Solid‐State Electrolyte Gate Transistor with Ion Doping for Biosignal Classification of Neuromorphic Computing

Biomimetic neuromorphic sensing systems, inspired by the structure and function of biological neural networks, represent a major advancement in the field of sensing technology and artificial intelligence. This review paper focuses on the development and application of electrolyte gated transistors (EGTs) as the core components (synapses and neuros) of these neuromorphic systems. EGTs offer unique advantages, including low operating voltage, high transconductance, and biocompatibility, making them ideal for integrating with sensors, interfacing with biological tissues, and mimicking neural processes. Major advances in the use of EGTs for neuromorphic sensory applications such as tactile sensors, visual neuromorphic systems, chemical neuromorphic systems, and multimode neuromorphic systems are carefully discussed. Furthermore, the challenges and future directions of the field are explored, highlighting the potential of EGT-based biomimetic systems to revolutionize neuromorphic prosthetics, robotics, and human-machine interfaces. Through a comprehensive analysis of the latest research, this review is intended to provide a detailed understanding of the current status and future prospects of biomimetic neuromorphic sensory systems via EGT sensing and integrated technologies.

Li+ electrolyte-gated transistors (EGTs) have attracted significant attention as artificial synapses because of the fast response of Li+ ion, low operating voltage, and applicability to flexible electronics. Due to the inherent nature of Li+ ion, Li+ EGTs show, however, limitations, such as poor long-term synaptic plasticity and nonlinear/nonsymmetric conductance update, which hinder the practical applications of artificial synapses. Herein, Li+ EGTs integrated with poly(vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFE) ferroelectric polymer as a channel–electrolyte interlayer are presented. Owing to the polarized domains of PVDF-TrFE, the transport of Li+ ions at the channel–electrolyte interface is accelerated, and Li+ ions effectively penetrate the channel. Moreover, the self-diffusion of Li+ ions from the channel to the electrolyte is suppressed by the downward polarized domains. Li+ EGTs, therefore, successfully demonstrate synaptic characteristics, including excitatory postsynaptic current, short-/long-term synaptic plasticity, and paired-pulse facilitation. Also, conductance update in Li+ EGTs shows a dynamic range (Gmax/Gmin) of 92.42, high linearity, and distinct stability over 100 cycles. Based on their synaptic characteristics, inference simulations using a convolution neural network for the CIFAR-10 dataset imply that Li+ EGTs are suitable as artificial synapses with an inference accuracy of 89.13%. The new methodological approach addressing modulation of ion dynamics at the interface is introduced for developing practical synaptic devices.

Due to the advantages of low‐voltage operation and memory characteristics, electrolyte‐gated transistors (EGTs) have been proposed as building blocks for neuromorphic systems. Recently, reservoir computing (RC) is attracting interests because RC utilize nonlinear system to enrich the input source into a higher dimension, whereas traditional neural network prefers linear system. RC also has the advantages of low energy consumption and fast response speed because it only trains at the readout layer. EGT is a promising candidate for RC due to tunable output current depending on input pulse voltage using its nonlinear system. Herein, we proposed poly (vinylidene fluoride‐hexafluoropropylene) (PVDF‐HFP) interlayered EGT for RC. Semicrystalline and porous morphology of PVDF‐HFP interlayer induced ion‐trapping at the interface between amorphous and crystalline regions resulted in long retention. Also, the thickness variation of the PVDF‐HFP interlayer modulates the filling capacity of trap sites, balancing ion‐transport for near‐linear weight update. We integrated near‐linear and asymmetric long‐term potentiation and depression curves into RC to elucidate the intensity difference between five frames of spatiotemporal events in dynamic vision sensor (DVS) gestures. Classification of 11 distinct gestures was successfully demonstrated using the RC framework. This work highlights compelling synergy between material‐level device physics and algorithmic architectures of neuromorphic computing.

