Ion-Movement-Based Synaptic Device for Brain-Inspired Computing

As the amount of data has grown exponentially with the advent of artificial intelligence and the Internet of Things, computing systems with high energy efficiency, high scalability, and high processing speed are urgently required. Unlike traditional digital computing, which suffers from the von Neumann bottleneck, brain-inspired computing can provide efficient, parallel, and low-power computation based on analog changes in synaptic connections between neurons. Synapse nodes in brain-inspired computing have been typically implemented with dozens of silicon transistors, which is an energy-intensive and non-scalable approach. Ion-movement-based synaptic devices for brain-inspired computing have attracted increasing attention for mimicking the performance of the biological synapse in the human brain due to their low area and low energy costs. This paper discusses the recent development of ion-movement-based synaptic devices for hardware implementation of brain-inspired computing and their principles of operation. From the perspective of the device-level requirements for brain-inspired computing, we address the advantages, challenges, and future prospects associated with different types of ion-movement-based synaptic devices

Implementation of threshold- and memory-switching memristors based on electrochemical metallization in an identical ferroelectric electrolyte

Abstract The use of an identical electrolyte in electrochemical metallization (ECM)-based neuron and synaptic devices has not yet been achieved due to their different resistive-switching characteristics. Herein, we describe ECM devices comprising the same ferroelectric PbZr0.52Ti0.48O3 (PZT) electrolyte, which can sustain both neuron and synaptic behavior depending on the identity of the active electrode. The Ag/PZT/La0.8Sr0.2MnO3 (LSMO) threshold switching memristor shows abrupt and volatile resistive switching characteristics, which lead to neuron devices with stochastic integration-and-fire behavior, auto-recovery, and rapid operation. In contrast, the Ni/PZT/LSMO memory switching memristor exhibits gradual, non-volatile resistive switching behavior, which leads to synaptic devices with a high on/off ratio, low on-state current, low variability, and spike-timing-dependent plasticity (STDP). The divergent behavior of the ECM devices is attributed to greater control of cation migration through the ultrathin ferroelectric PZT. Thus, ECM devices with an identical ferroelectric electrolyte offer promise as essential building blocks in the construction of high-performance neuromorphic computing systems

Graphene/Pentacene Barristor with Ion-Gel Gate Dielectric: Flexible Ambipolar Transistor with High Mobility and On/Off Ratio

High-quality channel layer is required for next-generation flexible electronic devices. Graphene is a good candidate due to its high carrier mobility and unique ambipolar transport characteristics but typically shows a low on/off ratio caused by gapless band structure. Popularly investigated organic semiconductors, such as pentacene, suffer from poor carrier mobility. Here, we propose a graphene/pentacene channel layer with high-k ion-gel gate dielectric. The graphene/pentacene device shows both high on/off ratio and carrier mobility as well as excellent mechanical flexibility. Most importantly, it reveals ambipolar behaviors and related negative differential resistance, which are controlled by external bias. Therefore, our graphene/pentacene barristor with ion-gel gate dielectric can offer various flexible device applications with high performances