-
As the field of artificial intelligence continues to evolve, it generates an escalating need for intensive computational resources and novel computing architectures. As a new generation of non-volatile memory, memristors can simulate biological synapses. This makes them ideal for neuromorphic computing, enabling brain-like learning and reasoning to significantly enhance computational capabilities. Current research on memristor dielectric materials primarily focuses on transition of metal oxides, perovskites, and organic polymers. Among these, the transition metal oxide TiO2 is widely used for the switching layer due to its high dielectric constant and excellent thermal stability. However, TiO2-based memristors face challenges including poor stability and inadequate analog performance, which limit their application in neuromorphic computing. This study developed a high-performance analog memristor using an aMoS2/a-TiO2 (amorphous MoS2/ amorphous TiO2) heterostructure, achieving over 200 stable cycles and a long data retention time exceeding 104 seconds. This device demonstrates a lower threshold voltage, higher endurance, and superior data retention, as compared to previously reported TiO2-based heterostructure memristors. Furthermore, various voltage sweep schemes were designed to successfully implement multi-level conductance modulation in the W/a-MoS2/a-TiO2/Pt device. The resistive switching mechanism of the W/a-MoS2/a-TiO2/Pt device was elucidated by combining conductive mechanism fitting with a physical model that attributes the switching to the localized formation and rupture of conductive filaments. Finally, synaptic functions like LTP and LTD were implemented in the device using square-wave pulses. A convolutional neural network leveraging these functions achieved a 95.8% accuracy in handwritten digit recognition. This study developed a W/a-MoS2/a-TiO2/Pt heterostructure that significantly enhances analog memristive performance, providing an effective strategy for improving transition metal oxide-based memristors.
-
Keywords:
- Memristor /
- Heterojunction structure /
- a-MoS2 /
- Neuromorphic computing
-
[1] Shan X Y, Wang Z Q, Xie J, Zheng J H, Xu H Y, Liu Y C 2022 Acta Phys. Sin. 71 148701（in Chinese）[单旋宇, 王中强, 谢君, 郑嘉慧, 徐海阳, 刘益春 2022 物理学报 71 148701]
[2] Guo T, Pan K Q, Jiao Y X, Sun B, Du C, Mills J P, Chen Z L, Zhao X Y, Wei L, Zhou Y N, Wu Y A 2022 Nanoscale Horiz. 7 299
[3] Gu Y N, Liang Y, Wang G Y, Xia C Y 2022 Acta Phys. Sin. 71 110501（in Chinese）[古亚娜, 梁燕, 王光义, 夏晨阳 2022 物理学报 71 110501]
[4] Messaris I, Ascoli A, Demirkol A S, Tetzlaff R 2023 IEEE Trans. Circuits Syst. I, Regul. Pap. 70 566
[5] Liu S X, Sun T, Li Y 2025 Sci. China Mater. 68 2582
[6] Sun K X, Chen J S, Yan X B 2021 Adv. Funct. Mater. 31 2006773
