Tilt-Engineered Molecular-Scale Selector for Enhanced Learning in Artificial Neural Networks

Abstract

Miniaturization of individual selectors in crossbar-array-based artificial neural networks is essential for the advancement of the underlying neuromorphic electronics, as it improves learning, recognition, and prediction accuracies. This study proposes a tilt-engineered molecular-scale selector comprising a heterostructure of biphenyl-4-thiol (OPT2) or 1-octanethiol (C8) molecular layers and an n-type two-dimensional MoS2 monolayer (1L-MoS2) at an approximate contact radius of 3 nm, which is evaluated via conductive atomic force microscopy under various tip-loading forces. The molecular tilt configuration controlled by the tip-loading force is used as a rectifying engineer for the OPT2/1L-MoS2 and C8/1L-MoS2 heterojunction accuracies. Rectification ratios and conductance levels are significantly influenced by the molecular backbones and tilt angle. The proposed tilt-engineered selector can aid in controlling undesired neural signals affecting vector-matrix multiplications and adjusting the switching range compatibility of an integrated synaptic device cell, significantly influencing the pattern recognition accuracy. By controlling the tilt angle, the recognition accuracy on the MNIST dataset increases from 78.65% to 86.45% and from 7.74% to 86.09% when using the OPT2/1L-MoS2 and C8/1L-MoS2 selector, respectively. The proposed molecular tilt configuration can be used for developing customized molecular-scale selectors for crossbar-array-based artificial neural networks to improve learning while suppressing undesired neural signals. Tailored molecular-scale selectors engineered by molecular tilt configuration in crossbar array-based artificial neural networks is presented at the ultimate scale limit (at a contact radius of approximate to 3 nm). This tilt-engineering molecular-scale selector can aid in mitigating undesired neural signals with adjustment of the switching range compatibility of an integrated synaptic device cell which significantly influenced the pattern recognition accuracy.image