Adaptive Biosensing and Neuromorphic Classification Based on an Ambipolar Organic Mixed Ionic–Electronic Conductor

Abstract

Organic mixed ionic–electronic conductors (OMIECs) are central to bioelectronic applications such as biosensors, health‐monitoring devices, and neural interfaces, and have facilitated efficient next‐generation brain‐inspired computing and biohybrid systems. Despite these examples, smart and adaptive circuits that can locally process and optimize biosignals have not yet been realized. Here, a tunable sensing circuit is shown that can locally modulate biologically relevant signals like electromyograms (EMGs) and electrocardiograms (ECGs), that is based on a complementary logic inverter combined with a neuromorphic memory element, and that is constructed from a single polymer mixed conductor. It is demonstrated that a small neuromorphic array based on this material effects high classification accuracy in heartbeat anomaly detection. This high‐performance material allows for straightforward monolithic integration, which reduces fabrication complexity while also achieving high on/off ratios with excellent ambient p‐ and n‐type stability in transistor performance. This material opens a route toward simple and straightforward fabrication and integration of more sophisticated adaptive circuits for future smart bioelectronics.

All‐Polymer Bulk‐Heterojunction Organic Electrochemical Transistors with Balanced Ionic and Electronic Transport

“…Ambipolar behavior that can provide both n-and p-channel performance in a single device is therefore very important for lower-cost, monolithic manufacturing of CMOS integrated circuits. [18,21,22] This has led to the search for novel ambipolar conjugated polymers for OECT-based circuits. One of the first polymers employed in ambipolar OECTs contains diketopyrrolopyrrole structure and showed balanced hole and electron transport.…”

Emulating the Signal Transmission in a Neural System Using Polymer Membranes

“…32−35 Meanwhile, ion-based devices, such as electrochemical memory (ECM) devices, are also emerging as neuromorphic devices. 36,37 The signals in the brain are transported based on ion movement. 38−42 Because ECM devices can precisely control ion movement using electrical inputs, these devices can emulate synaptic characteristics similar to those of biological systems.…”

“…48 utilizing the ionic diffusion phenomenon, artificial synapses could exhibit the chemical−electrical signal transition behaviors of the neural system. 37 To emulate chemical synapses, polymers have been widely used to mimic the function of the cell membrane and synapse. 51−55 Polymers could be designed as high-ionic-conductivity materials for ion transport.…”

“…Due to the brain’s efficiency, brain-inspired computing systems have emerged for highly efficient memory devices. − Neuromorphic devices have been developed by emulating the components of a brain to mimic its computing processes, such as postsynaptic current (PSC) and postsynaptic potential (PSP). ,− Artificial synapse devices have mimicked the electrical characteristics of the neurons and synapses, such as PSC, paired-pulse facilitation, short- to long-term plasticity transition, potentiation, and depression. ,,, To emulate the neural networks and calculation processes of the brain, resistance switching memory (RSM) devices are being employed as neuromorphic devices because they offer advantages in terms of integration, power consumption for the switching operation, and multi-level operation. − The switching characteristics of RSM devices can be tuned for neuronal signaling to emulate the threshold characteristics of neurons. − Meanwhile, ion-based devices, such as electrochemical memory (ECM) devices, are also emerging as neuromorphic devices. , The signals in the brain are transported based on ion movement. − Because ECM devices can precisely control ion movement using electrical inputs, these devices can emulate synaptic characteristics similar to those of biological systems. ,, …”

“…In addition, chemical-based synaptic properties have been emulated. − The chemical presynaptic input affects the signalization; thus, it can emulate biological computing such as memorization, calculation, and learning functions. − By utilizing the ionic diffusion phenomenon, artificial synapses could exhibit the chemical–electrical signal transition behaviors of the neural system . To emulate chemical synapses, polymers have been widely used to mimic the function of the cell membrane and synapse. − Polymers could be designed as high-ionic-conductivity materials for ion transport .…”