Physically reconfigurable synaptic plasticity and learning in stretchable neuromorphic systems

Abstract

Wearable electronics that use intrinsically stretchable organic neuromorphic devices offer a promising approach to achieve human-like on-device processing with a seamless human body interface. A central challenge, however, lies in achieving tunable synaptic plasticity within the neuromorphic systems for endowing task-adaptable functions for broad and versatile applications, because synaptic plasticity is typically hardwired by the structural configuration of conventional devices. Here, we present a physically reconfigurable neuromorphic transistor platform enabled by an ion-conductive adhesive elastomer (IAE) that ensures robust mechanical integration and electrolyte-gated neuromorphic operation. The IAE-gated organic neuromorphic transistors (IONTs) exhibit exceptional mechanical resilience, maintaining nearly identical electrical properties and synaptic plasticity under 50% strain and after 1000 mechanical stretching cycles in stark contrast to the conventional ion-gel-gated device, which suffers a current drop exceeding two orders of magnitude. Uniquely, by selection and assembly of the gate electrode materials that can be a stretchable carbon nanotube or a flexible gold electrode, we program the IONTs with distinct synaptic plasticity for sensory processing or learning. Utilizing the strategy, we demonstrate high-accuracy classification of handwritten digits and spoken digits using a reservoir computing framework. Our findings establish a stretchable neuromorphic platform wherein functionally distinct synaptic devices can be achieved individually through physical reconfiguration, paving the way for neuromorphic hardware for multi-functional body-conformable artificial intelligence.