With the development of information society, the traditional von Neumann-based computing system is facing significant challenges. The search for an intelligent computing system similar to the biological brain would be a very effective solution to the present-day von Neumann bottleneck. Electrolyte-gated transistors (EGTs) have received much attention because they can simulate biological synaptic behavior very effectively. However, large-scale EGTs arrays are still lacking because most of the existing reported EGTs use organic or liquid electrolytes, which poses a significant challenge to the current production methods for manufacturing integration using photolithography. Although synaptic transistor arrays using solid-state electrolytes have the potential for large-scale fabrication, the power consumption required for individual transistors is relatively high. In this work, an electrolyte transistor array (10 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> 10) fabricated by the photolithography process was successfully proposed, where the individual transistors in the array used a composite polyvinyl alcohol (PVA)/lignin electrolyte as the gate dielectric layer. The switching ratio of a single transistor can reach 106, and the maximum gate leakage current is 43.96 pA in the voltage range of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> 3 to 3 V. In addition, the synaptic properties, such as excitatory postsynaptic current (EPSC) and paired-pulse-facilitation (PPF), were successfully achieved. When the pulse duration is 100 ms, the energy consumption of the transistor is 0.63 nJ. The number’s dynamic memory and forgetting functions were also successfully simulated by the artificial synaptic transistor array. This work will provide a useful idea for large-scale array integration of EGTs using organic and liquid electrolytes as gate dielectric layers.

Electrolyte-gated transistors (EGTs) are typically limited to p-type operation, with stable n-type devices remaining scarce. In this study, we demonstrate high-performance n-type EGTs using a poly(benzimidazobenzophenanthroline) (BBL) polymer semiconductor gated by ionogel electrolytes. Electrochemical doping in BBL induces ion pathways in amorphous regions during initial doping, facilitating efficient electron transport. This yields exceptional device performance, including a geometry-normalized transconductance of 4.6 S cm−1, an ON/OFF ratio of ≈105, a product of electron mobility and volumetric capacitance (μC* ≈ 16.4 F cm–1 V–1 s–1), and minimal hysteresis (<0.1 V). These features surpass most reported n-type EGTs, demonstrating the importance of ion-driven electrochemical doping in BBL. Furthermore, we not only fabricate all-polymer complementary inverters, NAND, and NOR gates but also demonstrate flexible circuits by integrating n-type BBL and p-type polythiophene EGTs.

Electrolyte‐gated transistors (EGTs) are promising candidates for low‐power and energy‐efficient artificial synapses utilizing rapid ion‐mediated electrostatic and electrochemical reactions. However, the correlation between sub‐nanosecond ion transport and linear conductance modulation remains elusive when analyzed solely by macroscopic electrical characterization. Here, molecular dynamics (MD) simulations are integrated with experiments using Li‐EGTs at different ion concentrations. MD simulations quantify Li⁺ displacement, coordination environments, and ionic conductivity in poly(ethylene oxide) electrolytes, revealing suppressed Li⁺ diffusivity and reorganization of the ethylene oxide (EO) solvation structure at an [EO]:[Li⁺] ratio of 8:1. EGTs are fabricated with different EO ratios to investigate the transition regime. Electrochemical impedance spectroscopy, optical microscopy, and X‐ray diffraction confirm the MD‑predicted ion dynamic transition at 8:1 EO ratio and identify phase crystallization at the ultrahigh 2:1 ratio. Consequently, Li‐EGTs fabricated with the optimized 2:1 EO ratio successfully achieve an accuracy of 89.51% in neural network training. This study bridges atomic‑scale insights and device performance, offering a systematic, precision‑engineered framework for complex spatiotemporal ion dynamics in artificial synaptic EGTs.

Engineering of TiN/ZnO/SnO2/ZnO/Pt multilayer memristor with advanced electronic synapses and analog switching for neuromorphic computing

Memristors‐based neuromorphic devices represent emerging computing architectures to perform complex tasks by outpacing the traditional Von‐Neumann architectures in terms of speed, and energy efficiency. In this work, the resistive switching (RS) behavior of sol‐gel grown and ion‐irradiated BFO films is investigated under electrical stimulus. The Ag/BFO/FTO memristors emulate a combination of digital and analog RS behavior within a single device. The possible mechanism of analog digital hybridity is addressed by considering the formation of the conducting filament by oxygen vacancies, Ag+ ions and Schottky barrier height modulation. The ion‐irradiated BFO samples are analyzed using the Raman, XRD, and XPS studies. To uphold bioinspired synaptic actions, crucial synaptic functionalities like pair‐pulse facilitation and long‐term potentiation/depression are effectively achieved. More intricate synaptic behaviors are also demonstrated such as spike‐time‐dependent plasticity and Pavlovian classical conditioning, which represent the prominent attributes of both learning and forgetting behavior. Additionally, high pattern recognition accuracy (96.1%) is achieved in an artificial neural network simulation by using the synaptic weights of the memristors. This synergistic effect of digital and analog RS in ion‐irradiated BFO can be beneficial for the emulation of complex learning behavior as well as its incorporation into low‐power neuromorphic computing.

