Abstract Processing-in-memory (PIM) is gaining tremendous research and commercial
interest because of its potential to replace the von Neumann bottleneck in current computing …

Synaptic devices store the synaptic weight in spiking neural networks (SNNs). However,
because synaptic devices are based on memory cells, their synaptic weights are vulnerable …

An increase in the demand for artificial intelligence is leading to advanced research in the
field of neuromorphic systems, which imitate human brain functions with the hope of …

Thin-film transistor (TFT)-type synaptic devices with poly-Si channels have the benefits of
compatibility with the CMOS process, high reliability, and low power consumption. However …

Compute‐in‐memory (CIM) technology based on emerging nonvolatile memories (NVMs)
has shown promise in enhancing artificial intelligence applications by integrating …

In asynchronous Spiking Neural Networks (SNNs), the voltage division between passive
resistive random-access memory (RRAM) arrays and neuron circuits presents a significant …

A NOR-type flash array is proposed as a synaptic device array for on-chip training
neuromorphic systems. Compared to the previously proposed AND-type array, the …

A novel method of analyzing the dynamic read variation (equivalent gate bias deviation [()]
over a wide gate-voltage range of devices is proposed and applied to CMOS-compatible …

Recently, spiking neural networks (SNNs) have garnered significant attention owing to their
high energy efficiency, leading to considerable research advancements. These …

The growing interest in artificial intelligence has accelerated research in neuromorphic
systems, which aim to replicate the functions of the human brain with the hope of increasing …