Research & Development World

  • Home Page
  • Topics
    • Aerospace
    • Archeology
    • Automotive
    • Biotech
    • Chemistry
    • COVID-19
    • Environment
    • Energy
    • Life Science
    • Material Science
    • R&D Market Pulse
    • R&D Management
    • Physics
  • Technology
    • 3D Printing
    • A.I./Robotics
    • Battery Technology
    • Controlled Environments
      • Cleanrooms
      • Graphene
      • Lasers
      • Regulations/Standards
      • Sensors
    • Imaging
    • Nanotechnology
    • Scientific Computing
      • Big Data
      • HPC/Supercomputing
      • Informatics
      • Security
      • Software
    • Semiconductors
  • 2021 R&D 100 Award Winners
    • R&D 100 Awards
    • 2020 Winners
    • Winner Archive
  • Resources
    • Digital Issues
    • Podcasts
    • Subscribe
  • Global Funding Forecast
  • Webinars

Computers Mimic Brain Function

By R&D Editors | April 7, 2015

Researchers are always searching for improved technologies, but the most efficient computer possible already exists. It can learn and adapt without needing to be programmed or updated. It has nearly limitless memory, is difficult to crash, and works at extremely fast speeds. It’s not a Mac or a PC; it’s the human brain. And scientists around the world want to mimic its abilities.

Both academic and industrial laboratories are working to develop computers that operate more like the human brain. Instead of operating like a conventional, digital system, these new devices could potentially function more like a network of neurons.

“Computers are very impressive in many ways, but they’re not equal to the mind,” says Mark Hersam, the Bette and Neison Harris Chair in Teaching Excellence in Northwestern University’s McCormick School of Engineering. “Neurons can achieve very complicated computation with very low power consumption compared to a digital computer.”

A team of Northwestern researchers, including Hersam, has accomplished a new step forward in electronics that could bring brain-like computing closer to reality. The team’s work advances memory resistors, or “memristors,” which are resistors in a circuit that “remember” how much current has flowed through them.

The research is described in the April 6 issue of Nature Nanotechnology. Tobin Marks, the Vladimir N. Ipatieff Professor of Catalytic Chemistry, and Lincoln Lauhon, professor of materials science and engineering, are also authors on the paper. Vinod Sangwan, a postdoctoral fellow co-advised by Hersam, Marks, and Lauhon, served as first author. The remaining co-authors—Deep Jariwala, In Soo Kim, and Kan-Sheng Chen—are members of the Hersam, Marks, and/or Lauhon research groups.

“Memristors could be used as a memory element in an integrated circuit or computer,” Hersam says. “Unlike other memories that exist today in modern electronics, memristors are stable and remember their state even if you lose power.”

Current computers use random access memory (RAM), which moves very quickly as a user works but does not retain unsaved data if power is lost. Flash drives, on the other hand, store information when they are not powered but work much slower. Memristors could provide a memory that is the best of both worlds: fast and reliable. But there’s a problem: memristors are two-terminal electronic devices, which can only control one voltage channel. Hersam wanted to transform it into a three-terminal device, allowing it to be used in more complex electronic circuits and systems. 

Hersam and his team met this challenge by using single-layer molybdenum disulfide (MoS2), an atomically thin, two-dimensional nanomaterial semiconductor. Much like the way fibers are arranged in wood, atoms are arranged in a certain direction—called “grains”—within a material. The sheet of MoS2 that Hersam used has a well-defined grain boundary, which is the interface where two different grains come together.

“Because the atoms are not in the same orientation, there are unsatisfied chemical bonds at that interface,” Hersam explains. “These grain boundaries influence the flow of current, so they can serve as a means of tuning resistance.”

When a large electric field is applied, the grain boundary literally moves, causing a change in resistance. By using MoS2 with this grain boundary defect instead of the typical metal-oxide-metal memristor structure, the team presented a novel three-terminal memristive device that is widely tunable with a gate electrode.

“With a memristor that can be tuned with a third electrode, we have the possibility to realize a function you could not previously achieve,” Hersam says. “A three-terminal memristor has been proposed as a means of realizing brain-like computing. We are now actively exploring this possibility in the laboratory.”

Release Date: April 6, 2015
Source: Northwestern University  

Related Articles Read More >

‘Self-driving’ lab speeds up research, synthesis of energy materials
Record-breaking, ultrafast devices step to protecting the grid from EMPs
Surprising semiconductor properties revealed with innovative new method
Thermo Scientific Centrios HX offers precise circuit edit solution for fast prototyping
2021 R&D Global Funding Forecast

Need R&D World news in a minute?

We Deliver!
R&D World Enewsletters get you caught up on all the mission critical news you need in research and development. Sign up today.
Enews Signup

R&D World Digital Issues

February 2020 issue

Browse the most current issue of R&D World and back issues in an easy to use high quality format. Clip, share and download with the leading R& magazine today.

Research & Development World
  • Subscribe to R&D World Magazine
  • Enews Sign Up
  • Contact Us
  • About Us
  • Drug Discovery & Development
  • Pharmaceutical Processing
  • 2021 Global Funding Forecast

Copyright © 2022 WTWH Media LLC. All Rights Reserved. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of WTWH Media
Privacy Policy | Advertising | About Us

Search R&D World

  • Home Page
  • Topics
    • Aerospace
    • Archeology
    • Automotive
    • Biotech
    • Chemistry
    • COVID-19
    • Environment
    • Energy
    • Life Science
    • Material Science
    • R&D Market Pulse
    • R&D Management
    • Physics
  • Technology
    • 3D Printing
    • A.I./Robotics
    • Battery Technology
    • Controlled Environments
      • Cleanrooms
      • Graphene
      • Lasers
      • Regulations/Standards
      • Sensors
    • Imaging
    • Nanotechnology
    • Scientific Computing
      • Big Data
      • HPC/Supercomputing
      • Informatics
      • Security
      • Software
    • Semiconductors
  • 2021 R&D 100 Award Winners
    • R&D 100 Awards
    • 2020 Winners
    • Winner Archive
  • Resources
    • Digital Issues
    • Podcasts
    • Subscribe
  • Global Funding Forecast
  • Webinars