Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

Min-Kyu Song; Ji-Hoon Kang; Xinyuan Zhang; Wonjae Ji; Alon Ascoli; Ioannis Messaris; Ahmet Samil Demirkol; Bowei Dong; Samarth Aggarwal; Weier Wan; Seok-Man Hong; Suma George Cardwell; Irem Boybat; Jae-sun Seo; Jang-Sik Lee; Mario Lanza; Hanwool Yeon; Murat Onen; Ju Li; Bilge Yildiz; Jesús A. del Alamo; Seyoung Kim; Shinhyun Choi; Gianluca Milano; Carlo Ricciardi; Lambert Alff; Yang Chai; Zhongrui Wang; Harish Bhaskaran; Mark C Hersam; Dmitri Strukov; H.-S. Philip Wong; Ilia Valov; Bin Gao; Huaqiang Wu; Ronald Tetzlaff; Abu Sebastian; Wei Lu; Leon Chua; J. Joshua Yang; Jeehwan Kim

doi:10.1021/acsnano.3c03505

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

Min-Kyu Song, Ji-Hoon Kang, Xinyuan Zhang, Wonjae Ji, Alon Ascoli, Ioannis Messaris, Ahmet Samil Demirkol, Bowei Dong, Samarth Aggarwal, Weier Wan, Seok-Man Hong, Suma George Cardwell, Irem Boybat, Jae-sun Seo, Jang-Sik Lee, Mario Lanza, Hanwool Yeon, Murat Onen, Ju Li, Bilge YildizJesús A. del Alamo, Seyoung Kim, Shinhyun Choi, Gianluca Milano, Carlo Ricciardi, Lambert Alff, Yang Chai, Zhongrui Wang, Harish Bhaskaran, Mark C Hersam, Dmitri Strukov, H.-S. Philip Wong, Ilia Valov, Bin Gao, Huaqiang Wu, Ronald Tetzlaff, Abu Sebastian, Wei Lu, Leon Chua, J. Joshua Yang, Jeehwan Kim

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Abstract

Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems. In addition, we discuss research directions for various applications of memristive technology including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technology, outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technology, this review aims to inform and inspire further research in this field.

Original language	English (US)
Journal	ACS Nano
DOIs	https://doi.org/10.1021/acsnano.3c03505
State	Published - Jun 29 2023

ASJC Scopus subject areas

General Physics and Astronomy
General Materials Science
General Engineering

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10.1021/acsnano.3c03505

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Song, M.-K., Kang, J.-H., Zhang, X., Ji, W., Ascoli, A., Messaris, I., Demirkol, A. S., Dong, B., Aggarwal, S., Wan, W., Hong, S.-M., Cardwell, S. G., Boybat, I., Seo, J., Lee, J.-S., Lanza, M., Yeon, H., Onen, M., Li, J., ... Kim, J. (2023). Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems. ACS Nano. https://doi.org/10.1021/acsnano.3c03505

@article{85390cf818d64c428a5302c2ff1945d5,

title = "Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems",

abstract = "Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems. In addition, we discuss research directions for various applications of memristive technology including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technology, outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technology, this review aims to inform and inspire further research in this field.",

author = "Min-Kyu Song and Ji-Hoon Kang and Xinyuan Zhang and Wonjae Ji and Alon Ascoli and Ioannis Messaris and Demirkol, {Ahmet Samil} and Bowei Dong and Samarth Aggarwal and Weier Wan and Seok-Man Hong and Cardwell, {Suma George} and Irem Boybat and Jae-sun Seo and Jang-Sik Lee and Mario Lanza and Hanwool Yeon and Murat Onen and Ju Li and Bilge Yildiz and {del Alamo}, {Jes{\'u}s A.} and Seyoung Kim and Shinhyun Choi and Gianluca Milano and Carlo Ricciardi and Lambert Alff and Yang Chai and Zhongrui Wang and Harish Bhaskaran and Hersam, {Mark C} and Dmitri Strukov and Wong, {H.-S. Philip} and Ilia Valov and Bin Gao and Huaqiang Wu and Ronald Tetzlaff and Abu Sebastian and Wei Lu and Leon Chua and Yang, {J. Joshua} and Jeehwan Kim",

