Advanced design of high-performance artificial neuromorphic electronics

Recent years have witnessed the significant progress of nature artificial neuromorphic systems with advances achieved in interdisciplinary fields, like neurosciences, electronics and materials science. The research with focus on learning from human has been conducted from various hierarchy, aiming to realize the intelligent way of human to process information to the largest extent. Significant advancement in artificial neuromorphic electronics has been realized recently, like the ultrasmall size fabrication and high‐density integration of organic synapse. Though a few reviews presented the development from certain aspect, review in the view of the comprehensive learning from human at all levels, ranging from morphologies, structures, distributions of the device arrays and the computing mode of the brain, to fully simulate the function of human, is lacking. Here, the new developments are timely and systematically reviewed for advanced design of high-performance nature artificial neuromorphic electronics. First, recent breakthrough and mechanisms are illustrated, and then the elaborated considerations for the components of artificial neuromorphic devices are demonstrated based on perspective of learning from human neuromorphic systems from various hierarchy. After that, strategies are summarized to enhance the overall performance of the systems by taking the whole information processing procedure into consideration, and then the design thought for future artificial neuromorphic electronics is proposed. Finally, some perspectives are put forward. Copyright © 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.