DOI: 10.1038/srep10492. Data Clustering using Memristor Networks

Data Clustering using Memristor Networks

10.1038/srep10492

Crossref journal-article

Springer Science and Business Media LLC

Scientific Reports (297)

Abstract

AbstractMemristors have emerged as a promising candidate for critical applications such as non-volatile memory as well as non-Von Neumann computing architectures based on neuromorphic and machine learning systems. In this study, we demonstrate that memristors can be used to perform principal component analysis (PCA), an important technique for machine learning and data feature learning. The conductance changes of memristors in response to voltage pulses are studied and modeled with an internal state variable to trace the analog behavior of the device. Unsupervised, online learning is achieved in a memristor crossbar using Sanger’s learning rule, a derivative of Hebb’s rule, to obtain the principal components. The details of weights evolution during training is investigated over learning epochs as a function of training parameters. The effects of device non-uniformity on the PCA network performance are further analyzed. We show that the memristor-based PCA network is capable of linearly separating distinct classes from sensory data with high clarification success of 97.6% even in the presence of large device variations.

Bibliography

Choi, S., Sheridan, P., & Lu, W. D. (2015). Data Clustering using Memristor Networks. Scientific Reports, 5(1).

Authors 3

Shinhyun Choi (first)
Patrick Sheridan (additional)
Wei D. Lu (additional)

