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.

Training a Carbon-Nanotube/Liquid Crystal Data Classifier Using Evolutionary Algorithms

“…The optimisation to be solved is that of minimising (7) for a population of size S, subject to voltage bound constraints V j ∈ [V min , V max ], R > 0, electrode assignment p and classification rule (2). V min = 0 Volts and for the SC problem V max = 4 Volts whereas for the MC V max = 7 Volts.…”

“…In [20] it is argued that inorganic materials make a better medium for unconventional computing exploration. Nano-particles were used in [2] for developing a reconfigurable Boolean logic network. In [5] and [4] liquid crystals (LC) panels were used for evolving logic gates, a tone discriminator and a robot controller.…”

Data Classification Using Carbon-Nanotubes and Evolutionary Algorithms

“…The material blend is drop-deposited within a nylon washer (2.5 mm internal diameter) fixed to this platform for material/electronics interaction. Different organic and inorganic media have been used as materials, such as slime moulds [7], bacterial consortia [1], cells (neurons) [18], liquid crystals (LC) panels [6] and nano-particles [3]. Single walled carbon nanotubes (SWCNT) based materials have shown the potential to solve computational problems [8,11,22,14,15].…”

Evolution in Nanomaterio: The NASCENCE Project

“…These artificial genomes enable us to mimic evolution on the codes, in order to find configurations that cause the matter to perform useful computational tasks. This approach is illustrated by the following example that is taken from [3].…”

“…Consider the following figure, in which the small spheres represent coated gold nanoparticles (NPs) that are trapped on an electrode array to form a fixed NPnetwork; more details can be found in [3]. Figure 2).…”