DOI: https://doi.org/10.29363/nanoge.icpme.2021.011
Publication date: 1st December 2021
The development of internet of things and artificial intelligence induces the rapid growing of sensory nodes which generates a large portion of unstructured and redundant raw data. In the conventional design, the analogue sensory data are initially transformed into digital data with analogue-to-digital conversion, then stored in memory unit. The computational task is further performed by transferring the digital data between memory and local computation unit. The architecture of separated sensor, memory and data processing units results in the data-accessing latency and relatively high-power consumption. Alternatively, near-sensor computing with accelerator or processing unit reside besides sensor to execute computational task and in-sensor computing with individual sensors or multiple connected sensor to directly process information have been proposed to improve energy, area and time efficiency [1, 2].
However, transistor-based chips and single devices to implement near-/in-sensor computing make them bulky, energy-inefficient and complicated. The devices with relatively compact structure and simple operation mode are highly required. Memristor is a two-terminal electronic device featured by nanometer size, storage capacity and dynamic continuous variable resistance. In a typical bipolar resistive switching device, the switching of high resistance state (HRS) and low resistance state (LRS) is driven by the application of voltage above the set voltage. The rich dynamic properties of ion migration and electronic-ionic coupling ensures that memristor is the promising candidate for near-/in-sensor computing. In additon, the multi-field controlled memristor is expected to further scale down the chip size. In this presentation, we will discuss the benefit of functional memristor technologies in the application of near-/in-sensor computing [3-6].
References
[1] Kagawa, K. et al. IEEE J. Sel. Top. Quantum Electron. 2004, 10, 816
[2] Mennel, L. et al. Nature 2020, 579, 62
[3] S.-T. Han* et al. Matter 2021, 4, 1702
[4] S.-T. Han* et al. Nature Commun. 2021, DOI 10.1038/s41467-021-26314-8.
[5] S.-T. Han* et al. Adv. Funct. Mater. 2021, 31, 2100144
[6] S.-T. Han* et al. Adv. Mater. 2018, 30, 1802883
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