References 24 Referenced 109

Turel, Ö., Leem, J. H., Ma, X. & Likharev, K. K. Neuromorphic architectures for nanoelectronic circuits, Inter. J. Circuit Theory and Applications, 32, 277–302 (2004). (10.1002/cta.282) / Inter. J. Circuit Theory and Applications by Ö Turel (2004)
Chua, L. O. & Yang, L. Cellular neural networks: theory. IEEE Trans. Circuits and Systems-I, 35, 1257–72 (1988). (10.1109/31.7600) / IEEE Trans. Circuits and Systems-I by LO Chua (1988)
Jolliffe, I. T. Principal Component Analysis. (Springer, New York, 2002).
Alibart, F., Zamanidoost, E. & Strukov, D. B. Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat. Commun. 4, 2072 (2013). (10.1038/ncomms3072) / Nat. Commun. by F Alibart (2013)
Sheridan, P., Ma, W. & Lu, W. Pattern recognition with memristor networks. IEEE Inter. Symp. Circuits and Systems (ISCAS), 1078–81 (2014). (10.1109/ISCAS.2014.6865326)
Lee, M.-J. et al. A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5-x/TaO2-x bilayer structures. Nat. Mater., 10, 625–30 (2011). (10.1038/nmat3070) / Nat. Mater. by M-J Lee (2011)
Yang, J.-J. et al. High switching endurance in TaOx memristive devices. Appl. Phys. Lett., 97, 232102 (2010). (10.1063/1.3524521) / Appl. Phys. Lett. by J-J Yang (2010)
Torrezan, A. C., Strachan, J. P., Medeiros-Ribeiro, G. & Williams, R. S. Sub-nanosecond switching of a tantalum oxide memristor. Nanotechnology, 22, 485203 (2011). (10.1088/0957-4484/22/48/485203) / Nanotechnology by AC Torrezan (2011)
Govoreanu, B. et al. 10 × 10nm2 Hf/HfOx crossbar resistive RAM with excellent performance, reliability and low-energy operation. Electron Devices Meeting (IEDM), 2011 IEEE Inter., 31–36 (2011). (10.1109/IEDM.2011.6131652)
Yang, Y., Sheridan, P. & Lu, W. Complementary resistive switching in tantalum oxide-based resistive memory devices. Appl. Phys. Lett., 100, 203112 (2012). (10.1063/1.4719198) / Appl. Phys. Lett. by Y Yang (2012)
Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80–84 (2008). (10.1038/nature06932) / Nature by DB Strukov (2008)
Yu, S., Guan, X. & Wong, H.-S. P. Conduction mechanism of TiN∕HfOx∕Pt resistive switching memory: A trap-assisted-tunneling model. Appl. Phys. Lett. 99, 063507 (2011). (10.1063/1.3624472) / Appl. Phys. Lett. by S Yu (2011)
Chang, T., Jo, S., Kim, K. H., Sheridan, P., Gaba, S. & Lu, W. Synaptic behaviors and modeling of a metal oxide memristive device. Appl. Phys. A, 102, 857–63 (2011). (10.1007/s00339-011-6296-1) / Appl. Phys. A by T Chang (2011)
Kim, S., Choi, S. & Lu, W. Comprehensive physical model of dynamic resistive switching in an oxide memristor. ACS Nano, 8, 2369–76 (2014). (10.1021/nn405827t) / ACS Nano by S Kim (2014)
Mickel, P. R., Lohn, A. J., James, C. D. & Marinella, M. J. Isothermal switching and detailed filament evolution in memristive systems. Adv. Mater. 26, 4486–90 (2014). (10.1002/adma.201306182) / Adv. Mater. by PR Mickel (2014)
Choi, S., Lee, J., Kim, S. & Lu, W. D. Retention failure analysis of metal-oxide based resistive memory. Appl. Phys. Lett. 105, 113510 (2014). (10.1063/1.4896154) / Appl. Phys. Lett. by S Choi (2014)
Kim, S., Choi, S., Lee, J. & Lu, W. D. Tuning resistive switching characteristics of tantalum oxide memristors through Si doping. ACS Nano, 8, 10262–69 (2014). (10.1021/nn503464q) / ACS Nano by S Kim (2014)
Bache, K. & Lichman, M. Breast cancer Wisconsin (diagnostic) data set - UCI Machine Learning Repository (2013) Available at: http://archive.ics.uci.edu/ml. (Accessed: 6th July 2014).
Wolberg, W. H. & Mangasarian, O. L. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Natl. Acad. Sci, 87, 9193–96 (1990). (10.1073/pnas.87.23.9193) / Proc. Natl. Acad. Sci by WH Wolberg (1990)
Oja, E. Simplified neuron model as a principal component analyzer. J. Mathmatical, 15, 267–73 (1982). / J. Mathmatical by E Oja (1982)
Sanger, T. D. Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Networks, 2, 459–73 (1989). (10.1016/0893-6080(89)90044-0) / Neural Networks by TD Sanger (1989)
Bishop, C. M. Pattern recognition and machine learning [205] (Springer, New York, 2006).
Walt. S. Van Der, Africa, S. & Feb, M. S. The NumPy array : a structure for efficient numerical computation. Computing in Science & Engineering, 13, 22–30 (2011). (10.1109/MCSE.2011.37) / Computing in Science & Engineering by WaltS Van Der (2011)
Hunter, J. D. Matplotlib: A 2D graphics environment. Computing In Science & Engineering, 9, 90–95 (2007). (10.1109/MCSE.2007.55) / Computing In Science & Engineering by JD Hunter (2007)

Dates

Type	When
Created	10 years, 2 months ago (May 28, 2015, 10:53 a.m.)
Deposited	3 months ago (May 27, 2025, 11:57 p.m.)
Indexed	2 months, 1 week ago (June 16, 2025, 12:04 p.m.)
Issued	10 years, 3 months ago (May 28, 2015)
Published	10 years, 3 months ago (May 28, 2015)
Published Online	10 years, 3 months ago (May 28, 2015)

Funders 0

None

BibTeX

@article{Choi_2015, title={Data Clustering using Memristor Networks}, volume={5}, ISSN={2045-2322}, url={http://dx.doi.org/10.1038/srep10492}, DOI={10.1038/srep10492}, number={1}, journal={Scientific Reports}, publisher={Springer Science and Business Media LLC}, author={Choi, Shinhyun and Sheridan, Patrick and Lu, Wei D.}, year={2015}, month=may }

