Effects of Mg Doping to a LiCoO2 Channel on the Synaptic Plasticity of Li Ion-Gated Transistors

Sub-Millisecond and Energy-Efficient Electrochemical Synaptic Transistors with a Partially Reduced Graphene Oxide Channel

Designed artificial synaptic transistors, which emulate the functions of biological synapses, are intended to achieve information processing and computation, showcasing their promise in advancing artificial intelligence. Herein, we propose a synaptic transistor composed of a partially reduced graphene oxide (prGO) channel and a Nafion electrolyte, operating based on electrochemical reactions of the prGO channel, which are assisted by protons through the Nafion electrolyte. After electrical reduction of a pristine GO channel to the prGO channel by sweeping the drain voltage, the transistor exhibits over 200 distinct conductance states under applications of short gate voltage pulses down to 500 μs width, giving rise to a low energy consumption of 10-50 pJ per gate pulse. Using highly linear and symmetric long-term potentiation and depression characteristics, an image recognition accuracy using an artificial neural network based on a two-layer perceptron model is calculated to be 90%. If gate current pulses are used, the image recognition accuracy further increases to 94%, because of the improved linearity and symmetry of the conductance change. The transistor also exhibits short-term plasticity, such as paired-pulse facilitation and spike-timing-dependent plasticity, with time ranges of less than a few tens of milliseconds. These superior synaptic properties of the Nafion/prGO transistors will offer a remarkable paradigm for the development of neuromorphic computation architectures.

Oxide Ionic Neuro-Transistors for Bio-inspired Computing

Current computing systems rely on Boolean logic and von Neumann architecture, where computing cells are based on high-speed electron-conducting complementary metal-oxide-semiconductor (CMOS) transistors. In contrast, ions play an essential role in biological neural computing. Compared with CMOS units, the synapse/neuron computing speed is much lower, but the human brain performs much better in many tasks such as pattern recognition and decision-making. Recently, ionic dynamics in oxide electrolyte-gated transistors have attracted increasing attention in the field of neuromorphic computing, which is more similar to the computing modality in the biological brain. In this review article, we start with the introduction of some ionic processes in biological brain computing. Then, electrolyte-gated ionic transistors, especially oxide ionic transistors, are briefly introduced. Later, we review the state-of-the-art progress in oxide electrolyte-gated transistors for ionic neuromorphic computing including dynamic synaptic plasticity emulation, spatiotemporal information processing, and artificial sensory neuron function implementation. Finally, we will address the current challenges and offer recommendations along with potential research directions.