Short‐Term and Long‐Term Memory Functionality of a Brain‐Like Device Built from Nanoparticle Atomic Switch Networks - Oradee Srikimkaew; Saman Azhari; Deep Banerjee; Yuki Usami; Hirofumi Tanaka (Advanced Electronic Materials)
Abstract The synaptic plasticity of the Ag‐Ag2S nanoparticle‐based volatile memristor system is demonstrated. The nanoparticles self‐assemble into a network with over 103 interconnected atomic switch interfaces. Short‐term plasticity is identified by spontaneous conductance relaxation, attributed to the memristor's volatility. The conductance of the network is enhanced when a subsequent stimulus pulse arrives shortly after the previous one, analogous to the paired‐pulse facilitation in biological synapses. Furthermore, repeated pulse stimulation is used to achieve the transition from short‐term plasticity to long‐term potentiation, a process related to learning and memory formation. Remarkably, the result reveals that the lifetime of long‐term potentiation for 100‐pulse stimulation is 40 min, indicating that the device can forget newly acquired information after prolonged storage, akin to human memories. The findings provide insight into the the learning and memory abilities of atomic switch network memristors, facilitating the development of hardware‐implemented artificial neural networks.