Researchers at the University of Twente in The Netherlands have designed and demonstrated working electronic logic circuits produced using methods that resemble Darwinian evolution and neural networks like the human brain.
In a radical “designless” approach, the researchers used a 200-nanometer-wide cluster of 20-nanometer gold nanoparticles. They applied a series of voltages to eight electrodes and determined the resulting set of 16 different two-input Boolean logic gates.
Instead of designing logic circuits with specified functions, as with conventional transistors, this approach works around — or can even take advantage of — any material defects.
To do this, the researchers used an artificial evolution model — one that runs in less than an hour, rather than millions of years. “Natural evolution has led to powerful ‘computers’ like the human brain, which can solve complex problems in an energy-efficient way,” the researchers note. “Nature exploits complex networks that can execute many tasks in parallel.”
“This is the first time that scientists have succeeded in realizing robust electronics with small dimensions that can compete with commercial technology.”
Conventional transistors are limited to a handful of atoms. It would a major challenge to produce chips in which the millions of transistors required have the same characteristics, according to the researchers from the Twente MESA+ Institute for Nanotechnology and the CTIT Institute for ICT Research. Current transistor designs are also limited by their energy consumption, which is reaching unacceptable levels.
According to University of Twente prof. Wilfred van der Wiel, the logic circuits they discovered currently have limited computing power. “But with this research we have delivered a proof of principle. By scaling up the system, real added value will be produced in the future. This type of circuitry uses much less energy, both in production and use.” The researchers anticipate a wide range of applications — for example, in portable electronics and in the medical world.
“By choosing a smaller nanoparticle diameter, and scaling down the electrode geometry accordingly, our network would not only further reduce area, but room-temperature operation would come into sight as well,” the researchers note in a paper in the journal Nature Nanotechnology.
Mimicking brain-like systems
The researchers also contrast their “designless” reconfigurable approach with massively parallel (but still design-constrained) architectures such as IBM’s TrueNorth brain-inspired chip.
“An especially interesting avenue to explore is the suitability of this system for advanced functionality that is hard (or expensive) to realize in a conventional architecture, such as pattern recognition by mimicking brain-like systems, or simulations of complex physical systems,” the researchers note in the paper. “Our evolutionary approach works around device-to-device variations at the nanoscale and the accompanying uncertainties in performance, which is becoming more and more a bottleneck for the miniaturization of conventional electronic circuits.”
Abstract of Evolution of a designless nanoparticle network into reconfigurable Boolean logic
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.