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

TL;DR

This paper introduces organic electrochemical transistors capable of multi-modal sensing, memory, and processing, enabling efficient bionic learning functions like conditioned reflexes and real-time health diagnostics at the edge.

Contribution

It presents a universal organic transistor architecture that integrates sensing, memory, and processing, overcoming heterogeneity issues in digital hardware for bionic learning.

Findings

Achieved record-high 10-bit analog states in organic transistors

Demonstrated real-time cardiac disease diagnosis using reservoir computing

Enabled conditioned reflex functions with integrated organic devices

Abstract

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