Atomistic origins of compound semiconductor synthesis with computational neuromorphic engineering

Abstract

We propose the usage of many element bulk materials to mimic the neural dynamics instead of atomically-thin materials through the modeling of compound semiconductor growth using vacancy defects and dopants by creating and destroying one an other like a complex artificial neural network, where each atom itself is the device in analogy to crossbar memory arrays, where each node is a device. We quantify the effect of atomistic variations in electronic structure of a compound semiconductor using a hybrid method compose of semiempirical tight-binding method, density functional theory, Boltzmann transport theory, and transfer matrix method. We found that the artificial neural network resembles to the neural transmission dynamics and by proposing resistive switching in small areas with low energy consumption, we can increase the integration density similar to the human brain.