Bioinspired Electrolyte-Gated Organic Synaptic Transistors: From Fundamental Requirements to Applications

Abstract

Rapid development of artificial intelligence requires the implementation of hardware systems with bioinspired parallel information processing and presentation and energy efficiency. Electrolyte-gated organic transistors (EGOTs) offer significant advantages as neuromorphic devices due to their ultra-low operation voltages, minimal hardwired connectivity, and similar operation environment as electrophysiology. Meanwhile, ionic–electronic coupling and the relatively low elastic moduli of organic channel materials make EGOTs suitable for interfacing with biology. This review presents an overview of the device architectures based on organic electrochemical transistors and organic field-effect transistors. Furthermore, we review the requirements of low energy consumption and tunable synaptic plasticity of EGOTs in emulating biological synapses and how they are affected by the organic materials, electrolyte, architecture, and operation mechanism. In addition, we summarize the basic operation principle of biological sensory systems and the recent progress of EGOTs as a building block in artificial systems. Finally, the current challenges and future development of the organic neuromorphic devices are discussed.

1. Introduction

The fulminant development of digitization has placed great demands on the processing and presentation of information. Current digital logic computation relies heavily on the von Neumann computer architecture with separate processing and memory elements, which, however, is inadequate for future artificial intelligence due to its high energy consumption and limited parallel computing capability [1]. The biological neural networks in a human brain consist of ~1012 neurons with ~1015 interconnecting synapses. These networks can efficiently execute massive parallel information processing and simultaneously perform storage tasks with ultra-low power consumption (10 fJ/synaptic event) and fault-tolerant characteristics [2]. Since information processing and transmission between neurons rely on electrical/chemical signals mediated by ions and neurotransmitters released at the synapse in a complex network [3, 4], the development of synaptic devices with integrated signal transduction and neuron-inspired signal processing functions is highly desired to emulate the functions of biological neurons and synapses toward the upcoming artificial intelligence era [5–8].

The ideal bioinspired synaptic devices generally require: (1) low power consumption, which is the key advantages compared to the conventional von Neumann computer architecture, (2) long state retention time and stability at a given state, ensuring long-term data storage, especially for write-once–read-many times devices [9–12], and (3) ability to emulate specific typical brain-like functions, such as short-/long-term plasticity, excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and filtering characteristics. In recent decades, intense scientific efforts have focus on the realization of bioinspired (or neuromorphic) devices with adaptive properties to emulate the basic synaptic functions of neuromorphic computation and memory [7]. Several conceptual neuromorphic devices have been proposed including two-terminal memristors with metal–insulator–metal configuration [13–17] and three-terminal transistors [18]. Unlike the two-terminal memristive devices, which require separate circuits for each input, the structural nature of synaptic transistors with physically separated input and output terminals potentially allows information processing and learning to occur synchronously [19], i.e., the synaptic weight of each gate input can be tuned individually during fabrication [19, 20].