Atomic-Scale Hardware for Natural Computing

Posted on


PhysOrg reported on the development by Professor James Gimzewski and his research team of an atomic-scale hardware to implement natural computing.

Their findings were reported in the paper “Atomic Switch Networks – Nanoarchitectonic Design of a Complex System for Natural Computing” published in


this April.  

The UCLA members of the team (photos below) are current and former Gimzewski group members:  Eleanor Demis, Renato Aguilera (UCLA Chemistry Ph.D. students); Dr. Henry Sillin (Ph.D. ’14 UCLA Chemistry), UCLA Researcher; Kelsey Scharnhorst (UCLA Chemistry Ph.D. student); Eric Sandouk (UCLA Physics & Astronomy undergraduate); and Dr. Adam Stieg (Ph.D. ’07 UCLA Chemistry), Scientific Director, Nano & Pico Characterization Laboratory, UCLA California NanoSystems Institute (CNSI). Dr. Masakazu Aono, Director, International Center for Materials Nanoarchitectonics (MANA), National Institute for Materials Science, Tsukuba, Japan was also a member of the team.

Gimzewski Group Montage2
Prof. James Gimzewski, Eleanor Demis, Renato Aguilera, Dr. Henry Sillin, Kelsey Scharnhorst, Eric Sandouk, Dr. Adam Steig.
Excerpt from PhysOrg (by Lisa Zyga):

Scientists develop atomic-scale hardware to implement natural computing

Atomicswitch 0

An atomic switch network, showing (a) the array of platinum electrodes and (b) an SEM image of self-organized silver nanowires on a grid of copper posts. Overlapping junctions of wires form atomic switches. Scale bar = 500 µm. Credit: Demis, et al. ©2015 IOP Publishing

Despite the many great achievements of computers, no man-made computer can learn from its environment, adapt to its surroundings, spontaneously self-organize, and solve complex problems that require these abilities as well as a biological brain. These abilities arise from the fact that the brain is a complex system capable of emergent behavior, meaning that the system involves interactions between many units resulting in macroscale behavior that cannot be attributed to any individual unit. Unfortunately, conventional fabrication methods used for today’s computers cannot be used to realize complex systems to their full potential due to scaling limits—the methods simply cannot make small enough interconnected units. Now in a new paper published in Nanotechnology, researchers at UCLA and the National Institute for Materials Science in Japan have developed a method to fabricate a self-organized complex device called an atomic switch network that is in many ways similar to a brain or other natural or cognitive computing device. “Complex phenomena and self-organization—though ubiquitous in nature, social behavior, and the economy—have never been successfully used in conventional computers for prediction and modelling,” James Gimzewski, Chemistry Professor at UCLA, told “The device we have created is capable of rapidly generating self-organization in a small chip with high speed. Furthermore, it bypasses the issue of exponential machine complexity required as a function of problem complexity as in today’s computers. Our first steps form the basis for a new type of computation that is urgently needed in our ever increasingly connected world.” To read the full article, please visit PhysOrg. “Atomic Switch Networks—Nanoarchitectonic Design of a Complex System for Natural Computing ” is available at