Manipulating the conductance of single-walled carbon nanotubes based thin films for evolving threshold logic circuits using particle swarm optimisation

Qaiser, F.; Kotsialos, Apostolos; Massey, Mark K.; Zeze, Dagou A.; Pearson, Christopher; Petty, Michael C.

doi:10.1109/cec.2016.7748357

Cited by 3 publications

(2 citation statements)

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“…Liquid Crystals (LCs) from a display screen have been used as the material part of EiM for evolving robot controllers [8], a tone discrimination device [9] and logic gates [10]. In [11] and [12] a dry mix of Single-Walled Carbon Nanotubes (SWCNT) with a polymer were used as the computational material and its electrical conductance was used as the manipulated property for solving the problem of calculating Boolean functions using a threshold interpretation scheme; the same material is used in [13] and [14] for solving optimisation problems. A mixture of SWCNT and LC in liquid form has also shown potential to solve classification problems.…”

Section: Introductionmentioning

confidence: 99%

Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers

Vissol-Gaudin

Kotsialos

Groves

et al. 2018

2018 IEEE Congress on Evolutionary Computation (CEC)

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Section: Introductionmentioning

confidence: 99%

Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers

Vissol-Gaudin

Kotsialos

Groves

et al. 2018

2018 IEEE Congress on Evolutionary Computation (CEC)

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“…Liquid Crystals (LCs) from a display screen have been used as the material part of EiM for evolving robot controllers [5], a tone discrimination device [3] and logic gates [4]. In [9], [11], and [17] a dry mix of Single-Walled Carbon Nanotubes (SWCNT) with a polymer were used as the computational material and its electrical conductance was used as the manipulated property for solving the problem of calculating Boolean functions using a threshold interpretation scheme; the same material is used in [14] and [15] for solving optimisation problems. In [12], [22], [21] and [20] the material used is a solution of SWCNT with LC in liquid rather than in dry solid state.…”

Section: Introductionmentioning

confidence: 99%

Computing Based on Material Training: Application to Binary Classification Problems

Vissol-Gaudin¹,

Kotsialos²,

Groves³

et al. 2017

2017 IEEE International Conference on Rebooting Computing (ICRC)

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any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-Evolution-in-materio is a form of unconventional computing combining materials' training and evolutionary search algorithms. In previous work, a mixture of single-walled-carbonnanotubes (SWCNTs) dispersed in a liquid crystal (LC) was trained so that its morphology and electrical properties were gradually changed to perform a computational task. Materialbased computation is treated as an optimisation problem with a hybrid search space consisting of the voltages used for creating the electrical field and the material's infinitely possible SWCNT arrangements in LC. In this paper, we study solutions using synthetic data with a non-linear separating boundary. In addition, results for two real life datasets with partly merged classes are presented. The training process is based on a differential evolution (DE) algorithm, which subjects the SWCNT/LC material to repeated electrical charging, leading to progressive morphological and electric conductivity modifications. It is shown that the material configuration the DE algorithm converges to form a non-negligible part of the solution. Furthermore, the problem's complexity is relevant to the properties of the resulting physical solver. The material structures created when training for a problem allow the retraining for a less complex one. The result is a doubly-trained material that keeps the memory of the original more complex problem. This is not the case for doubly-trained materials where initial training is for the less complex problem. The optimal electric field found by the DE algorithm is also a necessary solution component for the material's output to be interpreted as a computation.

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