineered ZnO synaptic thin-film transistors for in-sensor visual preprocessing and reservoir computing. On flexible polyimide substrates, a low-temperature (150 °C) defect-engineering strategy combining an Al2O3 planarization layer with a controlled air anneal drives the ZnO channel into a partially passivated state, reducing the defect-related O 1s component from 41.37% to 13.09% while retaining trap-related states required for programmable synaptic plasticity. The resulting synaptic TFTs achieve pA-level off-state currents and gate-programmable paired-pulse facilitation tunable from 163% to 112%. Building on this gate-tunable defect-mediated photoresponse, we design a simulated 20 × 20 pixel-level adaptive preprocessing array in which calibration-based dynamic gate-bias control enables physical-layer gain modulation to mitigate overexposure and enhance underexposed signals. This device-assisted physical-layer preprocessing yields a 5.8 dB improvement in peak signal-to-noise ratio and a 0.29 gain in the structural similarity index, restoring downstream machine-vision accuracy by 30.56 percentage points. Furthermore, exploiting the intrinsic fading memory of the defect-engineered ZnO channel, in-sensor physical reservoir computing is realized with a 96.58% recognition accuracy on the MNIST data set. This material-engineered strategy offers a potential pathway toward robust visual perception in unpredictable optical environments for wearable-compatible electronics.