Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

Abstract

Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic‐electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron‐based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low‐level and high‐level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.

Porous crystalline materials for memories and neuromorphic computing systems

“…, artificial synapses and artificial neurons) is crucial for constructing neuromorphic computing systems capable of overcoming the von Neumann bottleneck in this post-Moore's law era. 71,72,74,75,89,393,394,479,480 Up to now, various devices, including memristor, 70,72,73,481–488 flash memory, 285,489–492 EG-FET, 293,295,296,489,490,493–496 and memtransistor, 497–499 based on different functional materials, 484,500,501 such as 2D materials, 85–88,387,502–508 perovskite, 76–80,389,509,510 biomaterials, 81,82 TMO, 385,511–513 and organic materials, 71,90,514,515 have been utilized for neuromorphic devices.…”

“…EG-FETs has been widely applied for neuromorphic devices due to the small operating voltage, low energy consumption, unique ion transport similar to biological synapse, global ionic conduction property. 293,295,296,489,490,[493][494][495][496] In 2022, Alshareef et al reported first COF based EG-FET for artificial synaptic device by using COF (Tp-Azo) and PVA/LiClO 4 as semiconductor channel and gate electrolyte, respectively. 343 The COF film was fabricated via dip coating method, and the device has an in-plane gate geometry architecture.…”

Optoelectronic Synapses and Photodetectors Based on Organic Semiconductor/Halide Perovskite Heterojunctions: Materials, Devices, and Applications

“…[110] Synapses can precisely control the strength of connections (synaptic weights) between adjacent neurons through the transmission of excitatory or inhibitory neurotransmitters, which is a capacity regarded as synaptic plasticity. [111][112][113][114] Several works have reported artificial OSDs based on organic semiconductor/halide perovskite heterojunctions for the simulation of biological synaptic functions, which can be primarily divided into two-terminal optoelectronic memristors and three-terminal optoelectronic synaptic transistors. The two-terminal optoelectronic memristor is a typical metal-semiconductor-metal (MSM) structure device similar to the biological synaptic structure, which can change the conductance state by the number of electrons/ions flowing through it under optical stimuli and work as an artificial synapse.…”

Optoelectronic Synaptic Transistors Based on Solution-Processable Organic Semiconductors and CsPbCl3 Quantum Dots for Visual Nociceptor Simulation and Neuromorphic Computing

“…From another perspective, great efforts have been utilized to design artificial electronics with the capability of simulating typical functions in CNS. ,− The optoelectronic synaptic devices with multiple conductance states that effectively increase the storage density and processing efficiency are suitable for the achievement of memorizing and neuromorphic computing functions inspired by the CNS. , More the conductance states of optoelectronic synaptic devices enable higher precision in representing synaptic weights, which may lead to improved accuracy and performance in certain tasks such as pattern recognition, classification, and prediction. In addition, the optoelectronic synaptic devices with more conductance states can perform computations with fewer devices and better tolerate device variations, noise, and other nonidealities, which potentially lead to more robust and reliable computing process .…”