Large Area, Highly Deformable All-Solid Organic Synaptor Implemented by Gas Phase In Situ Ion-Doped Ultrathin Polyelectrolyte

The ion gel-based synaptic transistor (synaptor) mimics a biological synapse by regulating ion movement. Through ionic charge accumulation, which forms an electrical double layer (EDL) at the interface between the dielectric and semiconductor and is managed by gate biasing, the channel conductance is modulated. This process is similar to the biological nervous system, where ions like Ca2+, Na+, and K+ control the membrane potential of nerves. Nonetheless, ion-conductive materials face challenges such as long-term stability, large-scale integration, and effective ion doping strategies. Moreover, there is a demand for solid-state ionic conductors that provide mechanical flexibility and can be processed over large areas, properties that are essential for wearable electronics, which need to be stable for long-term use and capable of withstanding physical deformation. To address the aforementioned issues, a novel method for solid-state electrolyte synthesis, the in-situ ion-doped polyelectrolyte (i-IDOPE), has been proposed and utilized to demonstrate a bio-inspired organic synapse device (BioSyn). At the molecular scale, a polyelectrolyte containing the <i>tert</i>-amine cation, inspired by the neurotransmitter acetylcholine was synthesized using initiated chemical vapor deposition (iCVD) with <i>in-situ</i> doping, a one-step vapor-phase deposition used to fabricate solid-state electrolytes. This method resulted in an ultra-thin, but highly uniform and conformal solid-state electrolyte layer compatible with large-scale integration, a form that was not previously attainable. At a synapse scale, synapse functionality was replicated, including short-term and long-term synaptic plasticity (STSP and LTSP), along with a transformation from STSP to LTSP regulated by pre-synaptic voltage spikes. On a system scale, a reflex in a peripheral nervous system was mimicked by mounting the BioSyns on various substrates such as rigid glass, flexible polyethylene naphthalate (PEN), and stretchable poly(styrene-ethylene-butylene-styrene) (SEBS) for a decentralized processing unit. Finally, an image classification ability of BioSyns was computed through semi-empirical simulations of MNIST pattern recognition, incorporating the measured LTSP characteristics from the BioSyns. The simulation confirmed that over 90% of accuracy was achieved even under bending and stretching conditions.