Electrical and computer engineers at the University of California, Santa Barbara (UCSB) have developed a design for a functional nanoscale computing device that involves a dense, three-dimensional circuit operating on an unconventional type of logic that could, theoretically, be packed into a block with dimensions of no longer than 50 nanometers on any side.

“Novel computing paradigms are needed to keep up with the demand for faster, smaller and more energy-efficient devices,” says Gina Adam, postdoctoral researcher in UCSB’s Department of Electrical and Computer Engineering. “In a regular computer, data processing and memory storage are separated, which slows down computation. Processing data directly inside a three-dimensional memory structure would allow more data to be stored and processed much faster.”

An illustration depicting the structure of stacked memristors. Image credit: UCSB.Key to the design is the use of a logic system called material implication logic combined with memristors—circuit elements whose resistance depends on the most recent charges and the directions of those currents that have flowed through them. Unlike the conventional computing logic and circuitry found in today's computers and other devices, in this form of computing, logic operation and information storage happen simultaneously and locally.

The design reduces the need for components and space typically used to perform logic operations and to move data back and forth between operation and memory storage. The result of the computation is immediately stored in a memory element, which prevents data loss in the event of power outages—a critical function in autonomous systems such as robotics.

In addition, the researchers reconfigured the traditionally two-dimensional architecture of the memristor into a three-dimensional block, which could then be stacked and packed into a 50-by-50-by-50-nanometer block.

Tiny memristors are being heavily researched in academia and in industry for their promising uses in memory storage and neuromorphic computing. While implementations of material implication logic are rather exotic, more mainstream uses for it could emerge, particularly in energy-scarce systems such as robotics and medical implants, the researchers say.