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Ren R, Cao Y, Wang C, Guan Y, Liu S, Wang L, Du Z, Feng C, Bekele ZA, Lan X, Zhang N, Yang G, Wang L, Li B, Hu Y, Liu Y, Parkin S, Wang K, Yu G. Initialization-Free and Magnetic Field-Free Spin-Orbit p-Bits with Backhopping-like Magnetization Switching for Probabilistic Applications. NANO LETTERS 2024; 24:10072-10080. [PMID: 39132906 DOI: 10.1021/acs.nanolett.4c01989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Probabilistic bits (p-bits) with thermal- and spin torque-induced nondeterministic magnetization switching are promising candidates for performing probabilistic computing. Previously reported spin torque p-bits include volatile low-energy barrier nanomagnets (LBNMs) with spontaneously fluctuating magnetizations and initialization-necessary nonvolatile magnets. However, initialization-free nonvolatile spin torque p-bits are still lacking. Here, we demonstrate moderately thermal stable spin-orbit torque (SOT) p-bits with non-consecutively deposited Pt//Pt/Co/Pt stacks. Backhopping-like (BH) magnetization switching with a wide range current-tunable probability of final up and down magnetization states from 0% to 100% was achieved, regardless of the initial magnetization state, which was attributed to the interplay of SOT and thermal contributions. Integer factorization using such BH-SOT p-bits in zero magnetic field was demonstrated at times that are significantly shorter than those of existing nonvolatile STT or volatile LBNMs p-bits. Our realization of initialization-free and magnetic field-free moderately thermally stable BH-SOT p-bits opens up a new perspective for probabilistic spintronic applications.
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Affiliation(s)
- Ruizhi Ren
- School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
- College of Sciences, Northeastern University, Shenyang 110819, China
| | - Yi Cao
- School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Chao Wang
- School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Yicheng Guan
- Max Planck Institute for Microstructure Physics, 06120 Halle, Germany
| | - Shuai Liu
- Department of Physics, Beijing Technology and Business University, Beijing 100048, China
| | - Lijin Wang
- School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Zeting Du
- School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Chun Feng
- School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Zelalem Abebe Bekele
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
| | - Xiukai Lan
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
| | - Nan Zhang
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
| | - Guang Yang
- Fert Beijing Institute, MIIT Key Laboratory of Spintronics, School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, China
| | - Le Wang
- Department of Physics and Beijing Key Laboratory of Optoelectronic Functional Natural Materials & Micro-nano Devices, Renmin University of China, Beijing 100872, China
| | - Baohe Li
- Department of Physics, Beijing Technology and Business University, Beijing 100048, China
| | - Yong Hu
- College of Sciences, Northeastern University, Shenyang 110819, China
| | - Yan Liu
- College of Sciences, Northeastern University, Shenyang 110819, China
| | - Stuart Parkin
- Max Planck Institute for Microstructure Physics, 06120 Halle, Germany
| | - Kaiyou Wang
- State Key Laboratory for Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
| | - Guanghua Yu
- School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
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Li X, Wan C, Zhang R, Zhao M, Xiong S, Kong D, Luo X, He B, Liu S, Xia J, Yu G, Han X. Restricted Boltzmann Machines Implemented by Spin-Orbit Torque Magnetic Tunnel Junctions. NANO LETTERS 2024; 24:5420-5428. [PMID: 38666707 DOI: 10.1021/acs.nanolett.3c04820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2024]
Abstract
Artificial intelligence has surged forward with the advent of generative models, which rely heavily on stochastic computing architectures enhanced by true random number generators with adjustable sampling probabilities. In this study, we develop spin-orbit torque magnetic tunnel junctions (SOT-MTJs), investigating their sigmoid-style switching probability as a function of the driving voltage. This feature proves to be ideally suited for stochastic computing algorithms such as the restricted Boltzmann machines (RBM) prevalent in pretraining processes. We exploit SOT-MTJs as both stochastic samplers and network nodes for RBMs, enabling the implementation of RBM-based neural networks to achieve recognition tasks for both handwritten and spoken digits. Moreover, we further harness the weights derived from the preceding image and speech training processes to facilitate cross-modal learning from speech to image generation. Our results clearly demonstrate that these SOT-MTJs are promising candidates for the development of hardware accelerators tailored for Boltzmann neural networks and other stochastic computing architectures.
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Affiliation(s)
- Xiaohan Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Caihua Wan
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Ran Zhang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Mingkun Zhao
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Shilong Xiong
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Dehao Kong
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Xuming Luo
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Bin He
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Shiqiang Liu
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Jihao Xia
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
| | - Guoqiang Yu
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Xiufeng Han
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing 100190, China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
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Sun B, Chen Y, Zhou G, Cao Z, Yang C, Du J, Chen X, Shao J. Memristor-Based Artificial Chips. ACS NANO 2024; 18:14-27. [PMID: 38153841 DOI: 10.1021/acsnano.3c07384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2023]
Abstract
Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.
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Affiliation(s)
- Bai Sun
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Yuanzheng Chen
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, People's Republic of China
| | - Guangdong Zhou
- College of Artificial Intelligence, Brain-inspired Computing & Intelligent Control of Chongqing Key Lab, Southwest University, Chongqing 400715, People's Republic of China
| | - Zelin Cao
- National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Department of Hepatobiliary Surgery, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Chuan Yang
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, People's Republic of China
| | - Junmei Du
- School of Physical Science and Technology, Key Laboratory of Advanced Technology of Materials, Southwest Jiaotong University, Chengdu, Sichuan 610031, People's Republic of China
| | - Xiaoliang Chen
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
| | - Jinyou Shao
- Micro-and Nano-technology Research Center, State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, People's Republic of China
- Frontier Institute of Science and Technology (FIST), Xi'an Jiaotong University, Xi'an, Shaanxi 710049, China
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