A Conductance-Based Design Methodology for Single-Transistor Organic Electrochemical Neurons

Organic electrochemical transistors (OECTs) combine ionic and electronic transport in a single device, exhibiting dynamics that closely resemble those of biological neurons. Their intrinsic plasticity, alongside low-voltage and low-energy operation, positions them as promising candidates for hardware-level neural emulation. Notably, the conjugated polymer poly(benzimidazobenzophenanthroline) (BBL) enables the realization of a spiking neuron within only one transistor. This configuration leverages BBL’s ion-dependent response times and antiambipolar transfer characteristics to produce the non-monotonic transconductance necessary to emulate action potentials. However, a unified model accounting for the combined effects of device geometry, input stimulus, and supply voltage on neural dynamics is currently lacking, constraining their systematic use in neuromorphic systems. To address this gap, we introduce a conductance-based design methodology for single-transistor organic electrochemical neurons (ST-OECNs). By decoupling drain current into carrier velocity and charge density, the framework captures critical multi-timescale dynamics, successfully replicating the subthreshold and suprathreshold spiking regimes observed in physical devices. Furthermore, the model reveals that scaling down channel length and thickness increases oscillation frequencies into biologically relevant regimes, while channel width tunes the operating stimulus range. Ultimately, the framework leverages these geometric relationships to inform device sizing for system integration, reducing the reliance on trial-and-error fabrication loops.