note = "KAUST Repository Item: Exported on 2023-07-04 Acknowledgements: This review was developed out of the conference “The 5th International Conference on Memristive Materials, Devices & Systems (MEMRISYS 2022)”, held on November 30 to December 02, 2022. M.-K.S. acknowledges support from the National Research Foundation of Korea (NRF-2021R1A6A3A14044297). L.A. acknowledges support from Deutsche Forschungsgemeinschaft (DFG) under Project AL 560/21-1 and from the framework of the WAKeMeUP and StorAIge project which received funding from the Electronic Components and Systems for European Leadership Joint Undertaking in collaboration with the European Union{\textquoteright}s H2020 Framework Programme (H2020/2014-2020) and National Authorities, under grant agreement No. 783176 and No. 101007321, respectively. H.B. acknowledges support from the European Union{\textquoteright}s Horizon 2020 research and innovation programme (Grant No. 101017237, PHOENICS Project) and European Union{\textquoteright}s Innovation Council Pathfinder programme (Grant No. 101046878, HYBRAIN Project). J.A.d.A., M.O., J.L., and B.Y. acknowledge support from the MIT-IBM Watson AI Lab. M.C.H. acknowledges support from the National Science Foundation Materials Research Science and Engineering Center at Northwestern University (NSF DMR-1720139). S.H. and S.C. acknowledge support from the R&D programs of National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (2022M3F3A2A01072851 and 2022M3I7A2078273). S.K. acknowledges support from Samsung Science & Technology Foundation (Grant No. SRFC-IT2001-40206). J.-S.S. acknowledges support from JUMP COCOSYS, a Semiconductor Research Corporation program sponsored by Defense Advanced Research Projects Agency. Z.W. thanks the support from the Hong Kong Research Grant Council (Grant Nos. 27206321 and 17205922) and National Natural Science Foundation of China (Grant No. 62122004). Y.C acknowledges support from Research Grant Council of Hong Kong (PolyU502/22). G.M., C.R., and I.V. acknowledge support from the European project MEMQuD, code 20FUN06. This project (EMPIR 20FUN06 MEMQuD) has received funding from the EMPIR programme cofinanced by the Participating States and from the European Union{\textquoteright}s Horizon 2020 research and innovation programme. S.G.C. acknowledges financial support from the DOE Office of Science (ASCR/BES) for the Microelectronics Co-Design project COINLFIPS. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy{\textquoteright}s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.",

year = "2023",

month = jun,

day = "29",

doi = "10.1021/acsnano.3c03505",

language = "English (US)",

journal = "ACS Nano",

issn = "1936-0851",

publisher = "American Chemical Society",

}

Song, M-K, Kang, J-H, Zhang, X, Ji, W, Ascoli, A, Messaris, I, Demirkol, AS, Dong, B, Aggarwal, S, Wan, W, Hong, S-M, Cardwell, SG, Boybat, I, Seo, J, Lee, J-S, Lanza, M, Yeon, H, Onen, M, Li, J, Yildiz, B, del Alamo, JA, Kim, S, Choi, S, Milano, G, Ricciardi, C, Alff, L, Chai, Y, Wang, Z, Bhaskaran, H, Hersam, MC, Strukov, D, Wong, H-SP, Valov, I, Gao, B, Wu, H, Tetzlaff, R, Sebastian, A, Lu, W, Chua, L, Yang, JJ & Kim, J 2023, 'Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems', ACS Nano. https://doi.org/10.1021/acsnano.3c03505

TY - JOUR

T1 - Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

AU - Song, Min-Kyu

AU - Kang, Ji-Hoon

AU - Zhang, Xinyuan

AU - Ji, Wonjae

AU - Ascoli, Alon

AU - Messaris, Ioannis

AU - Demirkol, Ahmet Samil

AU - Dong, Bowei

AU - Aggarwal, Samarth

AU - Wan, Weier

AU - Hong, Seok-Man

AU - Cardwell, Suma George

AU - Boybat, Irem

AU - Seo, Jae-sun

AU - Lee, Jang-Sik

AU - Lanza, Mario

AU - Yeon, Hanwool

AU - Onen, Murat

AU - Li, Ju

AU - Yildiz, Bilge

AU - del Alamo, Jesús A.

