Ultrathin HfO2/Al2O3 bilayer based reliable 1T1R RRAM electronic synapses with low power consumption for neuromorphic computing. Issue 4 (1st December 2022)

• Record Type: Journal Article
• Authors: Wang, Qiang; Wang, Yankun; Luo, Ren; Wang, Jianjian; Ji, Lanlong; Jiang, Zhuangde; Wenger, Christian; Song, Zhitang; Song, Sannian; Ren, Wei; Bi, Jinshun; Niu, Gang
• Is Part Of: Neuromorphic computing and engineering. Volume 2:Issue 4(2022)
• Publication Date: 2022-12-01
• Subjects: HfO2/Al2O3 -- 1T1R -- RRAM -- electronic synapse -- bilayer RRAM; Neural networks (Computer science) -- Periodicals; Neural computers -- Periodicals; Neuromorphics -- Periodicals; 006.3
• DOI: 10.1088/2634-4386/aca179
• Languages: English
• ISSNs: 2634-4386

Abstract

Abstract: Neuromorphic computing requires highly reliable and low power consumption electronic synapses. Complementary-metal-oxide-semiconductor (CMOS) compatible HfO2 based memristors are a strong candidate despite of challenges like non-optimized material engineering and device structures. We report here CMOS integrated 1-transistor-1-resistor (1T1R) electronic synapses with ultrathin HfO2 /Al2 O3 bilayer stacks (<5.5 nm) with high-performances. The layer thicknesses were optimized using statistically extensive electrical studies and the optimized HfO2 (3 nm)/ Al2 O3 (1.5 nm) sample shows the high reliability of 600 DC cycles, the low Set voltage of ∼0.15 V and the low operation current of ∼6 µ A. Electron transport mechanisms under cycling operation of single-layer HfO2 and bilayer HfO2 /Al2 O3 samples were compared, and it turned out that the inserted thin Al2 O3 layer results in stable ionic conduction. Compared to the single layer HfO2 stack with almost the same thickness, the superiorities of HfO2 /Al2 O3 1T1R resistive random access memory (RRAM) devices in electronic synapse were thoroughly clarified, such as better DC analog switching and continuous conductance distribution in a larger regulated range (0–700 µ S). Using the proposed bilayer HfO2 /Al2 O3 devices, a recognition accuracy of 95.6% of MNIST dataset was achieved. These results highlight the promising role of the ultrathin HfO2 /Al2 O3 bilayer RRAM devices in the application of high-performance neuromorphic computing.