電化學電晶體中離子與電子混合傳輸的理論探討

Abstract

類腦運算以事件驅動、並行處理及低功耗為特色，被視為突破傳統馮紐曼架構能耗瓶頸的潛力技術。實現類腦運算的關鍵元件為人工突觸，而在眾多突觸裝置中，電化學電晶體因其低功耗與非揮發特性，日益受到關注。此類元件由電解質、混合離子電子導體與金屬電極構成，透過閘極電壓調控混合導體中的導電性，以模擬生物突觸的可塑性。然而，離子與電子傳輸在方向與機制上的差異，以及兩者間的耦合效應，使得傳統等效電路模型難以全面描述其傳輸行為。目前多數電化學電晶體在類腦運算中的研究，仍以實驗方式探索最佳操作條件與材料選擇。因此，本研究建立一套模擬架構，旨在找出影響元件性能的關鍵操作條件與材料參數，並進一步解釋實驗中觀察到卻無法以現有模型合理說明的電流變化行為。 本研究構建一套有限元素法模擬框架，透過聯立求解 Nernst–Planck、Poisson及 Butler–Volmer方程，解析瞬態操作條件下混合離子電子導體通道內的離子／電子濃度與電位演變。我們模擬系統中探討兩種驅動模式：（1）傳統的閘極電壓調控與（2）閘極電流調控通道導電度。透過系統性改變離子擴散係數、元件幾何（通道長度與厚度）、閘極電流／汲極電壓、以及電解質濃度等關鍵參數，深入分析其對裝置瞬態響應的影響，進而建立設計指引。 模擬結果顯示，在電流驅動模式下，理想條件為低脈衝電流，短通道長度，適當通道厚度與小汲極電壓。其中，脈衝電流、厚度與汲極電壓影響導電度調變範圍，而通道長度主要決定響應速度。理想的人工突觸應具備單次脈衝下電導變化幅度小、經多次脈衝後電導可顯著調變，且響應時間短，以提升電化學電晶體於類腦運算中突觸權重調變的靈敏度與能效。在電壓控制模式下，需確保電解質與通道間具有低界面阻抗以縮短響應時間，而低離子擴散速率則可增強導電度的記憶性。此外，汲極電壓應控制在不大於−0.1V，以避免通道導電性產生非均勻變化。 本研究提供一套可預測電化學電晶體傳輸行為的理論模型，揭示影響突觸效能的關鍵因子，為未來高效能類腦處理器之設計提供理論依據與工程準則。

Neuromorphic computing, characterized by event-driven operation, parallel processing, and low power consumption, is considered a promising approach to overcoming the energy bottleneck inherent in the traditional von Neumann architecture. A key component in realizing neuromorphic computing is the artificial synapse. Among various types of synaptic devices, electrochemical transistors have garnered increasing attention due to their excellent low-power operation and non-volatile behavior. These devices typically consist of an electrolyte, a mixed ionic–electronic conductor, and metal electrodes. By applying a gate voltage to modulate the conductivity of the mixed conductor, they emulate the plasticity observed in biological synapses. However, due to the differences in transport direction and mechanism between ions and electrons, along with their coupling effects, traditional equivalent circuit models fail to comprehensively describe their charge transport behavior. At present, most studies on electrochemical transistors for neuromorphic applications still rely on experimental methods to explore optimal operating conditions and material selections. Therefore, this study establishes a simulation framework to identify the critical operating conditions and material parameters influencing device performance and to explain current variations observed in experiments that cannot be adequately interpreted by existing models. This research constructs a finite element simulation platform that simultaneously solves the Nernst–Planck equation, the Poisson equation, and the Butler–Volmer equation to analyze the transient evolution of ion and electron concentrations, as well as electric potential, within the channel of the mixed ionic–electronic conductor. Two driving modes are considered in the simulated system: (1) modulation of channel conductivity via gate voltage, and (2) modulation via gate current. By systematically varying key parameters—including the ionic diffusion coefficient, device geometry (channel length and thickness), gate current or drain voltage, and electrolyte concentration—we investigate their influence on the transient response of the device and develop corresponding design guidelines. Simulation results show that, under the current-driven mode, optimal conditions include low pulse current, short channel length, appropriate channel thickness, and a small drain voltage. Pulse current, thickness, and drain voltage mainly influence the range of conductivity modulation, whereas channel length primarily determines the response speed. An ideal artificial synapse should exhibit small changes in conductivity per pulse, a wide modulation range after successive pulses, and short response times to enhance the sensitivity and energy efficiency of synaptic weight modulation in neuromorphic computing. Under voltage-driven operation, low interfacial impedance between the electrolyte and the channel is required to shorten the response time, while a slow ionic diffusion rate enhances the memory retention of the conductivity state. Additionally, the drain voltage should be maintained below −0.1 V to prevent non-uniform modulation of the channel conductivity. This study provides a predictive theoretical model for charge transport in electrochemical transistors, reveals the key factors affecting synaptic performance, and offers theoretical foundations and engineering guidelines for the design of future high-performance neuromorphic processors.