Transforming Nanotechnologies into Applications: Part 6 of My Notes from 2017 MIT Health Sensing & Imaging Conference Sept. 19-20, 2017.

Transforming Nanotechnologies into Applications

Max Shulaker, Assistant Professor of Electrical Engineering and Computer Science, MIT Department of Electrical Engineering and Computer Science

The world relies on data, and there are increasing volumes of data. IoT has a massive potential for impact on our lives. But we’re currently “swimming in sensors, drowning in data”.

Problems:

  1. Power wall (https://www.systems.ethz.ch/sites/default/files/file/aos2012/slides/08-Multicore_print.pdf)
  2. Memory wall
  3. Communication wall
  4. Interconnect wall, complexity wall, resilience wall

Max listed several solutions that he didn’t feel would work – new sensors, better transistors, new architectures, improved algorithms. He believes Nanosystems have the answer…

Transform new nanotech involving new sensors, new fabrication, new devices to create revolutionary architectures enabling new applications.

Systems today are limited to 2-Dimensional circuits with a sensor, memory and a circuitboard. A futuristic nanosystem involves multiple levels of computing logic in a 3D stack with massive parallel sensing and storage. And this is here today in Max’s lab…

Image result for max shulaker A futuristic nanosystem

Replacing silicon in chips with carbon nanotubes (CNTs) in the gates. They use CNTs because they are excellent chemical sensors and would be very energy efficient (far more so than silicon transistors) if you could build a full system out of them. Furthermore the circuits would be designed the same way.

Main problems currently with CNTs are mis-positioned CNTs and metallic CNTs.

CNT Computers

Screen Shot 2017-09-19 at 14.32.24.png
BBC Article

 

Max’s team have just created the first monolithic 3D system with > 2million CNFETs and 1 Mbit RAM with a wafer-scale design +fabrication.

Our 3D nanosystem.
Nature article here

The chip can tell the difference between different compounds in gases by the pattern of sensor activation, using neural networks to stratify these.

The Take-aways

  • Energy-efficient logic + memory
  • High bandwith communication
  • Transform massive data into useful insights

Breath Analysis

MIT-ADI-MGH Collaboration for massively parallel sensing immersed in computation for breath analysis.

  • Known correlation between VOCs and disease:
    • Cancer, Irritable Bowel Disease, Parkinsons – 8 sensors
  • Gases in breath are highly heterogeneous between people.

Screen Shot 2017-09-19 at 14.49.06

Max worked in a retina lab during his PhD and answered that the sky is the limit when it comes to neuro-inspired / bio-inspired tech.

For more details:

See this link: https://spectrum.ieee.org/semiconductors/devices/how-well-put-a-carbon-nanotube-computer-in-your-hand

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