[7] Zhou Z, Huang P, Kang J F 2022 Acta Phys. Sin. 71 148507（in Chinese）[周正, 黄鹏, 康晋锋 2022 物理学报 71 148507]
[8] Zhu Y Y, Zhang Y F, Yang S N, Ma X Y, Lu H B, Liu Y B, Luo D B, Wang Y Q, Zhou J, Wang H J 2025 Appl. Phys. Lett. 126 013507
[9] Cao Z L, Sun B, Mao S S, Zhou G D, Duan X G, Yan W T, Sun S Y, Chen X L, Shao J Y 2023 Mater. Today Phys. 38 101264
[10] Abunahla H, Halawani Y, Alazzam A, Mohammad B 2020 Sci. Rep. 10 9473
[11] Zhou H B, Li S F, Ang K W, Zhang Y W 2024 Nano-Micro Lett. 16 121
[12] Shan F, Guo H B, Kim H S, Lee J Y, Sun H Z, Choi S G, Koh J H, Kim S J 2020 Phys. Status Solidi A 217 1900967
[13] Sun B, Ranjan S, Zhou G, Guo T, Xia Y, Wei L, Zhou Y N, Wu Y A 2021 Mater. Today Adv. 9 100125
[14] Zhu Y Y, Chen M Y, Lu H B, Mi P T, Luo D B, Wang Y Q, Liu Y, Xiong R, Wang H J 2024 Appl. Phys. Lett. 124 063504
[15] Guo Z C, Xiong R, Zhu Y Y, Wang Z Y, Zhou J, Liu Y, Luo D B, Wang Y Q, Wang H J 2023 Appl. Phys. Lett. 122 053502
[16] Yin S F, Sun Q J, Liu L F, Liu S Z, Jiang Y P, Tang X G 2025 Appl. Surf. Sci. 711 164049
[17] Zou Y L, Li X Y, Ghenzi N, Park T, Shin D H, Shin S J, Cho J M, Park T W, Cheong S, Mun S A, Hwang C S 2025 ACS Appl. Mater. Interfaces 17 47207
[18] Kim S, Ji H, Park K, So H, Kim H, Kim S, Choi W Y 2024 ACS Nano 18 25128
[19] Wen X Y, Wang Y S, He Y H, Miao X S 2022 Acta Phys. Sin. 71 140501（in Chinese）[温新宇, 王亚赛, 何毓辉, 缪向水 2022 物理学报 71 140501]
[20] Yu Z Q, Han X, Xu J M, Chen C, Qu X R, Liu B S, Sun Z J, Sun T Y 2023 Sensors-basel 23 3480
[21] Zhu Y Y, Guo Z C, Chen M Y, Zhang P, Shao P, Luo D B, Wang Y Q, Liu Y, Xiong R, Wang H J 2023 Appl. Phys. Lett. 123 083503
[22] Lin Y H, Pan J Y, Zhuang X M, Guo Q K, Li Y 2025 Nano Res. 18 94907660
[23] Qin J J, Sun B, Mao S S, Zhou G D, Liu M N, Rao Z W, Lin W, Yang Y L, Zhao Y 2025 Appl. Mater. Today 44 102696
[24] Liu M R, Chen J B, Tian X H, Jia S J, Liang Y X, Zhang L Z, Ye T, Chen J T, Wang J, Zhao Y, Zhang X Q, Li Y 2025 Mater. Today Commun. 43 111642
[25] So H, Lee J, Mahata C, Kim S, Kim S 2024 Adv. Mater. Technol. 9 2301390
[26] Li D D, Tang X G, Zhong W M, Sun Q J, Yang D P, Jiang Y P, Liu Q X 2024 ACS Sustainable Chem. Eng. 12 13361
[27] Islam R, Shi Y, Silva G V O, Sachdev M, Miao G X 2024 ACS Nano 33 22045
[28] Sun D D, Zhu X D, Chen S C, Fang H T, Zhu G X, Lan G P, He L X, Shi Y Y 2024 Nano Lett. 24 16283
[29] Wang Y, Huang H X, Huang X L, Guo T T 2023 Acta Phys. Sin. 72 197201（in Chinese）[王英, 黄慧香, 黄香林, 郭婷婷 2023 物理学报 72 197201]
[30] Wong H S P, Lee H Y, Yu S M, Chen Y S, Wu Y, Chen P S, Lee B, Chen F, Tsai M J 2012 Proc. IEEE 6 1951
[31] Lee S U, Kim S Y, Lee J H, Baek J H, Lee J W, Jang H W, Park N G 2024 Nano Lett. 24 4869
[32] Chen L, Zhou W H, Li C D, Huang J J 2021 Neurocomputing 456 126
Metrics
- Abstract views: 10
- PDF Downloads: 0
- Cited By: 0
下载:
