Electrochemical Ionic Synapses: Progress and Perspectives

Abstract

Artificial neural networks based on crossbar arrays of analog programmable resistors can address the high energy challenge of conventional hardware in artificial intelligence applications. However, state‐of‐the‐art two‐terminal resistive switching devices based on conductive filament formation suffer from high variability and poor controllability. Electrochemical ionic synapses are three‐terminal devices that operate by electrochemical and dynamic insertion/extraction of ions that control the electronic conductivity of a channel in a single solid‐solution phase. They are promising candidates for programmable resistors in crossbar arrays because they have shown uniform and deterministic control of electronic conductivity based on ion doping, with very low energy consumption. Here, the desirable specifications of these programmable resistors are presented. Then, an overview of the current progress of devices based on Li+, O2−, and H+ ions and material systems is provided. Achieving nanosecond speed, low operation voltage (≈1 V), low energy consumption, with complementary metal–oxide–semiconductor compatibility all simultaneously remains a challenge. Toward this goal, a physical model of the device is constructed to provide guidelines for the desired material properties to overcome the remaining challenges. Finally, an outlook is provided, including strategies to advance materials toward the desirable properties and the future opportunities for electrochemical ionic synapses.

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

“…Recently, a device family of nonvolatile three-terminal programmable resistors for deep learning applications has emerged. 97 This device class, often referred to as ECRAM (Electrochemical Random-Access Memory), ENODe (Electrochemical Neuromorphic Device), or EIS (Electrochemical Ionic Synapse), relies on controlled intercalation of dopant ions in a semiconductor channel 98−111 (Figure 9a). In essence, ions are shuttled back and forth between an ion reservoir (also performing the role of gate) and a channel, where the ions behave as dopants in response to the application of positive or negative voltage pulses to the reservoir with respect to the channel.…”

Direct measurements of size-independent lithium diffusion and reaction times in individual polycrystalline battery particles

“…I is the electrochemical current, t is the time, B is the Biot number, r is the radius of a particle, R is the gas constant, T is temperature, and Q is the integrated electrochemical charge during PITT. Equation (2) shows the relation between the j0, DLi and B. The term 𝝏𝑽 𝝏𝑪 , where V is the electrochemical potential and C the lithium concentration, is determined from the slope of the voltage vs. Li concentration curves for NMC532 composite electrode in a coin cell cycled at C/10 (Fig.…”

Epitaxial La0.5Sr0.5MnO3‐δ Bipolar Memristive Devices with Tunable and Stable Multilevel States

“…[3,10] On top of that, VCMs can be used for neuromorphic computing, where the device itself shows the behavior that is typically hardcoded in most AI applications, and hence, VCMs may present larger storage density and lower power consumption in applications such as those involving AI. [11][12][13] In our previous work, we studied pure LaMnO 3+δ , and proved that it was possible to change the resistance state up to two orders of magnitude by oxygen drift and by the concomitant redox reactions taking place at the materials' surface, as experimentally proven by combining conductive atomic force microscopy with X-ray photoemission electron spectroscopy Valence change memories are novel data storage devices in which the resistance is determined by a reversible redox reaction triggered by voltage. The oxygen content and mobility within the active materials of these devices play a crucial role in their performance.…”

“…[ 3,10 ] On top of that, VCMs can be used for neuromorphic computing, where the device itself shows the behavior that is typically hardcoded in most AI applications, and hence, VCMs may present larger storage density and lower power consumption in applications such as those involving AI. [ 11–13 ]…”