Flexible Neuromorphic Electronics for Wearable Near-Sensor and In-Sensor Computing Systems.

Journal: Advanced materials (Deerfield Beach, Fla.)
PMID:

Abstract

Flexible neuromorphic architectures that emulate biological cognitive systems hold great promise for smart wearable electronics. To realize neuro-inspired sensing and computing electronics, artificial sensory neurons that detect and process external stimuli must be integrated with central nervous systems capable of parallel computation. In near-sensor computing, synaptic devices, and sensors are used to emulate sensory neurons and receptors, respectively. In contrast, in in-sensor computing, a single multifunctional device serves as both the receptor and neuron. Bio-inspired cognitive systems efficiently detect and process stimuli through data structuring techniques, significantly reducing data volume and enabling the extension of neuromorphic applications to smart wearable systems. To construct wearable near- and in-sensor computing, it is crucial to develop artificial sensory neurons and central nervous synapses that replicate the biological functionalities. Additionally, the integrated systems must exhibit high mechanical flexibility and integration density. This review addresses research on flexible bio-inspired cognitive systems, classified into near- and in-sensor computing. It covers fundamental aspects, including biological cognitive processes, the required components, and the structures for each component, as well as applications for wearable smart systems. Finally, it offers perspectives on future research directions for flexible neuromorphic electronics in smart wearable systems connected to the next-generation Internet of Things.

Authors

  • Hyowon Jang
    School of Electrical and Computer Engineering, Center for Smart Sensor System of Seoul (CS4), University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul, 02504, Republic of Korea.
  • Jihwan Lee
    Department of Industrial and Data Engineering, Major in Industrial Data Science and Engineering, Pukyong National University, Busan 48513, Korea.
  • Chang-Jae Beak
    School of Advanced Fusion Studies, Department of Intelligent Semiconductor Engineering, University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul, 02504, Republic of Korea.
  • Swarup Biswas
    School of Electrical and Computer Engineering, Center for Smart Sensor System of Seoul (CS4), University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul, 02504, Republic of Korea.
  • Sin-Hyung Lee
    School of Electronics Engineering, and School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 702-701, Republic of Korea.
  • Hyeok Kim
    School of Electrical and Computer Engineering, Center for Smart Sensor System of Seoul (CS4), University of Seoul, 163 Seoulsiripdaero, Dongdaemun-gu, Seoul, 02504, Republic of Korea.