Design of a single-electron neural network circuit controlling weights for reservoir computing
Masaki Ueno, Takahide Oya · Japanese Journal of Applied Physics · 2021
Abstract We propose a weight function controllable circuit for single-electron (SE) reservoir computing (RC). While an SE circuit has advantages, e.g., low power consumption and non-linear operation, it also has the disadvantage of being sensitive to fluctuation. Therefore, we focus on RC, as weights do not need to be adjusted in the reservoir layer and there is redundancy for noise. Our idea is that introducing RC can overcome the disadvantages of an SE circuit. Our designed circuit expresses and adjusts the weights by determining how many element signals can pass through it. The result of an operation test indicates that our circuit could accurately express the weights and change them correctly. We also evaluated the discrimination of input signals and found that our circuit could accurately do this. Therefore, our SE RC circuit has the potential to become a new information processing circuit that overcomes the problems of the SE circuit.