JSON

{
  "indexed": {
    "date-parts": [
      [
        2025,
        6,
        16
      ]
    ],
    "date-time": "2025-06-16T16:04:35Z",
    "timestamp": 1750089875168,
    "version": "3.41.0"
  },
  "reference-count": 24,
  "publisher": "Springer Science and Business Media LLC",
  "issue": "1",
  "license": [
    {
      "start": {
        "date-parts": [
          [
            2015,
            5,
            28
          ]
        ],
        "date-time": "2015-05-28T00:00:00Z",
        "timestamp": 1432771200000
      },
      "content-version": "tdm",
      "delay-in-days": 0,
      "URL": "https://creativecommons.org/licenses/by/4.0"
    },
    {
      "start": {
        "date-parts": [
          [
            2015,
            5,
            28
          ]
        ],
        "date-time": "2015-05-28T00:00:00Z",
        "timestamp": 1432771200000
      },
      "content-version": "vor",
      "delay-in-days": 0,
      "URL": "https://creativecommons.org/licenses/by/4.0"
    }
  ],
  "content-domain": {
    "domain": [
      "link.springer.com"
    ],
    "crossmark-restriction": false
  },
  "abstract": "<jats:title>Abstract</jats:title><jats:p>Memristors have emerged as a promising candidate for critical applications such as non-volatile memory as well as non-Von Neumann computing architectures based on neuromorphic and machine learning systems. In this study, we demonstrate that memristors can be used to perform principal component analysis (PCA), an important technique for machine learning and data feature learning. The conductance changes of memristors in response to voltage pulses are studied and modeled with an internal state variable to trace the analog behavior of the device. Unsupervised, online learning is achieved in a memristor crossbar using Sanger\u2019s learning rule, a derivative of Hebb\u2019s rule, to obtain the principal components. The details of weights evolution during training is investigated over learning epochs as a function of training parameters. The effects of device non-uniformity on the PCA network performance are further analyzed. We show that the memristor-based PCA network is capable of linearly separating distinct classes from sensory data with high clarification success of 97.6% even in the presence of large device variations.</jats:p>",
  "DOI": "10.1038/srep10492",
  "type": "journal-article",
  "created": {
    "date-parts": [
      [
        2015,
        5,
        28
      ]
    ],
    "date-time": "2015-05-28T14:53:23Z",
    "timestamp": 1432824803000
  },
  "update-policy": "https://doi.org/10.1007/springer_crossmark_policy",
  "source": "Crossref",
  "is-referenced-by-count": 109,
  "title": "Data Clustering using Memristor Networks",
  "prefix": "10.1038",
  "volume": "5",
  "author": [
    {
      "given": "Shinhyun",
      "family": "Choi",
      "sequence": "first",
      "affiliation": []
    },
    {
      "given": "Patrick",
      "family": "Sheridan",
      "sequence": "additional",
      "affiliation": []
    },
    {
      "given": "Wei D.",
      "family": "Lu",
      "sequence": "additional",
      "affiliation": []
    }
  ],
  "member": "297",
  "published-online": {
    "date-parts": [
      [
        2015,
        5,
        28
      ]
    ]
  },
  "reference": [
    {
      "key": "BFsrep10492_CR1",
      "doi-asserted-by": "publisher",
      "first-page": "277",
      "DOI": "10.1002/cta.282",
      "volume": "32",
      "author": "\u00d6 Turel",
      "year": "2004",
      "unstructured": "Turel, \u00d6., Leem, J. H., Ma, X. & Likharev, K. K. Neuromorphic architectures for nanoelectronic circuits, Inter. J. Circuit Theory and Applications, 32, 277\u2013302 (2004).",
      "journal-title": "Inter. J. Circuit Theory and Applications"
    },
    {
