High performance, integrated 1T1R oxide-based oscillator: Stack engineering for low-power operation in neural network applications

Abstract

Brain-inspired non-Boolean computing paradigms are gaining wide interest due to their error resilient nature and massive parallelism. This work explores oxide-based compact oscillators for oscillatory neural networks (ONN). We demonstrate for the first time, best in class high-frequency performance at 500 MHz and low power (<; 200 μW). The superior figures of merit are achieved due to device engineering to give maximum swing at low power and integration as a 1T1R structure. We show frequency control over 2 orders of magnitude by varying the gate voltage and show its applicability to an ONN-based associative memory.