Evolution of a designless nanoparticle network into reconfigurable Boolean logic

Saurabh Bose, Celestine Preetham Lawrence, Zhihua Liu, K.S. Makarenko, Rudolf M.J. van Damme, Haitze J. Broersma, Wilfred Gerard van der Wiel

    Research output: Contribution to journalArticleAcademicpeer-review

    46 Citations (Scopus)
    6 Downloads (Pure)

    Abstract

    Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures.
    Original languageUndefined
    Pages (from-to)1048-1052
    Number of pages5
    JournalNature nanotechnology
    Volume10
    Issue number12
    DOIs
    Publication statusPublished - Dec 2015

    Keywords

    • CR-B.4.2
    • EWI-26637
    • Organic–inorganic nanostructures
    • CR-I.1.m
    • IR-98923
    • Nanoparticles
    • Unconventional computing
    • Electronic devices
    • METIS-315129
    • Evolution in materio

    Cite this

    Bose, Saurabh ; Lawrence, Celestine Preetham ; Liu, Zhihua ; Makarenko, K.S. ; van Damme, Rudolf M.J. ; Broersma, Haitze J. ; van der Wiel, Wilfred Gerard. / Evolution of a designless nanoparticle network into reconfigurable Boolean logic. In: Nature nanotechnology. 2015 ; Vol. 10, No. 12. pp. 1048-1052.
    @article{af6112fd49274c55b2bbf1b24c94f0e9,
    title = "Evolution of a designless nanoparticle network into reconfigurable Boolean logic",
    abstract = "Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures.",
    keywords = "CR-B.4.2, EWI-26637, Organic–inorganic nanostructures, CR-I.1.m, IR-98923, Nanoparticles, Unconventional computing, Electronic devices, METIS-315129, Evolution in materio",
    author = "Saurabh Bose and Lawrence, {Celestine Preetham} and Zhihua Liu and K.S. Makarenko and {van Damme}, {Rudolf M.J.} and Broersma, {Haitze J.} and {van der Wiel}, {Wilfred Gerard}",
    note = "eemcs-eprint-26637",
    year = "2015",
    month = "12",
    doi = "10.1038/nnano.2015.207",
    language = "Undefined",
    volume = "10",
    pages = "1048--1052",
    journal = "Nature nanotechnology",
    issn = "1748-3387",
    publisher = "Nature Publishing Group",
    number = "12",

    }

    Evolution of a designless nanoparticle network into reconfigurable Boolean logic. / Bose, Saurabh; Lawrence, Celestine Preetham; Liu, Zhihua; Makarenko, K.S.; van Damme, Rudolf M.J.; Broersma, Haitze J.; van der Wiel, Wilfred Gerard.

    In: Nature nanotechnology, Vol. 10, No. 12, 12.2015, p. 1048-1052.

    Research output: Contribution to journalArticleAcademicpeer-review

    TY - JOUR

    T1 - Evolution of a designless nanoparticle network into reconfigurable Boolean logic

    AU - Bose, Saurabh

    AU - Lawrence, Celestine Preetham

    AU - Liu, Zhihua

    AU - Makarenko, K.S.

    AU - van Damme, Rudolf M.J.

    AU - Broersma, Haitze J.

    AU - van der Wiel, Wilfred Gerard

    N1 - eemcs-eprint-26637

    PY - 2015/12

    Y1 - 2015/12

    N2 - Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures.

    AB - Natural computers exploit the emergent properties and massive parallelism of interconnected networks of locally active components. Evolution has resulted in systems that compute quickly and that use energy efficiently, utilizing whatever physical properties are exploitable. Man-made computers, on the other hand, are based on circuits of functional units that follow given design rules. Hence, potentially exploitable physical processes, such as capacitive crosstalk, to solve a problem are left out. Until now, designless nanoscale networks of inanimate matter that exhibit robust computational functionality had not been realized. Here we artificially evolve the electrical properties of a disordered nanomaterials system (by optimizing the values of control voltages using a genetic algorithm) to perform computational tasks reconfigurably. We exploit the rich behaviour that emerges from interconnected metal nanoparticles, which act as strongly nonlinear single-electron transistors, and find that this nanoscale architecture can be configured in situ into any Boolean logic gate. This universal, reconfigurable gate would require about ten transistors in a conventional circuit. Our system meets the criteria for the physical realization of (cellular) neural networks: universality (arbitrary Boolean functions), compactness, robustness and evolvability, which implies scalability to perform more advanced tasks. Our evolutionary approach works around device-to-device variations and the accompanying uncertainties in performance. Moreover, it bears a great potential for more energy-efficient computation, and for solving problems that are very hard to tackle in conventional architectures.

    KW - CR-B.4.2

    KW - EWI-26637

    KW - Organic–inorganic nanostructures

    KW - CR-I.1.m

    KW - IR-98923

    KW - Nanoparticles

    KW - Unconventional computing

    KW - Electronic devices

    KW - METIS-315129

    KW - Evolution in materio

    U2 - 10.1038/nnano.2015.207

    DO - 10.1038/nnano.2015.207

    M3 - Article

    VL - 10

    SP - 1048

    EP - 1052

    JO - Nature nanotechnology

    JF - Nature nanotechnology

    SN - 1748-3387

    IS - 12

    ER -