Organic Synaptic Transistors: The Evolutionary Path from Memory Cells to the Application of Artificial Neural Networks

Abstract

The progress of neural synaptic devices is experiencing an era of explosive growth. Given that the traditional storage system has yet to overcome the von Neumann bottleneck, it is critical to develop hardware with bioinspired information processing functions and lower power consumption. Transistors based on 2D materials, metal oxides, and organic materials have been adopted to mimic the synapse of a human brain, due to their high plasticity, parallel computing, integrated storage, and system information processing. Among these materials used to build transistors, organic semiconductors are considered to be the most promising candidate for neural synaptic devices and bio‐electronics, owing to their easy processing, mechanical flexibility, low cost, good bio‐compatibility, and ductility. This review focuses on the recent advances in organic synaptic devices with various structures, materials, and working mechanisms. The applications of artificial neural networks that integrate multiple organic synaptic transistors are also concretely discussed. Finally, the challenges that organic synaptic devices currently face are discussed and future developments are forecast.

Low‐Energy‐Consumption and Electret‐Free Photosynaptic Transistor Utilizing Poly(3‐hexylthiophene)‐Based Conjugated Block Copolymers

“…Next, the photosynaptic transistor was tested with different intensities of 450 nm light (Figure 4c ) or different light wavelengths with an intensity of 22.0 mW cm −2 (Figure 4d ). The PPF index can be fitted by two exponential decay curves as follows [ 45 ] where τ 1 and τ 2 are the characteristic relaxation of the slow and rapid phases. The PPF index increased from 1.26, 1.31, to 1.38 as the light intensities were increased from 2.20, 18.0, to 22.0 mW cm −2 , respectively.…”

Bio‐Inspired 3D Artificial Neuromorphic Circuits

“…Unlike two-terminal memristors, the basic transistor structure usually adopts multiple-terminal configuration, and superior weight controllability as well as advanced synaptic functions of nonvolatile in-memory computing devices can be realized by additional input terminals and versatile device configurations. [221][222][223][224][225][226][227][228][229][230] It also bears mentioning that we should distinguish these transistor-based nonvolatile in-memory computing system from the volatile in-memory computing primitives based on, for example, dynamic random access memory (DRAM) and static random access memory (SRAM). Although the CMOS-based DRAM and SRAM are mature commercialized techniques, their slow scaling trend and limited capacitance have motivated the research focus toward nonvolatile in-memory computing technology.…”

Back‐End CMOS Compatible and Flexible Ferroelectric Memories for Neuromorphic Computing and Adaptive Sensing

“…[ 152 ] Organic neuromorphic devices for artificial neural network application have been discussed in detail in some recent works. [ 23,53,153 ]…”

“…[152] Organic neuromorphic devices for artificial neural network application have been discussed in detail in some recent works. [23,53,153] Ferroelectric polymers are promising for vital parameter sensing like pulse pressure and change in body temperature. [154] By combining their piezo-and pyroelectric properties, multiple parameter sensing through a single sensing element is possible.…”