Optoelectronic Neural Module for Accurate Optical Nonlinear Computing
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Solution Overview
Problem
Deep learning neural networks face computational complexity and high power consumption issues due to the exponential increase in computation with the number of hidden layers, and optical neural computing methods struggle with low accuracy in non-linear computations.
Innovation Solution
An optoelectronic module comprising a photodetector, electronic element, and light source element that performs non-linear computation by converting input light into current, amplifying it, and generating output light with specific activation functions, such as Relu and Sigmoid, to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If semiconductor-based electrical computation is used to increase the number of hidden layers and neurons, then computation capability is improved, but power consumption and device complexity increase exponentially
Solution Approach 1:
The patent replaces semiconductor-based electrical computation with optical computation using light sources and photodetectors. The optical neural network uses light intensity modulation to perform computations, eliminating the need for electrical current flow through transistors and reducing power consumption associated with electrical signal processing and heat generation.
Solution Approach 2:
The patent changes the fundamental parameter of computation from electrical current to light intensity. By using optical signals with varying intensities to represent data and perform computations, the system achieves exponential scaling capability without the quadratic power consumption growth inherent in electrical systems.
2Productivity
If optical non-linearity of material is used for non-linear computation, then optical processing is achieved, but accuracy deteriorates
Solution Approach 1:
The patent introduces photodetectors as intermediary devices that convert optical signals to electrical signals for non-linear processing, then convert back to optical signals. This intermediary electrical processing stage enables accurate implementation of activation functions while maintaining the overall optical processing architecture, solving the accuracy problem of direct optical non-linearity.
3Productivity
If the number of neurons and hidden layers is increased, then computation capability is improved, but device complexity increases
Solution Approach 1:
The patent transitions from two-dimensional planar integration of electrical components to three-dimensional vertical stacking of optical components. Multiple neural network layers are stacked vertically with light sources, waveguides, and photodetectors arranged in three-dimensional space, enabling high-density integration without increasing planar footprint or interconnection complexity.
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
The optoelectronic module achieves high-accuracy non-linear computation with reduced power consumption and complexity, enabling efficient parallel processing in optical artificial neural networks.
Implementation Method 1
a photodetector that receives input light and converts the input light to generate a first current
Implementation Method 2
a light source element that converts the second current to generate output light
Data Source
AI summary
An optoelectronic module according to an embodiment of the present invention includes a photodetector that receives an input light and converts the input light to generate a first current, an electronic element that amplifies the first current to generate a second current, and a light source element that converts the second current to generate an output light. The output light has characteristics of a result of performing a non-linear computation of an optical artificial neural network.