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Resistors In Parallel

Resistors In Parallel

Resistors are in parallel when one end of all the resistors are connected to a continuous wire of negligible resistance and the other end of all the resistors are also connected to one another through a continuous wire of negligible resistance. In the case of a parallel configuration, the potential drop across each resistor is the same. Current through each resistor can be found using Ohm’s law, I = V/R, where the voltage is constant across each resistor. The sum of the individual...

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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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Nanosecond protonic programmable resistors for analog deep learning.

Murat Onen^1,2, Nicolas Emond^2,3, Baoming Wang^2,3

¹Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA.

Science (New York, N.Y.)

|July 28, 2022

Summary

This summary is machine-generated.

Researchers developed nanoscale protonic programmable resistors for analog deep learning. These artificial synapses operate efficiently under extreme electric fields, outperforming biological neurons and synapses in speed and energy usage.

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Area of Science:

Materials Science
Neuroscience
Electrical Engineering

Background:

Nanoscale ionic programmable resistors are being explored for analog deep learning applications.
The performance comparison between these artificial synapses and biological neurons/synapses, especially concerning speed, remains unclear.
Operation under extreme electric fields within solid electrolytes is crucial for understanding device behavior.

Purpose of the Study:

To develop silicon-compatible nanoscale protonic programmable resistors capable of operating under extreme electric fields.
To investigate the operational characteristics, including speed and energy efficiency, of these artificial synapses.
To assess the potential for these devices to surpass the performance of biological neurons and synapses.

Main Methods:

Performed scaling analyses of ionic transport and charge-transfer reaction rates.
Fabricated silicon-compatible nanoscale protonic programmable resistors.
Tested device performance under extreme electric fields, focusing on proton shuttling and intercalation.
Evaluated modulation characteristics, dynamic range, and energy efficiency at room temperature.

Main Results:

Successfully generated nanoscale protonic programmable resistors with desirable characteristics under extreme electric fields.
Achieved controlled proton shuttling and intercalation in nanoseconds at room temperature, demonstrating energy efficiency.
Observed symmetric, linear, and reversible modulation with a 20x dynamic range.
Indicated that the space-time-energy performance can significantly exceed biological counterparts.

Conclusions:

Nanoscale protonic programmable resistors operating under extreme electric fields offer a promising pathway for advanced analog deep learning.
The developed artificial synapses demonstrate superior speed and energy efficiency compared to biological neurons and synapses.
These findings pave the way for next-generation neuromorphic computing hardware with enhanced capabilities.