Adaptive Biosensing and Neuromorphic Classification Based on an Ambipolar Organic Mixed Ionic–Electronic Conductor

Yanxi Zhang*, Eveline R.W. van Doremaele, Gang Ye*, Tim Stevens, Jun Song, Ryan C. Chiechi, Yoeri van de Burgt*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

55 Citations (Scopus)
232 Downloads (Pure)

Abstract

Organic mixed ionic–electronic conductors (OMIECs) are central to bioelectronic applications such as biosensors, health-monitoring devices, and neural interfaces, and have facilitated efficient next-generation brain-inspired computing and biohybrid systems. Despite these examples, smart and adaptive circuits that can locally process and optimize biosignals have not yet been realized. Here, a tunable sensing circuit is shown that can locally modulate biologically relevant signals like electromyograms (EMGs) and electrocardiograms (ECGs), that is based on a complementary logic inverter combined with a neuromorphic memory element, and that is constructed from a single polymer mixed conductor. It is demonstrated that a small neuromorphic array based on this material effects high classification accuracy in heartbeat anomaly detection. This high-performance material allows for straightforward monolithic integration, which reduces fabrication complexity while also achieving high on/off ratios with excellent ambient p- and n-type stability in transistor performance. This material opens a route toward simple and straightforward fabrication and integration of more sophisticated adaptive circuits for future smart bioelectronics.

Original languageEnglish
Article number2200393
Number of pages9
JournalAdvanced materials
Volume34
Issue number20
DOIs
Publication statusPublished - 19-May-2022

Keywords

  • adaptive sensing
  • ambipolar inverters
  • neuromorphic computing
  • organic mixed ionic–electronic conductors

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