부산대학교 도서관

In this work, for the first time, we algorithmically merge the memory trace effect into activation function in neural network for reinforcement learning, and a novel ferroelectric FET (FeFET) based agent with both activation function and plastic weight is proposed and experimentally demonstrated. By exploiting the physics of short-time scale dynamics of depolarization process induced by depolarization field, the activation function with memory trace effect can be emulated in one FeFET at ultra-low operating voltage of 0.5V. In addition, the plastic weight with multilevel states can be realized by the long-time scale retention of spontaneous polarization in FeFET simultaneously. Moreover, based on the proposed FeFET-based agent, reinforcement learning is demonstrated with convergence rate boost and high energy efficiency. This work provides a promising highly-integrated agent solution for RL system.