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UMass Leads International Team Publishing Research on Diffusive Memristors in Nature Materials

J. Joshua Yang

J. Joshua Yang

Qiangfei Xia

Qiangfei Xia

Professors J. Joshua Yang and Qiangfei Xia of the Electrical and Computer Engineering (ECE) Department at the University of Massachusetts Amherst are leading an international team of researchers who are publishing an article in Nature Materials entitled “Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing.” Nature Materials is a premier Nature Publishing Group journal with an impact factor as high as 38.89.

Yang describes the research in the article as part of “our collaborative work on a new type of memristive device that can faithfully emulate the functionality of a biological synapse.”

As Xia adds, “This work opens a new avenue of neuromorphic computing hardware based on memristors.” As the researchers explain, neuromorphic computing – meaning microprocessors configured more like human brains than like traditional chips – is one of the most promising transformative computing technologies currently under intensive study.

The authors include Zhongrui Wang, Saumil Joshi, Hao Jiang, Rivu Midya, Peng Lin, Xia, and Yang of the UMass ECE department; Sergey E. Savel’ev of the Department of Physics, Loughborough University, Loughborough, UK; Miao Hu, Ning Ge, John Paul Strachan, Zhiyong Li, and R. Stanley Williams of the Hewlett Packard Labs, Palo Alto, California; Qing Wu and Mark Barnell of the Air Force Research Lab, Information Directorate, Rome, New York; Geng-Lin Li of the UMass Department of Biology; and Huolin L. Xin of the Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, New York.

“Memristors have become a leading candidate to enable neuromorphic computing by reproducing the functions in biological synapses and neurons in a neural network system, while providing advantages in energy and size,” the researchers say.

They observe that, unfortunately, most of the synaptic demonstrations with memristors to date have not implemented diffusive dynamics unless sub-threshold complementary metal–oxide–semiconductor (CMOS) computers equipped with large capacitors are used to simulate it. CMOS, a standard technology for constructing integrated circuits, is commonly used in microprocessors, microcontrollers, static RAM, and other digital logic circuits.

“In the manuscript, we proposed and demonstrated a bio-inspired solution to the diffusive dynamics that is fundamentally different from the CMOS approach while sharing great similarities with synapses,” the research team suggests. “Specifically, we developed a diffusive-type memristor where diffusion of atoms offers a similar dynamics and the needed time-scales as its bio-counterpart, leading to a more faithful emulation of actual synapses, i.e., a true synaptic emulator.”

The researchers believe that their new approach has considerable advantages over the CMOS approach. Compared to the CMOS approach, they disclose, “The two-terminal diffusive memristor will lead to a significant reduction in footprint, complexity, and energy-consumption.” 

The team notes that their approach “offers not only a timing mechanism close to that of actual synapses but also other important features observed in synapses, such as the dynamical balance of Ca2+ concentration, depletion effects of mobile species, and interacted but separated physical entities for different functions and others. These enable us to more closely capture synaptic functions that could not be demonstrated before.”

The working mechanism of the proposed novel memristor was confirmed with a combination of in-situ transmission electron microscopy and nanoparticle dynamics simulations. Used together with a regular drift-type memristor, the operating characteristics of the devices were verified experimentally by demonstrating some important synaptic functions, including both short-term and long-term plasticity.

The researchers conclude that “The results here provide an encouraging pathway toward synaptic emulation using diffusive memristors for neuromorphic computing.” (September 2016)