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Emerging Devices for Sensing-Memory-Computing Applications

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Near-sensor and In-sensor Computing

Abstract

Traditional artificial visual system consisting of separated photodetector, memory unit, and processing unit is facing the problems of high energy consumption and high delay, not conducive to the development of real-time processing. It is in an urgent need to develop sensing-memory-computing electronics for high-efficiency information processing, breaking the bottleneck of separated functional units in artificial visual system. Emerging neuromorphic computing memristors are considered as the most attractive candidate for next-generation sensing-memory-computing electronics owing to excellent characteristics including low power consumption, high speed, and low operation voltage. Various materials and device structures were developed to fabricate neuromorphic computing memristors, such as metal oxide, two-dimensional materials, organic material, phase change material, and ferroelectric materials. Binary oxide material, ternary oxide material, and oxide heterojunctions could be fabricated as active layers in oxide-based memristor with the advantages of CMOS compatibility, uniform distribution operating voltage, and excellent endurance characteristics. Two-dimensional materials have shown great advantages in mechanical flexibility, dangling-bond-free lattice, tunable bandgap, and diverse heterostructures. Organic materials have advantages of low Young’s modulus and easily changing properties by chemical design. Phase change materials show advantages in low power consumption, high speed, multi-bit storage, and optical sensing. Ferroelectric materials have advantages in dielectric, piezoelectric, pyroelectric, electro-optic, and acousto-optic effect. However, there is still a long way to go for industrial applications of sensing-memory-computing devices with these materials. The CMOS compatibility, high-density integration capability, low cost, and stable performance of memristors are key factors of sensing-memory-computing devices for industrial applications. For future sensing-memory-computing chips, novel heterogeneous integration of different material systems and structure by combining the advantages of specific material system may be the possible direction of the next-generation real-time processing neuromorphic system.

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Acknowledgements

This work was supported by the National Key R&D Program of China (2021YFA1202600), NSFC (92064009, 61904033, 62004044), Shanghai Rising-Star Program (19QA1400600), the Program of Shanghai Subject Chief Scientist (18XD1402800), China Postdoctoral Science Foundation (Grant 2022TQ0068, BX2021070, 2021M700026), the Zhejiang Lab’s International Talent Fund for Young Professionals, and the Young Scientist Project of MOE Innovation Platform.

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  1. State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai, China

    Lin Chen, Tianyu Wang, Jialin Meng, Qingxuan Li, Yuqing Fang & Jiajie Yu

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  1. Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China

    Yang Chai

  2. The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzen, China

    Fuyou Liao

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Chen, L., Wang, T., Meng, J., Li, Q., Fang, Y., Yu, J. (2022). Emerging Devices for Sensing-Memory-Computing Applications. In: Chai, Y., Liao, F. (eds) Near-sensor and In-sensor Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-11506-6_7

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