      "key": "BFsrep10492_CR2",
      "doi-asserted-by": "publisher",
      "first-page": "1257",
      "DOI": "10.1109/31.7600",
      "volume": "35",
      "author": "LO Chua",
      "year": "1988",
      "unstructured": "Chua, L. O. & Yang, L. Cellular neural networks: theory. IEEE Trans. Circuits and Systems-I, 35, 1257\u201372 (1988).",
      "journal-title": "IEEE Trans. Circuits and Systems-I"
    },
    {
      "key": "BFsrep10492_CR3",
      "unstructured": "Jolliffe, I. T. Principal Component Analysis. (Springer, New York, 2002)."
    },
    {
      "key": "BFsrep10492_CR4",
      "doi-asserted-by": "publisher",
      "first-page": "2072",
      "DOI": "10.1038/ncomms3072",
      "volume": "4",
      "author": "F Alibart",
      "year": "2013",
      "unstructured": "Alibart, F., Zamanidoost, E. & Strukov, D. B. Pattern classification by memristive crossbar circuits using ex situ and in situ training. Nat. Commun. 4, 2072 (2013).",
      "journal-title": "Nat. Commun."
    },
    {
      "key": "BFsrep10492_CR5",
      "doi-asserted-by": "crossref",
      "unstructured": "Sheridan, P., Ma, W. & Lu, W. Pattern recognition with memristor networks. IEEE Inter. Symp. Circuits and Systems (ISCAS), 1078\u201381 (2014).",
      "DOI": "10.1109/ISCAS.2014.6865326"
    },
    {
      "key": "BFsrep10492_CR6",
      "doi-asserted-by": "publisher",
      "first-page": "625",
      "DOI": "10.1038/nmat3070",
      "volume": "10",
      "author": "M-J Lee",
      "year": "2011",
      "unstructured": "Lee, M.-J. et al. A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5-x/TaO2-x bilayer structures. Nat. Mater., 10, 625\u201330 (2011).",
      "journal-title": "Nat. Mater."
    },
    {
      "key": "BFsrep10492_CR7",
      "doi-asserted-by": "publisher",
      "first-page": "232102",
      "DOI": "10.1063/1.3524521",
      "volume": "97",
      "author": "J-J Yang",
      "year": "2010",
      "unstructured": "Yang, J.-J. et al. High switching endurance in TaOx memristive devices. Appl. Phys. Lett., 97, 232102 (2010).",
      "journal-title": "Appl. Phys. Lett."
    },
    {
      "key": "BFsrep10492_CR8",
      "doi-asserted-by": "publisher",
      "first-page": "485203",
      "DOI": "10.1088/0957-4484/22/48/485203",
      "volume": "22",
      "author": "AC Torrezan",
      "year": "2011",
      "unstructured": "Torrezan, A. C., Strachan, J. P., Medeiros-Ribeiro, G. & Williams, R. S. Sub-nanosecond switching of a tantalum oxide memristor. Nanotechnology, 22, 485203 (2011).",
      "journal-title": "Nanotechnology"
    },
    {
      "key": "BFsrep10492_CR9",
      "doi-asserted-by": "crossref",
      "unstructured": "Govoreanu, B. et al. 10 \u00d7 10nm2 Hf/HfOx crossbar resistive RAM with excellent performance, reliability and low-energy operation. Electron Devices Meeting (IEDM), 2011 IEEE Inter., 31\u201336 (2011).",
      "DOI": "10.1109/IEDM.2011.6131652"
    },
    {
      "key": "BFsrep10492_CR10",
      "doi-asserted-by": "publisher",
      "first-page": "203112",
      "DOI": "10.1063/1.4719198",
      "volume": "100",
      "author": "Y Yang",
      "year": "2012",
      "unstructured": "Yang, Y., Sheridan, P. & Lu, W. Complementary resistive switching in tantalum oxide-based resistive memory devices. Appl. Phys. Lett., 100, 203112 (2012).",
      "journal-title": "Appl. Phys. Lett."
    },
    {
      "key": "BFsrep10492_CR11",
      "doi-asserted-by": "publisher",
      "first-page": "80",
      "DOI": "10.1038/nature06932",
      "volume": "453",
      "author": "DB Strukov",
      "year": "2008",
      "unstructured": "Strukov, D. B., Snider, G. S., Stewart, D. R. & Williams, R. S. The missing memristor found. Nature 453, 80\u201384 (2008).",
      "journal-title": "Nature"
    },
    {
      "key": "BFsrep10492_CR12",
