Brain‐Inspired Organic Electronics: Merging Neuromorphic Computing and Bioelectronics Using Conductive Polymers

• Published in: Advanced functional materials 2024-04, Vol.34 (15), p.n/a
• Main Authors: Krauhausen, Imke, Coen, Charles‐Théophile, Spolaor, Simone, Gkoupidenis, Paschalis, van de Burgt, Yoeri
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Biocompatibility; Brain; brain‐inspired; Conducting polymers; conductive polymers; Hardware; hardware computing; Neuromorphic computing; neuro‐inspired; organic bioelectronics; organic electrochemical transistors; Organic materials; organic neuromorphic computing; Transistors
• ISSN: 1616-301X; 1616-3028
• DOI: 10.1002/adfm.202307729

Neuromorphic computing offers the opportunity to curtail the huge energy demands of modern artificial intelligence (AI) applications by implementing computations into new, brain‐inspired computing architectures. However, the lack of fabrication processes able to integrate several computing units into monolithic systems and the need for new, hardware‐tailored training algorithms still limit the scope of application and performance of neuromorphic hardware. Recent advancements in the field of organic transistors present new opportunities for neuromorphic systems and smart sensing applications, thanks to their unique properties such as neuromorphic behavior, low‐voltage operation, and mixed ionic‐electronic conductivity. Organic neuromorphic transistors push the boundaries of energy efficient brain‐inspired hardware AI, facilitating decentralized on‐chip learning and serving as a foundation for the advancement of closed‐loop intelligent systems in the next generation. The biocompatibility and dual ionic‐electronic conductivity of organic materials introduce new prospects for biointegration and bioelectronics. Their ability to sense and regulate biosystems, as well as their neuro‐inspired functions can be combined with neuromorphic computing to create the next‐generation of bioelectronics. These systems will be able to seamlessly interact with biological systems and locally compute biosignals in a relevant matter. Polymer‐based organic electronics, featuring neuromorphic behavior and low voltage operation, provide energy‐efficient in‐memory computing as an alternative to conventional artificial intelligence. They support decentralized on‐chip learning and hold promise for closed‐loop intelligent systems. Their biocompatibility and ionic‐electronic conductivity enable interaction with biological systems and local biosignal processing. This review examines their unique qualities in device technology, biointegration, adaptive processing, and neuromorphic algorithms, envisioning steps toward brain‐inspired electronics.