Neuromodulator‐Inspired Gate‐Tunable Tetracene–Metal Oxide Phototransistor for Adaptive Optoelectronic Memory and Neuromorphic Computing
ABSTRACT We present a neuromodulator‐inspired organic–inorganic hybrid phototransistor that enables programmable optoelectronic memory and synaptic plasticity via dual optical–electrical modulation. The device integrates a tetracene‐based photoactive polymer blend with an indium gallium zinc oxide (IGZO) thin‐film transistor, forming a type‐II heterojunction where photogenerated electrons transfer to the IGZO channel while holes are stored in deep trap states within the organic layer. These trapped charges induce persistent photogating, modulating channel conductance analogous to synaptic potentiation. Trap‐dynamics modeling reveals that deep traps govern long‐term retention, whereas shallow traps allow rapid adaptation. Gate bias further reshapes the trapped‐charge distribution and tunes trapping–detrapping kinetics, achieving programmable transitions between short‐term plasticity and long‐term memory. Negative gate bias reinforces conductance retention (dopamine‐like potentiation), whereas positive bias accelerates relaxation (serotonin‐like depression), enabling polarity‐controlled switching between analog learning, long‐term storage, and controlled forgetting. Operating below 5 µW·cm −2 , the device supports weak‐light imaging (0.5 µW·cm −2 ), memory retention beyond 5000 s, and spatiotemporal trajectory tracking via transient charge storage. A device‐calibrated artificial neural network using differential synapse pairs achieves >90% MNIST classification accuracy with near‐linear weight updates over hundreds of cycles. These results demonstrate an energy‐efficient platform unifying sensing, memory, and computation for neuromodulator‐inspired in‐sensor neuromorphic processing.
Institutions
- Oregon State University (US)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2026-05-19
- DOI
- https://doi.org/10.1002/adfm.75942
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation