Electrolyte-gated transistors with good retention for neuromorphic computing

Li, Yue; Han, Xu; Lu, Jikai; Wu, Zuheng; Wu, Shuyu; Zhang, Xumeng; Liu, Qi; Shang, Dashan

doi:10.1063/5.0082061

Cited by 12 publications

(5 citation statements)

References 20 publications

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“…The average channel conductance change of Li-EGT is proportional to the width of write pulse (Figure 3e), which is consistent with the charge control mechanism of Li-EGT. [24,39] Currently, metal-oxide-based EGTs usually have a write-read delay at several seconds. [16,18,21] In contrast, the fabricated Li-EGT exhibits a competitive write-read delay of 3 ms.…”

Section: Resultsmentioning

confidence: 99%

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Li‐Ion‐Based Electrolyte‐Gated Transistors with Short Write‐Read Delay for Neuromorphic Computing

Han

Fang

et al. 2022

Adv Elect Materials

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The hardware implementation of artificial neural networks requires synaptic devices with linear and high‐speed weight modulation. Memristors as a candidate suffer from excessive write variation and asymmetric resistance modulation that inherently rooted in their stochastic mechanisms. Thanks to a controllable ion intercalation/deintercalation mechanism, electrolyte‐gated transistors (EGTs) hold prominent switching linearity and low write variation, and thus have been the promising alternative for synaptic devices. However, the operation frequency of EGTs is seriously limited by the time that is required for the state stabilization, that is, the write‐read delay after each set/reset operation. Here, a Li‐ion‐based EGT (Li‐EGT) with write‐read delay of 3 ms along with multistates, low energy consumption, and quasi‐linear weight update is introduced. The origin of the short write‐read delay of the device is attributed to the permeable interface between electrolyte and gate electrode. Leveraging the Li‐EGT characteristic, near‐ideal accuracy (≈94%) on handwritten digital image data set has been achieved by the corresponding neural network simulation. These results provide an insight into the development of Li‐EGTs for high‐speed neuromorphic computing.

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Section: Resultsmentioning

confidence: 99%

“…The average channel conductance change of Li‐EGT is proportional to the width of write pulse (Figure 3e), which is consistent with the charge control mechanism of Li‐EGT. [ 24,39 ]…”

Section: Resultsmentioning

confidence: 99%

Li‐Ion‐Based Electrolyte‐Gated Transistors with Short Write‐Read Delay for Neuromorphic Computing

Han

Fang

et al. 2022

Adv Elect Materials

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“…While organic ECRAMs allow for high-speed operation, their on-chip integration poses challenges [ 79 ]. In contrast, 2D materials and metal oxides tend to exhibit slow ion kinetics [ 46 , 78 , 80 ]. MXenes are known for their high noise levels and suboptimal linearity.…”

Section: Methodsmentioning

confidence: 99%

Electrochemical random-access memory: recent advances in materials, devices, and systems towards neuromorphic computing

Kwak,

Kim,

Jeon

et al. 2024

Nano Convergence

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Artificial neural networks (ANNs), inspired by the human brain's network of neurons and synapses, enable computing machines and systems to execute cognitive tasks, thus embodying artificial intelligence (AI). Since the performance of ANNs generally improves with the expansion of the network size, and also most of the computation time is spent for matrix operations, AI computation have been performed not only using the general-purpose central processing unit (CPU) but also architectures that facilitate parallel computation, such as graphic processing units (GPUs) and custom-designed application-specific integrated circuits (ASICs). Nevertheless, the substantial energy consumption stemming from frequent data transfers between processing units and memory has remained a persistent challenge. In response, a novel approach has emerged: an in-memory computing architecture harnessing analog memory elements. This innovation promises a notable advancement in energy efficiency. The core of this analog AI hardware accelerator lies in expansive arrays of non-volatile memory devices, known as resistive processing units (RPUs). These RPUs facilitate massively parallel matrix operations, leading to significant enhancements in both performance and energy efficiency. Electrochemical random-access memory (ECRAM), leveraging ion dynamics in secondary-ion battery materials, has emerged as a promising candidate for RPUs. ECRAM achieves over 1000 memory states through precise ion movement control, prompting early-stage research into material stacks such as mobile ion species and electrolyte materials. Crucially, the analog states in ECRAMs update symmetrically with pulse number (or voltage polarity), contributing to high network performance. Recent strides in device engineering in planar and three-dimensional structures and the understanding of ECRAM operation physics have marked significant progress in a short research period. This paper aims to review ECRAM material advancements through literature surveys, offering a systematic discussion on engineering assessments for ion control and a physical understanding of array-level demonstrations. Finally, the review outlines future directions for improvements, co-optimization, and multidisciplinary collaboration in circuits, algorithms, and applications to develop energy-efficient, next-generation AI hardware systems.

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“…As a strongly correlated transition metal oxide, NbO x has an intrinsic low conductance level and sensitivity to ion doping, allowing the system to be easily modulated under ion doping with lower power consumption. [34][35][36][37] Figure 2b shows the top view of the device under an optical microscope, and the crosssectional transmission electron microscope (TEM) images can be seen in Figure 2c. The detailed process of device fabrication is presented in Experimental Section.…”

Section: Tunable Ion Dynamics and Plasticity Of The Ion-modulated Mem...mentioning

confidence: 99%

Neuromorphic Artificial Vision Systems Based on Reconfigurable Ion‐Modulated Memtransistors

Zhen

Zhang

Liu

et al. 2023

Advanced Intelligent Systems

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Conventional vision systems suffer from lots of data handling between memory and processing units. Inspired by how humans recognize noisy images and the flexible modulation on the timescale of ion dynamics inside an emerging memtransistor, a novel neuromorphic vision system is reported based on the ion‐modulated memtransistors. By controlling the ion‐doping processes under adequate stimuli strengths, both short‐term and long‐term ion dynamics can be utilized to deliver energy‐efficient data processing. When dealing with image reconstructions, the short‐term accumulation effect of the device can help filter noises in a set of received noisy images while enhancing the original pattern information. The increased contrast can help distinguish the actual contents. To demonstrate systematic performances with the reconfiguration of devices, the nonlinear relationship between channel conductance variation and the amplitude of gate pulses into the network‐level simulation is extracted. Also, with the nonvolatile conductance change characteristic, the task of recognizing noisy images is performed to verify the versatility of ion‐modulated memtransistors in the neuromorphic artificial vision systems. An interactive preprint version of the article can be found here: https://doi.org/10.22541/au.167939089.96499861/v1

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Electrolyte-gated transistors with good retention for neuromorphic computing

Cited by 12 publications

References 20 publications

Li‐Ion‐Based Electrolyte‐Gated Transistors with Short Write‐Read Delay for Neuromorphic Computing

Li‐Ion‐Based Electrolyte‐Gated Transistors with Short Write‐Read Delay for Neuromorphic Computing

Electrochemical random-access memory: recent advances in materials, devices, and systems towards neuromorphic computing

Neuromorphic Artificial Vision Systems Based on Reconfigurable Ion‐Modulated Memtransistors

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