      "doi-asserted-by": "publisher",
      "first-page": "063507",
      "DOI": "10.1063/1.3624472",
      "volume": "99",
      "author": "S Yu",
      "year": "2011",
      "unstructured": "Yu, S., Guan, X. & Wong, H.-S. P. Conduction mechanism of TiN\u2215HfOx\u2215Pt resistive switching memory: A trap-assisted-tunneling model. Appl. Phys. Lett. 99, 063507 (2011).",
      "journal-title": "Appl. Phys. Lett."
    },
    {
      "key": "BFsrep10492_CR13",
      "doi-asserted-by": "publisher",
      "first-page": "857",
      "DOI": "10.1007/s00339-011-6296-1",
      "volume": "102",
      "author": "T Chang",
      "year": "2011",
      "unstructured": "Chang, T., Jo, S., Kim, K. H., Sheridan, P., Gaba, S. & Lu, W. Synaptic behaviors and modeling of a metal oxide memristive device. Appl. Phys. A, 102, 857\u201363 (2011).",
      "journal-title": "Appl. Phys. A"
    },
    {
      "key": "BFsrep10492_CR14",
      "doi-asserted-by": "publisher",
      "first-page": "2369",
      "DOI": "10.1021/nn405827t",
      "volume": "8",
      "author": "S Kim",
      "year": "2014",
      "unstructured": "Kim, S., Choi, S. & Lu, W. Comprehensive physical model of dynamic resistive switching in an oxide memristor. ACS Nano, 8, 2369\u201376 (2014).",
      "journal-title": "ACS Nano"
    },
    {
      "key": "BFsrep10492_CR15",
      "doi-asserted-by": "publisher",
      "first-page": "4486",
      "DOI": "10.1002/adma.201306182",
      "volume": "26",
      "author": "PR Mickel",
      "year": "2014",
      "unstructured": "Mickel, P. R., Lohn, A. J., James, C. D. & Marinella, M. J. Isothermal switching and detailed filament evolution in memristive systems. Adv. Mater. 26, 4486\u201390 (2014).",
      "journal-title": "Adv. Mater."
    },
    {
      "key": "BFsrep10492_CR16",
      "doi-asserted-by": "publisher",
      "first-page": "113510",
      "DOI": "10.1063/1.4896154",
      "volume": "105",
      "author": "S Choi",
      "year": "2014",
      "unstructured": "Choi, S., Lee, J., Kim, S. & Lu, W. D. Retention failure analysis of metal-oxide based resistive memory. Appl. Phys. Lett. 105, 113510 (2014).",
      "journal-title": "Appl. Phys. Lett."
    },
    {
      "key": "BFsrep10492_CR17",
      "doi-asserted-by": "publisher",
      "first-page": "10262",
      "DOI": "10.1021/nn503464q",
      "volume": "8",
      "author": "S Kim",
      "year": "2014",
      "unstructured": "Kim, S., Choi, S., Lee, J. & Lu, W. D. Tuning resistive switching characteristics of tantalum oxide memristors through Si doping. ACS Nano, 8, 10262\u201369 (2014).",
      "journal-title": "ACS Nano"
    },
    {
      "key": "BFsrep10492_CR18",
      "unstructured": "Bache, K. & Lichman, M. Breast cancer Wisconsin (diagnostic) data set - UCI Machine Learning Repository (2013) Available at: http://archive.ics.uci.edu/ml. (Accessed: 6th July 2014)."
    },
    {
      "key": "BFsrep10492_CR19",
      "doi-asserted-by": "publisher",
      "first-page": "9193",
      "DOI": "10.1073/pnas.87.23.9193",
      "volume": "87",
      "author": "WH Wolberg",
      "year": "1990",
      "unstructured": "Wolberg, W. H. & Mangasarian, O. L. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Natl. Acad. Sci, 87, 9193\u201396 (1990).",
      "journal-title": "Proc. Natl. Acad. Sci"
    },
    {
      "key": "BFsrep10492_CR20",
      "first-page": "267",
      "volume": "15",
      "author": "E Oja",
      "year": "1982",
      "unstructured": "Oja, E. Simplified neuron model as a principal component analyzer. J. Mathmatical, 15, 267\u201373 (1982).",
      "journal-title": "J. Mathmatical"
    },
    {
      "key": "BFsrep10492_CR21",
      "doi-asserted-by": "publisher",
      "first-page": "459",
      "DOI": "10.1016/0893-6080(89)90044-0",
