Novel transistor paves the way for advanced neuromorphic computing

In a study published in the journal Advanced Intelligent Systems, researchers from Japan led by Associate Professor Tohru Higuchi at Tokyo University of Science (TUS) have developed a redox reaction-based ion-gating reservoir (redox-IGR) that can achieve a high number of reservoir states. Physical systems, known as ‘reservoirs’, are designed to emulate neural networks and meet the need for improved computational efficiency and speed. The development of an ion-gating transistor with improved reservoir states and short-term memory capabilities based on redox reactions opens up the possibility of utilising redox-based ionic devices for high-performance neuromorphic computing.

To enhance the efficiency and speed of data-driven tasks, researchers are exploring the possibility of recognising complex patterns and relationships inherent in data for the development of high-performance ‘neuromorphic’ computing technology. This approach aims to replicate the brain’s ability to process information in a parallel and interconnected manner, by constructing a network of nodes capable of transforming data into high-dimensional representations suitable for complex tasks like pattern recognition, prediction and classification.

Physical reservoirs resembling neural networks receive and interact with input signals or data, and their constituent elements, namely neurons and their interconnections, change over time. These reservoir states represent the physical system at a specific point and play a vital role in transforming input signals into high-dimensional representations. The researchers from TUS have now advanced the possibility of translating higher-performance neuromorphic computing technology into a reality.

Ion-gating reservoirs consist of gate, drain and source electrodes and are separated by an electrolyte that acts as a medium to control the flow of ions. Applying a voltage to the gate electrode triggers a redox reaction within the channel connecting the source and drain electrodes, resulting in a drain current that can be modulated. Converting the time-series datasets into gate voltages can therefore allow the corresponding output currents to serve as distinct reservoir states.

The researchers used lithium-ion conducting glass ceramic (LICGC) as an electrolyte. In LICGC, the Li+ ions travel faster compared to the channel, leading to the generation of two output currents — the drain current and a gate current, doubling the number of reservoir states. The different rates of ion transport in the channel and the electrolyte result in a delay in response of the drain current compared to the gate current. This delayed response enables short-term memory capabilities, allowing the reservoir to retain and utilise information from past inputs.

“We have successfully reproduced electrical characteristics similar to those of neural circuits by utilising redox reactions induced by the insertion and desorption of Li+ ions into the LixWO3 thin film,” Higuchi said.

The device achieved 40 reservoir states (20 from the drain current and 20 from the gate current), outperforming other physical reservoirs such as memristors and spin torque devices when solving second-order nonlinear dynamic equations. “The developed system has the potential to become a general-purpose technology that will be implemented in a wide range of electronic devices including computers and cell phones in the future,” Higuchi said.

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