Data · dataset · 2026
Emulation of Synaptic Functions with Poly(Ionic Liquid) Heterojunction for Visual Pattern Recognition
Listed in ZivaHub
The progress in artificial intelligence has driven the development of bioinspired iontronics for neuromorphic computing, offering scalability and energy efficiency.
Description
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.
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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.
Links
Where it is published
- DOI doi.org/10.1021/acsnano.6c07651.s001 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
- From keywords
- Artificial intelligence · Chemistry · Computer Science & AI · Earth & Environmental Science · Life Sciences · Plant biology · Real and complex functions
- Inferred from text
- Image 75%
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/34018719 | 5 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].anzsrc:field:490411 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['complex functions'] |
| concepts[field].anzsrc:group:3108 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['Plant Biology'] |
| concepts[field].anzsrc:group:4602 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['artificial intelligence'] |
| concepts[field].local:field:chemistry | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · zivahub uct ac za | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/description |
| license | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/rights |
| publication_date | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |