Organic Electrochemical Transistors for Neuromorphic Bioelectronics: Materials and Manufacturing Technologies
Neuromorphic bioelectronics operate in soft, hydrated environments where ionic flux, electronic transport, and molecular reconfiguration jointly encode history-dependent computation. Organic mixed ionic-electronic conductors provide a materials platform that supports concurrent ionic and electronic transport within the same bulk medium, enabling conductance to evolve continuously under electrochemical stimuli. This review examines organic electrochemical transistors as a unifying device architecture in which electrochemical gating induces volumetric (de)doping, coupling channel geometry and electrolyte accessibility to ionic time constants, nonlinearity, retention or volatility, and energy dissipation. Materials coverage includes conducting polymers such as poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT:PSS), glycolated conjugated semiconductors, donor-acceptor mixed conductors enabling n-type and complementary operation, and semiconducting hydrogels and composites engineered for aqueous stability and tissue-like mechanical compliance. Manufacturing strategies ranging from microfabrication and lithographic patterning to printing-based additive manufacturing (AM) and hybrid process flows are discussed, with emphasis on their roles in defining planar and vertical device architectures and electrolyte integration. Neuromorphic functionalities are analyzed across hierarchical levels, spanning synaptic primitives including excitatory postsynaptic current, short-term plasticity, long-term plasticity, and spike-timing-dependent plasticity, as well as artificial neurons, reservoir computing, and closed-loop biohybrid sensing-processing interfaces.
Authors
- Tao Zhou (ORCID: https://orcid.org/0000-0002-6507-8912)
- Jihyang Park
- Yueqi Deng
- Jiashu Ren
- Hyunjin Lee
- Xinyi Wang
Institutions
- Pennsylvania State University (US)
Publication Details
- Journal
- Advanced Science
- Published
- 2026-09-16
- DOI
- https://doi.org/10.1002/advs.77655
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- National Institutes of Health