Alignment of liquid crystal/carbon nanotube dispersions for application in unconventional computing

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

doi:10.1063/1.4912538

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…Thus, this model describes the IV characteristics of an ensemble of nanoparticles between electrodes of the EiM processor, rather than individual elements (Lawson and Wolpert, 2006) or junctions (Greff et al, 2017). The motivation for this approach is that individual nanoparticles are usually at least an order of magnitude smaller than the electrode array upon which they are deposited (Massey et al, 2015b;Bose et al, 2015;Massey et al, 2015a) and so it is generally only possible to experimentally characterise a network of nanoparticles, rather than the nanoparticles themselves. In this paper, three material models are considered: (1) a Resistive Random Network (RRN), in which a randomly selected resistor is between every node pair;…”

Section: Physical Modelmentioning

confidence: 99%

Towards Intelligently Designed Evolvable Processors

Jones

Chouard

Branco

et al. 2022

Evolutionary Computation

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Evolution-in-Materio is a computational paradigm in which an algorithm reconfigures a material's properties to achieve a specific computational function. This paper addresses the question of how successful and well performing Evolution-in-Materio processors can be designed through the selection of nanomaterials and an evolutionary algorithm for a target application. A physical model of a nanomaterial network is developed which allows for both randomness, and the possibility of Ohmic and non-Ohmic conduction, that are characteristic of such materials. These differing networks are then exploited by differential evolution, which optimises several configuration parameters (e.g., configuration voltages, weights, etc.), to solve different classification problems. We show that ideal nanomaterial choice depends upon problem complexity, with more complex problems being favoured by complex voltage dependence of conductivity and vice versa. Furthermore, we highlight how intrinsic nanomaterial electrical properties can be exploited by differing configuration parameters, clarifying the role and limitations of these techniques. These findings provide guidance for the rational design of nanomaterials and algorithms for future Evolution-in-Materio processors.

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Section: Physical Modelmentioning

confidence: 99%

Towards Intelligently Designed Evolvable Processors

Jones

Chouard

Branco

et al. 2022

Evolutionary Computation

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show abstract

“…[ 9 ] Relying on a fixed network of coupled nonlinear elements has motivated the research to develop RCs based on nonlinear physical systems. [ 10,11 ] Physical RCs using fluidic systems, [ 12 ] photonic devices, [ 13 ] mechanical oscillators, [ 14,15 ] nanocomposites, [ 16 ] and memristors [ 17–19 ] have thus far been demonstrated. These demonstrations, alas, depend on complex measurement setups, impeding their practical utilization.…”

Section: Introductionmentioning

confidence: 99%

A Neuromorphic Electrothermal Processor for Near‐Sensor Computing

Shirmohammadli

Bahreyni

2022

Adv Materials Technologies

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The statistical processing of sensor data using conventional digital computers is inefficient in terms of time, energy usage, and communication bandwidth, among others. Therefore, new approaches are sought to create context and make sense of the sensor data using special‐purpose computers that excel in specific computation tasks. Herein, the requirements for physical systems to perform sophisticated nonlinear computations needed for real‐time pattern recognition in data, specifically sensor data, are discussed. The focus is on physical reservoir computing as a neuromorphic computing approach. Considering energy flow as the coupling mechanism between nonlinear dynamic systems, it is demonstrated that many physical systems satisfy the basic requirements for building reservoir computers. Using physical reservoir computers brings up exciting opportunities for near‐ or in‐sensor computing as to how new data are collected and processed. The concepts are demonstrated through a novel physical computation platform, where off‐the‐shelf, temperature‐sensitive resistors are used to perform various standard and specific computational tasks. This platform is used as a near‐sensor processor to detect particular events. How a similar platform may be used for in‐sensor neuromorphic computations is further discussed.

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“…An often used example is nanocomposite based on single-walled carbon nanotubes (SWCNT) dispersed in polymer, Poly-Butyl Methacrylate (PBMA) as sketched in Figure 2. Another example of SWCNT based material used for EIM in NASCENCE is SWCNTs mixed with liquid crystals (LC) [18,32]. Figure 4 1 shows a blob of the material dispersed on a glass slide over gold electrodes.…”

Section: Evolution-in-materiomentioning

confidence: 99%

An Explanation of Computation - Collective Electrodynamics in Blobs of Carbon Nanotubes

Laketic¹,

Tufte²,

Lykkebø³

et al. 2016

Proceedings of the 9th EAI International Conference on Bio-Inspired Information and Communications Technologies (Formerly BIONE

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In this short paper an attempt is made to explain computations in nanomaterials under the Evolution-In-Materio scenario. Computations performed by the material are considered within the framework of system theory as introduced in classical cybernetics. Three conceptual domains of computations are identified which are related to different hierarchical levels. Further, a deeper look is taken into the physics of one material in particular since it is extensively used in our experiments. It is revealed that the physics of the investigated nanocomposites, which is the basis for computations, can be explained as a collective property of the wave functions which describe electrons moving within the material. The explanation of computations given in this paper is found valuable for identifying further research directions. The first direction is towards how to manipulate nanomaterials for achieving computations at measurable levels. Manipulation at quantum level is suggested as a possible domain of material manipulation. Secondly, another research direction is identified towards using parameters, environmental parameters for example, for computing.

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Alignment of liquid crystal/carbon nanotube dispersions for application in unconventional computing

Cited by 7 publications

References 7 publications

Towards Intelligently Designed Evolvable Processors

Towards Intelligently Designed Evolvable Processors

A Neuromorphic Electrothermal Processor for Near‐Sensor Computing

An Explanation of Computation - Collective Electrodynamics in Blobs of Carbon Nanotubes

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