Ionic transistor for meuromorphic computing

The ions in question are lithium, its ‘gate oxide’ is a solid lithium-ion-conducting glass-ceramic electrolyte, and its channel is lithium tungsten oxide that changes conductivity as ions are inserted.

Physically, it is built around the electrolyte in the form of a 150μm thick ion-conducting glass-ceramic substrate (see diagram). 200nm of lithium cobalt oxide (LiCoO2) gate ‘electrode’ is deposited on one side, topped by a 50nm platinum current collector (not shown).

On the other side of the glass-ceramic substrate, 50nm platinum patches are used for drain and source electrodes, over which was deposited 100nm of lithium tungsten oxide for the channel.

“Applying a voltage to the gate electrode triggers a redox [reduction-oxidation] reaction within the channel connecting the source and drain electrodes, resulting in a drain current that can be precisely modulated,” according to the university.

The team is proposing it’s transistor as a ‘reservoir’ for reservoir neuromorphic computing.

“We have 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,” said lead researcher Tohru Higuchi.

A total of 40 reservoir states were achieved, 20 from the drain current and 20 from the gate current.

“It outperformed other physical reservoirs such as memristors and spin torque devices when solving second-order nonlinear dynamic equations,” according to the university. “Most notably, the non-linearity, the short-term memory capabilities and the high number of reservoir states enabled the device to make predictions with a low mean square prediction error of 0.163 in the second-order nonlinear autoregressive moving average task [NARMA2] – a benchmark for evaluating a reservoir system in performing complex nonlinear operations and predicting the future value of a time-series input based on its past values of both input and output.”

Tokyo University of Science worked with the NIMS, the National Institute for Materials Science in Japan.

To find out more, read ‘A redox-based ion-gating reservoir, utilizing double reservoir states in drain and gate nonlinear responses‘ published in Advanced Intelligent Systems – full paper available without payment.