Performance Analysis and Optimization of Fructose Memristor-Based Neuromorphic Systems

Natural organic memristors have demonstrated promising synaptic behavior, positioning them as strong candidates for synaptic devices in neuromorphic systems. This paper presents the fabrication and evaluation of a neuromorphic system based on natural organic 16-level and 32-level fructose memristors. First, the manufacturing process of fructose memristors is described in detail. Second, the nonlinear property associated with fructose memristors is investigated, and an optimization method is applied to address the nonlinear effects. The performance of the fructose memristor-based neuromorphic system on MNIST with a multi-layer perceptron and on CIFAR-10 with VGG-8 is evaluated and reported under various conditions -with/without nonlinearity optimization and temporal and spatial variations. Without nonlinearity optimization, simulation results on MNIST demonstrate that fructose memristor-based neuromorphic systems achieve average accuracies of 65% for 16-level and 76% for 32-level devices. Applying the nonlinearity optimization has increased the average accuracies to 70% in 16-level and 81% in 32-level devices. On CIFAR-10, maximum test accuracies reach 79%-81% (16-level) and 86%–87% (32-level), but the nonlinearity optimization provides none/minor improvements for 16-level and 32-level devices. Simulations demonstrate 16-level devices are more favorable for edge devices, while 32-level devices are suitable for accuracy-critical tasks. The results presented in this paper demonstrate the potential and viability of the fructose memristor-based neuromorphic systems. The fructose memristor represents a promising addition to sustainable alternatives for neuromorphic systems, encouraging further exploration of natural organic materials.