Atul C. Khot; Tukaram D. Dongale; Kiran A. Nirmal; Ji Hoon Sung; Ho Jin Lee; Revannath Dnyandeo Nikam; Tae Geun Kim · 2022 · ACS Applied Materials & Interfaces
Paper
Although two-dimensional (2D) nanomaterials are promising candidates for use in memory and synaptic devices owing to their unique physical, chemical, and electrical properties, the process compatibility, synthetic reliability, and cost-effectiveness of 2D materials must be enhanced. In this context, amorphous boron nitride (a-BN) has emerged as a potential material for future 2D nanoelectronics. Therefore, we explored the use of a-BN for multilevel resistive switching (MRS) and synaptic learning applications by fabricating a complementary metal-oxide-semiconductor (CMOS)-compatible Ag/a-BN/Pt memory device. The redox-active Ag and boron vacancies enhance the mixed electrochemical metallization and valence change conduction mechanism. The synthesized a-BN switching layer was characterized using several analyses. The fabricated memory devices exhibited bipolar resistive switching with low set and reset voltages (+0.8 and −2 V, respectively) and a small operating voltage distribution. In addition, the switching voltages of the device were modeled using a time-series analysis, for which the Holt’s exponential smoothing technique provided good modeling and prediction results. According to the analytical calculations, the fabricated Ag/a-BN/Pt device was found to be memristive, and its MRS ability was investigated by varying the compliance current. The multilevel states demonstrated a uniform resistance distribution with a high endurance of up to 104 direct current (DC) cycles and memory retention characteristics of over 106 s. Conductive atomic force microscopy was performed to clarify the resistive switching mechanism of the device, and the likely mixed electrochemical metallization and valence change mechanisms involved therein were discussed based on experimental results. The Ag/a-BN/Pt memristive devices mimicked potentiation/depression and spike-timing-dependent plasticity-based Hebbian-learning rules with a high pattern accuracy (90.8%) when implemented in neural network simulations.
Analysis
This paper presents a novel amorphous boron nitride (a-BN) memristive device for high-density memory and neuromorphic computing, demonstrating CMOS compatibility and effective synaptic learning capabilities.
Discovery
Fernando Aguirre; Abu Sebastian; Manuel Le Gallo; Wenhao Song; Tong Wang; J. Joshua Yang; Wei Lü; Meng‐Fan Chang; Daniele Ielmini; Yuchao Yang; Adnan Mehonić; Anthony J. Kenyon; Marco A. Villena; J.B. Roldán; Yuting Wu; Hung-Hsi Hsu; Nagarajan Raghavan; J. Suñé; E. Miranda; Ahmed M. Eltawil; Gianluca Setti; Kamilya Smagulova; K. Saláma; Olga Krestinskaya; Xiaobing Yan; Kah‐Wee Ang; Samarth Jain; Sifan Li; Osamah Alharbi; Sebastián Pazos; Mario Lanza
Dwipak Prasad Sahu; Kitae Park; Peter Hayoung Chung; Jimin Han; Tae‐Sik Yoon
Sundar Kunwar; Zachary Jernigan; Zach Hughes; Chase Somodi; Michael Saccone; Francesco Caravelli; Pinku Roy; Di Zhang; Haiyan Wang; Q. X. Jia; Judith L. MacManus‐Driscoll; Garrett T. Kenyon; Andrew Sornborger; Wanyi Nie; Aiping Chen
Ji Hyun Baek; Kyung Ju Kwak; Seung Ju Kim; Jaehyun Kim; Jae Young Kim; In Hyuk Im; Sunyoung Lee; Kisuk Kang; Ho Won Jang
Xuwen Xia; Wen Huang; Pengjie Hang; Tao Guo; Yong Yan; Jianping Yang; Deren Yang; Xuegong Yu; Xing’ao Li
Matteo Farronato; Piergiulio Mannocci; Margherita Melegari; Saverio Ricci; Christian Monzio Compagnoni; Daniele Ielmini
Source record