Polymer Electrolyte-Gated Organic Electrochemical Transistors for Bioinspired Neuromorphic Computing

Organic electrochemical transistors (OECTs) are compelling artificial synapses because mixed ionic–electronic coupling and transport enables low-voltage, analog weight updates that mirror biological plasticity. Here, we engineered solid-state, polymer electrolyte-gated vertical OECTs (vOECTs) and elucidate how electrolyte molecular weight influences synaptic dynamics. Using Pg2T-T as the redox-active channel and pDADMAC polymer electrolytes spanning low- (~100 k), medium- (~300 k), and high- (~500 k) molecular weights, cyclic voltammetry reveals reversible Pg2T-T redox, while peak separation and current density systematically track ion transport kinetics. Increasing electrolyte molecular weight enlarges the transfer curve hysteresis (memory window ΔV_mem from ~0.15 V to ~0.50 V) but suppresses on-current, consistent with slower, more confining ion motion and stabilized partially doped states. Devices exhibit rich short- and long-term plasticity: paired-pulse facilitation (A2/A1 ≈ 1.75 at Δt = 50 ms), frequency-dependent EPSCs (low-pass accumulation), cumulative potentiation, and reversible LTP/LTD. A device-aware CrossSim framework built from continuous write/erase cycles (probabilistic LUT) supports Fashion-MNIST inference with high accuracy and bounded update errors (mean −0.02; asymmetry 0.198), validating that measured nonidealities remain algorithm-compatible. These results provide a materials-level handle on polymer–ion coupling to deterministically tailor temporal learning in compact, robust neuromorphic hardware.