- Researchers develop an ionic device utilising redox reactions as a major step towards using physical reservoir computing.
- 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.
Japanese researchers have now advanced the possibility of translating higher-performance neuromorphic computing technology into a reality.
Led by Associate Professor Tohru Higuchi at Tokyo University of Science (TUS), along with Tomoki Wada and Daiki Nishioka from TUS, and Dr. Takashi Tsuchiya and Dr. Kazuya Terabe from National Institute for Materials Science (NIMS), Japan, have developed an ionic device, utilising redox reactions, as a major step towards using physical reservoir computing.
“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,” Dr. Higuchi said.
Physical systems known as “reservoirs” are designed to emulate neural networks and meet the need for improved computational efficiency and speed.
Overcoming the previous issues with compatibility, performance, and integration of such reservoir systems, researchers developed an ion-gating transistor with improved reservoir states and short-term memory capabilities based on redox reactions.
With major breakthroughs in artificial intelligence, image recognition, and object detection, the field of computing has witnessed a remarkable revolution in recent times.
Being a data-driven field, the efficient analysis and processing of large and complex datasets is of utmost importance in computing.
Replicating brain’s ability
To enhance the efficiency and speed of data-driven tasks, researchers are exploring the possibility of recognising complex patterns and relationships inherent in the 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 doing so, it seeks to construct a network of nodes capable of transforming data into high-dimensional representations suitable for complex tasks like pattern recognition, prediction, and classification.
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 precisely modulated.
Converting the time-series datasets into gate voltages can thus allow the corresponding output currents to serve as distinct reservoir states.
Explaning the project
The researchers used lithium (Li+) 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 ー the drain current and an additional gate current, effectively doubling the number of reservoir states.
Moreover, 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. The delayed response enables short-term memory capabilities within the system, allowing the reservoir to retain and utilise information from past inputs, a crucial requirement for physical reservoirs.
To fabricate this device, the researchers deposited a 200nm thick film of lithium cobalt oxide (LiCoO2) onto a 0.15mm thick LICGC substrate. The gate electrode was composed of a thin film of Li-ion/platinum (Pt), while Pt thin films were used for the drain and source electrodes. The channel connecting the drain and source electrodes consisted of a 100nm thick tungsten (VI) oxide (WO3) thin film.
“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,” Dr. Higuchi said.
He added that the ionic device achieved a total of 40 reservoir states (20 from the drain current and 20 from the gate current) and outperformed other physical reservoirs such as memristors and spin torque devices when solving second-order nonlinear dynamic equations.