Vertical oxygen-gradient-engineered photoelectrochemical transistors for efficient on-chip sparsity capture and neural network processing units

Traditional hardware systems struggle with implementing current artificial neural networks due to the waste of substantial computational resources on insignificant data. Hardware realization of sparse neural networks offers a significant solution because of their potential to concentrate solely on crucial data. However, these devices still face great challenges in signal encoding and attention-guided sparse capture. Herein, we demonstrate a large-scale sparse-capture neural network (SCNN) using vertical multichannel photoelectrochemical transistors, which are constructed from the ultrashort, tri-layer, oxygen-gradient-engineered indium-tin oxide channel with an approximately 15 nm thick. This device exhibits high sparsity at a low operating voltage of 3.0 V, facilitating dynamic neural connectivity and outstanding energy efficiency. The proposed SCNN achieves recognition accuracy exceeding 94% and reduces energy consumption by over 30%. Therefore, this work offers a promising avenue toward energy-efficient neuromorphic systems for edge AI, real-time sensing, and adaptive decision-making.