Publication date: 15th December 2025
Organic electrochemical transistors (OECTs) are emerging as powerful platforms for neuromorphic hardware due to their intrinsic coupling between ionic motion and electronic transport, which enables low-voltage operation and rich temporal dynamics. Recent studies have demonstrated that n-doped poly(benzodifurandione) (nPBDF)-based OECTs exhibit stable synaptic plasticity and can be successfully implemented in artificial neural network simulations, validating the neuromorphic application potential of this material system.
Building on this foundation, our work focuses on uncovering the physical origin of neuromorphic dynamics in OECTs from a time-scale perspective. In our previous Advanced Materials study, impedance spectroscopy was employed to identify characteristic ionic and electronic time constants, establishing a frequency-domain framework for understanding memory and dynamic responses in OECTs. Extending this approach, transient gate- and drain-current analyses (Organic Electronics) reveal that vertical ionic diffusion governs the intrinsic response time, while lateral electronic transport modulates the transient evolution under different bias conditions.
This separation of ionic and electronic time scales provides a unified physical picture for key neuromorphic behaviors, including pulse facilitation, rate-dependent hysteresis, and conductance plasticity. Furthermore, a recently proposed theoretical model of a single-OECT self-sustained oscillator demonstrates that neuron-like spiking can emerge solely from the intrinsic nonlinearity and ion–electron dynamics of OECTs, without external amplifiers.
Together, existing material demonstrations, time-scale-resolved mechanistic understanding, and oscillator-based neuron models indicate a clear pathway toward experimentally realizing OECT-based neuron devices. These insights establish design principles for neuromorphic circuits where memory and computation emerge directly from mixed ionic–electronic transport physics.
This work was funded by the European Research Council (ERC) via Horizon Europe Advanced Grant, grant agreement nº 101097688 (“PeroSpiker”)
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