Zernike Coefficient Prediction for Faster Optical Aberration Correction

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Solution Overview

Problem

Conventional methods for correcting optical aberrations are computationally intensive, leading to increased calculation time, which needs to be reduced for efficient aberration correction.

Innovation Solution

A light correction coefficient prediction method that involves acquiring intensity distributions, generating comparison data between actual and target distributions, and using a learning model to predict coefficients for aberration correction, thereby compressing data and shortening calculation time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional methods are used to derive coefficients for aberration correction, then accurate correction can be achieved, but the calculation time increases

Engineering Contradiction:

Improveaberration correction accuracyVSAvoidcalculation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent applies preliminary action by pre-training a learning model with aberration correction data before actual use. The model is trained in advance using intensity distributions and corresponding correction coefficients, so that when actual aberration correction is needed, the pre-trained model can quickly predict coefficients without performing the full conventional calculation process, thus reducing calculation time while maintaining accuracy

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent substitutes the mechanical calculation system with a learning model-based prediction system. Instead of using conventional iterative optimization algorithms to derive correction coefficients, the system uses a trained neural network model to predict coefficients directly from intensity distribution data, replacing computationally intensive mechanical calculations with faster model inference

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If conventional calculation methods are used for aberration correction, then correction coefficients can be derived, but the computational complexity increases

Engineering Contradiction:

Improvecorrection effectivenessVSAvoidcalculation complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces complex mechanical calculation processes with a learning model that has already processed the complexity during training. The conventional iterative optimization and coefficient derivation processes are substituted with a single forward pass through a pre-trained neural network, significantly reducing computational complexity while maintaining correction effectiveness

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent uses copying by creating a learned representation of the aberration correction process through the learning model. The model learns and stores the relationship between intensity distributions and optimal correction coefficients during training, creating a copied knowledge base that can be quickly applied without re-running the complex original calculation process

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20240185125A1Optical correction coefficient prediction method, optical correction coefficient prediction device, machine learning method, machine learning preprocessing method, and trained learning model

Publication Date: 2024.06.06 HAMAMATSU PHOTONICS KK

AI summary

A control device includes: an acquisition unit that acquires an intensity distribution along a predetermined direction for an intensity image obtained by observing an action caused by light corrected using a spatial light modulator based on a Zernike coefficient; a generation unit that calculates a comparison result between the intensity distribution and a target distribution to generate comparison data; and a prediction unit that predicts a Zernike coefficient, which is for performing aberration correction related to the light so that the intensity distribution approaches the target distribution, by inputting the comparison data and the Zernike coefficient, which is a basis of the intensity distribution, to a learning model.