NIST Chip Lights Up Optical Neural Network Demo

NIST's grid-on-a-chip distributes light signals precisely, showcasing a potential new design for neural networks. The three-dimensional structure enables complex routing schemes, which are necessary to mimic the brain. Light could travel farther and faster than electrical signals. Credit: Chiles/NIST

Researchers at the National Institute of Standards and
Technology (NIST) have made a silicon chip that distributes optical signals
precisely across a miniature brain-like grid, showcasing a potential new design
for neural networks.

The human brain has billions of neurons (nerve cells), each
with thousands of connections to other neurons. Many computing research
projects aim to emulate the brain by creating circuits of artificial neural
networks. But conventional electronics, including the electrical wiring of
semiconductor circuits, often impedes the extremely complex routing required
for useful neural networks.

The NIST team proposes to use light instead of electricity
as a signaling medium. Neural networks already have demonstrated remarkable
power in solving complex problems, including rapid pattern recognition and data
analysis. The use of light would eliminate interference due to electrical
charge, and the signals would travel faster and farther.

"Light's advantages could improve the performance of neural
nets for scientific data analysis such as searches for Earth-like planets and
quantum information science, and accelerate the development of highly intuitive
control systems for autonomous vehicles," NIST physicist Jeff Chiles said.

A conventional computer processes information through
algorithms, or human-coded rules. By contrast, a neural network relies on a
network of connections among processing elements, or neurons, which can be
trained to recognize certain patterns of stimuli. A neural or neuromorphic
computer would consist of a large, complex system of neural networks.

Described in a new paper, the NIST chip overcomes a major
challenge to the use of light signals by vertically stacking two layers of
photonic waveguides"”structures that confine light into narrow lines for routing
optical signals, much as wires route electrical signals. This three-dimensional
(3D) design enables complex routing schemes, which are necessary to mimic
neural systems. Furthermore, this design can easily be extended to incorporate
additional waveguiding layers when needed for more complex networks.

The stacked waveguides form a three-dimensional grid with 10
inputs or "upstream" neurons each connecting to 10 outputs or "downstream"
neurons, for a total of 100 receivers. Fabricated on a silicon wafer, the
waveguides are made of silicon nitride and are each 800 nanometers (nm) wide
and 400 nm thick. Researchers created software to automatically generate signal
routing, with adjustable levels of connectivity between the neurons.

Laser light was directed into the chip through an optical
fiber. The goal was to route each input to every output group, following a
selected distribution pattern for light intensity or power. Power levels
represent the pattern and degree of connectivity in the circuit. The authors
demonstrated two schemes for controlling output intensity: uniform (each output
receives the same power) and a "bell curve" distribution (in which middle
neurons receive the most power, while peripheral neurons receive less).

To evaluate the results, researchers made images of the
output signals. All signals were focused through a microscope lens onto a
semiconductor sensor and processed into image frames. This method allows many
devices to be analyzed at the same time with high precision. The output was
highly uniform, with low error rates, confirming precise power distribution.

"We've really done two things here," Chiles said. "We've
begun to use the third dimension to enable more optical connectivity, and we've
developed a new measurement technique to rapidly characterize many devices in a
photonic system. Both advances are crucial as we begin to scale up to massive
optoelectronic neural systems."

Paper: J. Chiles, S.M. Buckley, S.W. Nam, R.P. Mirin and J.
M. Shainline. Published July 26, 2018. Design, fabrication and metrology of
10x100 multi-planar integrated photonic routing manifolds for neural networks.
APL Photonics. doi:10.1063/1.5039641

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