      "volume": "2",
      "author": "TD Sanger",
      "year": "1989",
      "unstructured": "Sanger, T. D. Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Networks, 2, 459\u201373 (1989).",
      "journal-title": "Neural Networks"
    },
    {
      "key": "BFsrep10492_CR22",
      "unstructured": "Bishop, C. M. Pattern recognition and machine learning [205] (Springer, New York, 2006)."
    },
    {
      "key": "BFsrep10492_CR23",
      "doi-asserted-by": "publisher",
      "first-page": "22",
      "DOI": "10.1109/MCSE.2011.37",
      "volume": "13",
      "author": "WaltS Van Der",
      "year": "2011",
      "unstructured": "Walt. S. Van Der, Africa, S. & Feb, M. S. The NumPy array : a structure for efficient numerical computation. Computing in Science & Engineering, 13, 22\u201330 (2011).",
      "journal-title": "Computing in Science & Engineering"
    },
    {
      "key": "BFsrep10492_CR24",
      "doi-asserted-by": "publisher",
      "first-page": "90",
      "DOI": "10.1109/MCSE.2007.55",
      "volume": "9",
      "author": "JD Hunter",
      "year": "2007",
      "unstructured": "Hunter, J. D. Matplotlib: A 2D graphics environment. Computing In Science & Engineering, 9, 90\u201395 (2007).",
      "journal-title": "Computing In Science & Engineering"
    }
  ],
  "container-title": "Scientific Reports",
  "original-title": [],
  "language": "en",
  "link": [
    {
      "URL": "https://www.nature.com/articles/srep10492",
      "content-type": "text/html",
      "content-version": "vor",
      "intended-application": "text-mining"
    },
    {
      "URL": "https://www.nature.com/articles/srep10492.pdf",
      "content-type": "application/pdf",
      "content-version": "vor",
      "intended-application": "text-mining"
    },
    {
      "URL": "https://www.nature.com/articles/srep10492.pdf",
      "content-type": "application/pdf",
      "content-version": "vor",
      "intended-application": "similarity-checking"
    }
  ],
  "deposited": {
    "date-parts": [
      [
        2025,
        5,
        28
      ]
    ],
    "date-time": "2025-05-28T03:57:32Z",
    "timestamp": 1748404652000
  },
  "score": 1,
  "resource": {
    "primary": {
      "URL": "https://www.nature.com/articles/srep10492"
    }
  },
  "subtitle": [],
  "short-title": [],
  "issued": {
    "date-parts": [
      [
        2015,
        5,
        28
      ]
    ]
  },
  "references-count": 24,
  "journal-issue": {
    "issue": "1",
    "published-online": {
      "date-parts": [
        [
          2015,
          9,
          22
        ]
      ]
    }
  },
  "alternative-id": [
    "BFsrep10492"
  ],
  "URL": "http://dx.doi.org/10.1038/srep10492",
  "relation": {},
  "ISSN": [
    "2045-2322"
  ],
  "subject": [],
  "container-title-short": "Sci Rep",
  "published": {
    "date-parts": [
      [
        2015,
        5,
        28
      ]
    ]
  },
  "assertion": [
    {
      "value": "4 March 2015",
      "order": 1,
      "name": "received",
      "label": "Received",
      "group": {
        "name": "ArticleHistory",
        "label": "Article History"
      }
    },
    {
      "value": "15 April 2015",
      "order": 2,
      "name": "accepted",
      "label": "Accepted",
      "group": {
        "name": "ArticleHistory",
        "label": "Article History"
      }
    },
    {
      "value": "28 May 2015",
      "order": 3,
      "name": "first_online",
      "label": "First Online",
      "group": {
        "name": "ArticleHistory",
        "label": "Article History"
      }
    },
    {
      "value": "The authors declare no competing financial interests.",
      "order": 1,
      "name": "Ethics",
      "group": {
        "name": "EthicsHeading",
        "label": "Competing interests"
      }
    }
  ],
  "article-number": "10492"
}