Ferroelectric Polarized in Transistor Channel Polarity Modulation for Reward-Modulated Spike-Time-Dependent Plasticity Application

Reward signals reflect the developmental tendency of reinforcement learning (RL) agents. Reward-modulated spike-time-dependent plasticity (R-STDP) is an efficient and concise information processing feature in RL. However, the physical construction of R-STDP normally demands complex circuit design engineering, resulting in large power consumption and large area. In this work, we studied the role of ferroelectric polarization in the modulation of carbon nanotube transistor channel polarity. Furthermore, we applied a modulating channel method to construct a 2T synaptic component by spin-coating technology. Based on the nonvolatility of ferroelectric polarization, the synaptic component constructed has the characteristics of reconfigurable polarity. One channel could be modulated to n-type and the other to p-type. One modulated channel was used to perform the STDP function when the reward signal arrived, and the other modulated channel was used to perform the anti-STDP function when the punishment signal arrived. Finally, R-STDP learning rules are implemented on hardware. This work provides a strategy for hardware construction of RL.

Automated summary

The study explores the role of ferroelectric polarization in modulating the channel polarity of carbon nanotube transistors to construct synaptic components with reconfigurable polarity. By using a modulating channel method, the components are able to perform both STDP and anti-STDP functions based on reward and punishment signals, providing a strategy for hardware implementation of reinforcement learning.