AU - Kim, Seyoung

AU - Choi, Shinhyun

AU - Milano, Gianluca

AU - Ricciardi, Carlo

AU - Alff, Lambert

AU - Chai, Yang

AU - Wang, Zhongrui

AU - Bhaskaran, Harish

AU - Hersam, Mark C

AU - Strukov, Dmitri

AU - Wong, H.-S. Philip

AU - Valov, Ilia

AU - Gao, Bin

AU - Wu, Huaqiang

AU - Tetzlaff, Ronald

AU - Sebastian, Abu

AU - Lu, Wei

AU - Chua, Leon

AU - Yang, J. Joshua

AU - Kim, Jeehwan

N1 - KAUST Repository Item: Exported on 2023-07-04 Acknowledgements: This review was developed out of the conference “The 5th International Conference on Memristive Materials, Devices & Systems (MEMRISYS 2022)”, held on November 30 to December 02, 2022. M.-K.S. acknowledges support from the National Research Foundation of Korea (NRF-2021R1A6A3A14044297). L.A. acknowledges support from Deutsche Forschungsgemeinschaft (DFG) under Project AL 560/21-1 and from the framework of the WAKeMeUP and StorAIge project which received funding from the Electronic Components and Systems for European Leadership Joint Undertaking in collaboration with the European Union’s H2020 Framework Programme (H2020/2014-2020) and National Authorities, under grant agreement No. 783176 and No. 101007321, respectively. H.B. acknowledges support from the European Union’s Horizon 2020 research and innovation programme (Grant No. 101017237, PHOENICS Project) and European Union’s Innovation Council Pathfinder programme (Grant No. 101046878, HYBRAIN Project). J.A.d.A., M.O., J.L., and B.Y. acknowledge support from the MIT-IBM Watson AI Lab. M.C.H. acknowledges support from the National Science Foundation Materials Research Science and Engineering Center at Northwestern University (NSF DMR-1720139). S.H. and S.C. acknowledge support from the R&D programs of National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (2022M3F3A2A01072851 and 2022M3I7A2078273). S.K. acknowledges support from Samsung Science & Technology Foundation (Grant No. SRFC-IT2001-40206). J.-S.S. acknowledges support from JUMP COCOSYS, a Semiconductor Research Corporation program sponsored by Defense Advanced Research Projects Agency. Z.W. thanks the support from the Hong Kong Research Grant Council (Grant Nos. 27206321 and 17205922) and National Natural Science Foundation of China (Grant No. 62122004). Y.C acknowledges support from Research Grant Council of Hong Kong (PolyU502/22). G.M., C.R., and I.V. acknowledge support from the European project MEMQuD, code 20FUN06. This project (EMPIR 20FUN06 MEMQuD) has received funding from the EMPIR programme cofinanced by the Participating States and from the European Union’s Horizon 2020 research and innovation programme. S.G.C. acknowledges financial support from the DOE Office of Science (ASCR/BES) for the Microelectronics Co-Design project COINLFIPS. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.

PY - 2023/6/29

Y1 - 2023/6/29

N2 - Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems. In addition, we discuss research directions for various applications of memristive technology including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technology, outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technology, this review aims to inform and inspire further research in this field.

AB - Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomimetic memory properties, which promise to significantly improve power consumption in computing applications. Here, we provide a comprehensive overview of recent advances in memristive technology, including memristive devices, theory, algorithms, architectures, and systems. In addition, we discuss research directions for various applications of memristive technology including hardware accelerators for artificial intelligence, in-sensor computing, and probabilistic computing. Finally, we provide a forward-looking perspective on the future of memristive technology, outlining the challenges and opportunities for further research and innovation in this field. By providing an up-to-date overview of the state-of-the-art in memristive technology, this review aims to inform and inspire further research in this field.

UR - http://hdl.handle.net/10754/692771

UR - https://pubs.acs.org/doi/10.1021/acsnano.3c03505

U2 - 10.1021/acsnano.3c03505

DO - 10.1021/acsnano.3c03505

M3 - Article

C2 - 37382380

SN - 1936-0851

JO - ACS Nano

JF - ACS Nano

ER -

Recent Advances and Future Prospects for Memristive Materials, Devices, and Systems

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