Molybdenum Oxide Artificial Synapse: Enabling Cognitive Learning, Image Recognition, and Denoising

Neuromorphic systems, which are inspired by the human brain, hold great promise for significant advances in future AI applications, particularly in energy-efficient and real-time image processing. This paper presents the fabrication of a high-temperature sputtered molybdenum oxide-based artificial synapse that replicates essential synaptic properties, including Paired Pulse Facilitation/Depression (PPF/PPD), Spike Timing-Dependent Plasticity (STDP), Spike Number-Dependent Plasticity (SNDP), and Spike Frequency-Dependent Plasticity (SFDP) as well as two specific cognitive models: the Atkinson-Shiffrin model and Ebbinghaus memory curve. The intrusion of noise into an image results in degradation of the image quality during processing and visualization. Scanning tunneling microscopy (STM) is a powerful tool for atomic-scale surface characterization; however, its inherently slow scanning process and susceptibility to various noise sources often result in low-quality images that are frequently discarded. There are denoising algorithms that are relatively effective but have low energy efficiency and a long computation time. This paper presents a Convolutional Neural Network (CNN)-based denoising model based on the Au/MoxOy/FTO artificial synapse with denoising and image preservation of the Highly Oriented Pyrolytic Graphite (HOPG) STM images and a cartoon rendering of a cat, achieved through a correlation between conductance states and image pixels The model’s performance is quantitatively evaluated using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics, demonstrating effective noise suppression while maintaining image integrity. The device exhibits efficient pattern recognition on the MNIST handwritten digit data set, attaining an accuracy of 92.2%, underscoring its potential for neuromorphic computing applications. Furthermore, its applicability in image processing is validated through training and inference on the Canadian Institute For Advanced Research-10 (CIFAR-10) data set using the CNN model, where a maximum recognition accuracy of 94.06% is attained. This study emphasizes the capabilities of molybdenum oxide-based synaptic devices in progressing artificial intelligence, image enhancement, and edge computing applications.