On-Skin Artificial Intelligence via Supramolecular Polymer Memtransistors

On-skin artificial intelligence (AI) demands hardware that couples skin-like mechanics with efficient, real-time computation on noisy, spatiotemporal biosignals. We introduce a supramolecular polymer memtransistor that unifies intrinsic stretchability, autonomous self-healing and low-power neuromorphic dynamics in a single material platform. The device integrates a supramolecular elastomer matrix with a p-n heterojunction semiconductor to realize charge trapping-driven short-term plasticity, high on/off ratios (>103) and tight device-to-device uniformity (σ/μ = 5.25%). Operating energies span 0.29 fJ-1.8 nJ per event, approaching the lower bound of biological synapses while retaining reliable control of synaptic weights. Arrays (7 × 7) serve as a physical reservoir for on-device reservoir computing, achieving >99% accuracy in spoken-digit recognition and robust emotion recognition (74% under 30% biaxial strain; 71% after self-healing), all maintained during 30% biaxial deformation and after autonomous recovery from deliberate damage. Beyond classification, recursive multi-step forecasting with online learning stably models chaotic dynamics with normalized RMSE ≲ 0.02, sustaining accurate long-horizon predictions. These results establish supramolecular polymer memtransistors as a materials-driven route to elastic, damage-tolerant and energy-efficient neuromorphic electronics that perform AI inference and prediction directly on the body, enabling bio-integrated systems for speech, affect and complex physiological time-series analysis.