An IGZO phototransistor-based ternary inverter integrating optical sensing and weight quantization in ternary neural networks for color image recognition

As deep neural networks (DNNs) continue to advance computer vision, natural language processing, and medical diagnostics, their reliance on 32-bit full-precision weights imposes substantial model size and computational burdens that hinder deployment at the edge; to improve efficiency, we adopt ternary neural networks (TNNs). Here, we present a ternary circuit composed of two parallel indium-gallium-zinc-oxide (IGZO) thin-film phototransistors (TFPTs) and a resistor, exhibiting three stable, discrete current states 'OFF', 'Intermediate', and 'ON' that map to the ternary weight set {-1, 0, 1}; we further realize a compact ternary inverter using only two IGZO TFPTs and two resistors, avoiding complex binary CMOS logic. The processing path begins with optical sensing, wherein the incident light power densities and wavelength determine discrete voltage outputs; during preprocessing, these voltages discretize pixel values (0-255) into multiple intervals that are supplied to the TNN for image recognition. Leveraging this integrated sensing, preprocessing and inference hardware module, we achieve >90% accuracy on CIFAR-10, thereby validating device-level data discretization and transformation and charting a path toward integrated neuromorphic vision systems.