Perovskite Nanowire based Resistive RAM for Data Storage and Neuromorphic Computing
Swapnadeep Poddar a, Zhiyong Fan a
a The Hong Kong University of Science and Technology, Hong Kong
Proceedings of Neuromorphic Materials, Devices, Circuits and Systems (NeuMatDeCaS)
VALÈNCIA, Spain, 2023 January 23rd - 25th
Organizers: Rohit Abraham John, Irem Boybat, Jason Eshraghian and Simone Fabiano
Contributed talk, Swapnadeep Poddar, presentation 029
DOI: https://doi.org/10.29363/nanoge.neumatdecas.2023.029
Publication date: 9th January 2023

In the recent past, halide perovskites (HPs) have earned fame in the genre of optoelectronics and photovoltaics owing to their excellent optoelectronic properties. Besides optoelectronic traits, HPs also possess a plethora of charge migration pathways, inherent hysteresis, high charge-carrier and ionic mobilities which make them perfect candidates for resistive random access switching memories (RRAMs). However, due to material and electrical instability, the figures-of-merits (FOMs) namely retention, endurance and switching speed were not up to the state of-the-art standard until recently. In order to revolutionize HP Re-RAMs, we devised a novel structure where we replaced the thin-film architecture with vertically aligned high-density HP nanowires rooted in a porous alumina membrane (PAM) sandwiched between silver and aluminum contacts. The excellent passivation provided by the PAM imparted the electrical and material stability to the environmentally fragile HPs by drastically reducing the moisture aided attacks. Data retention time as long as 28.3 years and device endurance of 5 million cycles were obtained along with a switching speed of 100 ps and a down-scaled cell size of 14 nm was achieved. Besides data storage, the HP nanowires were utilized in developing neuromorphic devices possessing low power and high-precision computing functionalities. The nanowire based devices were made to operate in the electro-chemical metallization (ECM) mode and the non-electro-chemical metallization (non-ECM) mode by using silver and indium doped tin oxide as the top electrodes respectively, in order to obtain robust multi-level resistance states. The artificial neural networks tuned by the multi-level states thus obtained, was used to perform image processing tasks of outlining, sharpening and embossing in the ECM mode while the one in the non-ECM mode was utilized to emulate the cognitive learning model of Gestalt Closure Principle. All in all, our nanowire in PAM based architecture, uplifts HP RRAMs to the state-of-the-art standard in various applications concerning data storage and brain-inspired computing.

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