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Bionic learning with fused sensing, memory and processing functionsoutperforms artificial neural networks running on silicon chips in terms ofefficiency and footprint. However, digital hardware implementation of bioniclearning suffers from device heterogeneity in sensors and processing cores,which incurs large hardware, energy and time overheads. Here, we present auniversal solution to simultaneously perform multi-modal sensing, memory andprocessing using organic electrochemical transistors with designed architectureand tailored channel morphology, selective ion injection into thecrystalline/amorphous regions. The resultant device work as either a volatilereceptor that shows multi-modal sensing, or a non-volatile synapse thatfeatures record-high 10-bit analog states, low switching stochasticity and goodretention without the integration of any extra devices. Homogeneous integrationof such devices enables bionic learning functions such as conditioned reflexand real-time cardiac disease diagnose via reservoir computing, illustratingthe promise for future smart edge health informatics.

Molecular-scale Integration of Multi-modal Sensing and Neuromorphic Computing with Organic Electrochemical Transistors