AbstractThe fabrication of integrated circuits (ICs) employing two-dimensional (2D) materials is a major goal of semiconductor industry for the next decade, as it may allow the extension of the Moore’s law, aids in in-memory computing and enables the fabrication of advanced devices beyond conventional complementary metal-oxide-semiconductor (CMOS) technology. However, most circuital demonstrations so far utilizing 2D materials employ methods such as mechanical exfoliation that are not up-scalable for wafer-level fabrication, and their application could achieve only simple functionalities such as logic gates. Here, we present the fabrication of a crossbar array of memristors using multilayer hexagonal boron nitride (h-BN) as dielectric, that exhibit analog bipolar resistive switching in >96% of devices, which is ideal for the implementation of multi-state memory element in most of the neural networks, edge computing and machine learning applications. Instead of only using this memristive crossbar array to solve a simple logical problem, here we go a step beyond and present the combination of this h-BN crossbar array with CMOS circuitry to implement extreme learning machine (ELM) algorithm. The CMOS circuit is used to design the encoder unit, and a h-BN crossbar array of 2D hexagonal boron nitride (h-BN) based memristors is used to implement the decoder functionality. The proposed hybrid architecture is demonstrated for complex audio, image, and other non-linear classification tasks on real-time datasets.
Bibliographical noteKAUST Repository Item: Exported on 2022-01-26
Acknowledgements: This work is primarily done under BRICS-STI Framework program with Chinese Grant No: 2018YFE0100800 from the Ministry of Science and Technology of China and Indian Grant No: DST/IMRCD/BRICS/PilotCall2/2DNEURO//2018(G) from Department of Science and Technology, India. This work has also been supported by the National Natural Science Foundation of China (grant no. 61874075), the Ministry of Science and Technology of China (grant no. 2018YFE0100800), the Collaborative Innovation Centre of Suzhou Nano Science and Technology, the Priority Academic Program Development of Jiangsu Higher Education Institutions, and the 111 Project from the State Administration of Foreign Experts Affairs of China. This work has also been supported by the Department of Science and Technology of India (PratikshaYI/2017-8512, DST/INSPIRE/04/2016/000216, DST/IMP/2018/000550). Mr. Shaochuan Chen from Soochow University is acknowledged for assistance on the characterization of some of the Au/h-BN/Au devices. Prof. Santanu Mahapatra from the Indian Institute of Science, Bengaluru is acknowledged for the initial discussions and reviewing the manuscript.