Frequency Adaptive Single-Transistor Neuron Based on Temporal Charge Trapping
Jae‐Hyun Lee, Yena Lee, Jun‐Young Park, Joon‐Kyu Han · IEEE Transactions on Electron Devices · 2025
Spiking frequency adaptation (SFA) plays an important role in neuromorphic systems, enhancing energy efficiency and resilience to unwanted noise. In this study, the SFA function is implemented in a single-transistor neuron utilizing a silicon–oxide–nitride–silicon (SONS) gate-stack. The experimental results confirm that the SFA function is successfully achieved through temporal hole trapping in the nitride layer adjacent to the channel. Unlike circuit-based approaches, this work presents a novel method for implementing SFA at the single-transistor level, significantly improving both area and energy efficiencies.