Optical Resonator Neural Network for High-Speed Signal Transfer

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Solution Overview

Problem

Current neural networking technologies are limited by computer memory and processing speed, preventing rapid information exchange between artificial memory cells, which hinders the simulation of human brain functionality in machines.

Innovation Solution

A linear optical system utilizing total internal reflection, optical waveguides, evanescent waves, and optical resonators, combined with electro-optical modulators for feedback control, to create a neural network with rapid parallel signal transfer and nonlinear state changes, simulating a human brain's neural structure.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Speed

If computer memory and processing speed are used for neural network operations, then computational capability is achieved, but information exchange speed between memory cells is limited

Engineering Contradiction:

Improveinformation exchange speedVSAvoidmemory and processing architecture

Core Design Contradiction:

SpeedVSDevice complexity

Solution Approach 1:

The patent replaces electronic computation and memory systems with an optical system using resonators and waveguides. Light propagation through the optical network enables information exchange at the speed of light, eliminating the speed limitations of electronic memory and processing while maintaining neural network functionality through optical resonance and interference patterns.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent utilizes periodic optical resonance in resonator structures to store and process information. The resonant oscillations of light within the resonators create periodic action that enables memory functionality and computational operations, allowing rapid information exchange through synchronized optical oscillations rather than electronic memory access.

Inventive Principle:

Principle #19Periodic action

2Productivity

If traditional electronic neural networks are used, then computational tasks can be performed, but rapid parallel signal transfer as in human brain is prevented

Engineering Contradiction:

Improveparallel signal transfer capabilityVSAvoidsignal transmission time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent segments the neural network into multiple independent optical resonators connected by waveguides, with each resonator representing a neuron and waveguides representing synaptic connections. This segmentation allows parallel signal transfer through multiple optical paths simultaneously, enabling high productivity while maintaining fast signal transmission through direct optical coupling without sequential electronic processing delays.

Inventive Principle:

Principle #1Segmentation

3Speed

If linear optical systems are used, then rapid signal transfer is achieved, but nonlinear state changes required for neural computation are lost

Engineering Contradiction:

Improvesignal transfer speedVSAvoidnonlinear state change capability

Core Design Contradiction:

SpeedVSAdaptability or versatility

Solution Approach 1:

The patent incorporates feedback mechanisms where the output of optical resonators is fed back into the system through waveguides, creating closed-loop optical paths. This feedback enables nonlinear state changes through optical resonance conditions and interference patterns, allowing the linear optical components to exhibit nonlinear computational behavior necessary for neural network operations while maintaining rapid signal transfer speeds.

Inventive Principle:

Principle #23Feedback

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

Enables the creation of a neural network with many artificial neurons for rapid parallel signal transfer, mimicking human brain functionality, potentially enhancing machine learning and artificial intelligence capabilities.

Implementation Method 1

Using fundamental principles of physics such as total internal reflection, optical waveguides, evanescent waves and evanescent wave coupling

Methodology Applied

Scientific EffectTotal internal reflection: Total Internal Reflection

Implementation Method 2

evanescent waves and evanescent wave coupling

Methodology Applied

Scientific EffectEvanescent wave coupling:

Implementation Method 3

optical resonators

Methodology Applied

Scientific EffectOptical resonance: Resonance

Implementation Method 4

electronic modulation controllers used for changing state nonlinearly

Methodology Applied

Scientific EffectElectro-optic effect: Electro-Optic Effects

Data Source

PatentUS11836609B2Physical neural network of optical resonators and waveguides

Publication Date: 2023.12.05 LAMB CODY WILLIAM

AI summary

Utilizing the principles of wavelength-dependent evanescent wave coupling in closely-spaced optical waveguides, along with optical resonators, a method for creating a neural network out of entirely electro-optical components is discussed. Optical resonators, which can store energy as standing waves or whispering gallery modes, act as neurons. Waveguides integrated onto a chip act as dendrites or connectomes, with coupling between them simulating the analog exchange of signals in brains. Additional electro-optic controls can be utilized, such as conductive plates utilizing the electro-optic effect to change the refractive indices of the optics and coupling coefficients based on electrical signals from outside stimuli.