2026-10-06T07:27:47Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34018719 2026-09-29T00:07:05Z category_1 category_915 category_16 category_873 category_734 category_931 category_133 portal_63 item_type_6 month_year_09_2026

Emulation of Synaptic Functions with Poly(Ionic Liquid) Heterojunction for Visual Pattern Recognition Falihah Balqis (12505004) Jin Pyo Lee (2375005) Zhenxiang Xing (14236516) Hui Wang (30400) Tupei Chen (3192468) Rong Ji (1496803) Pooi See Lee (1419415) Biophysics Physical Sciences not elsewhere classified Physiology Chemical Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Plant Biology tunable gradual formation many mechanisms stop highly delocalized charge convolutional image processing based iontronic devices also successfully applied artificial neural network ionic depletion layer visual pattern recognition heterojunction artificial synapse ionic artificial synapses artificial intelligence recognition hardware efficient pattern work highlights synaptic functions submillisecond biases solvent independence sensitive response offering scalability neuromorphic computing millisecond pulses memory computing large ions extracted parameters complex functions bioinspired iontronics 10 μs The progress in artificial intelligence has driven the development of bioinspired iontronics for neuromorphic computing, offering scalability and energy efficiency. Ionic-liquid-based iontronic devices have emerged as capable of emulating the complex functions of neurons and synapses. However, many mechanisms stop at millisecond pulses to trigger ion transport spikes. To open possibilities toward fast in-memory computing, we report a poly(ionic liquid)s (PILs) heterojunction artificial synapse with a sensitive response to submillisecond biases. It exhibits bidirectional modulation driven by voltage-tunable gradual formation and destruction of an ionic depletion layer at the interface. The device-extracted parameters are implemented in an image recognition task using an artificial neural network, resulting in 90% accuracy. It is also successfully applied to perform convolutional neural network inference and convolutional image processing. The solvent independence of PILs facilitates thermal stability while showcasing low energy consumption of 2.96 fJ per spike under 10 μs of 5 mV voltage pulse by leveraging the highly delocalized charge of large ions. This work highlights the reliability of all-ionic artificial synapses as an energy-efficient pattern-recognition hardware. 2026-09-28T00:00:00Z Text Journal contribution 10.1021/acsnano.6c07651.s001 https://figshare.com/articles/journal_contribution/Emulation_of_Synaptic_Functions_with_Poly_Ionic_Liquid_Heterojunction_for_Visual_Pattern_Recognition/34018719 CC BY-NC 4.0