Electrolyte-gated organic transistors (EGOTs) are promising candidates as a new class of neuromorphic devices in hardware-based artificial neural networks that can outperform their complementary metal oxide semiconductor (CMOS) counterparts regarding processing speed and energy consumption. Several ways in which to implement such networks exist, two prominent methods of which can be implemented by nanoscopic vertical EGOTs, as we show here. First, nanoscopic vertical electrolyte-gated transistors with a donor-acceptor diketopyrrolopyrrole-terthiophene polymer as an active material can be used to reversibly switch the channel conductivity over five orders of magnitude (3.8 nS to 392 μS) and perform switching at low operation voltages down to -1 mV. Second, nanoscopic EGOTs can also mimic fundamental synaptic functions, and we show an interconnection of up to three transistors, highlighting the possibility to emulate biological nerve cells.

The memristor is a resistive switch where its resistive state is programable based on the applied voltage or current. Memristive devices are thus capable of storing and computing information simultaneously, breaking the Von Neumann bottleneck. Since the first nanomemristor made by Hewlett‐Packard in 2008, advances so far have enabled nanostructured, low‐power, high‐durability devices that exhibit superior performance over conventional CMOS devices. Herein, the development of memristors based on different physical mechanisms is reviewed. In particular, device stability, integration density, power consumption, switching speed, retention, and endurance of memristors, that are crucial for neuromorphic computing, are discussed in detail. An overview of various neural networks with a focus on building a memristor‐based spike neural network neuromorphic computing system is then provided. Finally, the existing issues and challenges in implementing such neuromorphic computing systems are analyzed, and an outlook for brain‐like computing is proposed.

To develop neuromorphic computational systems and achieve artificial intelligence, it is crucial to realize neuromorphic devices that mimic basic neural behavior. The electrolyte-gated transistors (EGTs) were fabricated using a 3-triethoxysilylpropylamine modified graphene-oxide (KH550-GO) electrolyte. The transistor exhibited good performance owing to the EDL effect of the KH550-GO electrolyte. A resistor-load inverter was also achieved with a high voltage gain of 12.4, and have low operating voltage. Furthermore, the crucial short-term synaptic plasticity(STP) functions, including excitatory post-synaptic current (EPSC) and high-pass filtering, were realized. More importantly, adaptation was emulated in our devices.

Study on aluminium oxide doping modification of indium oxide and thermoelectric properties

Nanoscale channel organic ferroelectric synaptic transistor array for high recognition accuracy neuromorphic computing

Neuromorphic computing is one promising post-Moore’s law era technology, which takes inspiration from biological brains to perform computing tasks. The human brain contains billions of neurons with trillions of synapses and as neuromorphic hardware systems scale to larger and larger sizes, the communication system used to transfer information between neuromorphic elements and traditional computers must scale to keep up. In prior work, we describe the use of a separate neuromorphic array communications controller to support low-latency, high-throughput communication between our neuromorphic systems and a traditional computer. In this work, the neuromorphic array communications controller is used to support the scaling of a neuromorphic development system which uses multiple neuromorphic processors arranged in a two-dimensional array. The neuromorphic array communications controller, along with scalable local connections, is used to create a scalable neuromorphic platform to enable the development and testing of large neuromorphic network arrays.

Printed electrolyte-gated oxide electronics is an emerging electronic technology in the low voltage regime (≤1 V). Whereas in the past mainly dielectrics have been used for gating the transistors, many recent approaches employ the advantages of solution processable, solid polymer electrolytes, or ion gels that provide high gate capacitances produced by a Helmholtz double layer, allowing for low-voltage operation. Herein, with special focus on work performed at KIT recent advances in building electronic circuits based on indium oxide, n-type electrolyte-gated field-effect transistors (EGFETs) are reviewed. When integrated into ring oscillator circuits a digital performance ranging from 250 Hz at 1 V up to 1 kHz is achieved. Sequential circuits such as memory cells are also demonstrated. More complex circuits are feasible but remain challenging also because of the high variability of the printed devices. However, the device inherent variability can be even exploited in security circuits such as physically unclonable functions (PUFs), which output a reliable and unique, device specific, digital response signal. As an overall advantage of the technology all the presented circuits can operate at very low supply voltages (0.6 V), which is crucial for low-power printed electronics applications.