A Reconfigurable Two‐WSe₂‐Transistor Synaptic Cell for Reinforcement Learning

Abstract Reward‐modulated spike‐timing‐dependent plasticity (R‐STDP) is a brain‐inspired reinforcement learning (RL) rule, exhibiting potential for decision‐making tasks and artificial general intelligence. However, the hardware implementation of the reward‐modulation process in R‐STDP usually requires complicated Si complementary metal–oxide–semiconductor (CMOS) circuit design that causes high power consumption and large footprint. Here, a design with two synaptic transistors (2T) connected in a parallel structure is experimentally demonstrated. The 2T unit based on WSe₂ ferroelectric transistors exhibits reconfigurable polarity behavior, where one channel can be tuned as n‐type and the other as p‐type due to nonvolatile ferroelectric polarization. In this way, opposite synaptic weight update behaviors with multilevel (>6 bit) conductance states, ultralow nonlinearity (0.56/−1.23), and large G_max/G_min ratio of 30 are realized. By applying positive/negative reward to (anti‐)STDP component of 2T cell, R‐STDP learning rules are realized for training the spiking neural network and demonstrated to solve the classical cart–pole problem, exhibiting a way for realizing low‐power (32 pJ per forward process) and highly area‐efficient (100 µm²) hardware chip for reinforcement learning.