Freeform Surface Imaging Optical System Design via Iterative Optimization
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Solution Overview
Problem
Conventional methods for designing freeform surface imaging optical systems are not intelligent or automated, heavily reliant on human intervention and starting points, making them inefficient, especially for systems with advanced specifications, and often fail to find suitable starting points.
Innovation Solution
A method involving the selection of feature rays, surface fitting based on Snell's law, and iterative processes to design freeform surfaces, which includes establishing initial surfaces, defining constraints, and iteratively optimizing surface positions and tilts to achieve optimal optical systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional optimization methods are used, then design flexibility is maintained, but design time and human effort increase significantly
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing optimal surface parameters in a database before the actual design process. When designing an optical system, the method queries the database for pre-computed surfaces that match the required object-image relationships, eliminating the need for time-consuming iterative optimization during the design phase.
Solution Approach 2:
The patent creates a database of pre-computed freeform surfaces that can be copied and applied to different optical design scenarios. Instead of重新 optimizing surfaces for each design, the method selects and adapts pre-existing surface solutions from the database, significantly reducing design time while maintaining quality.
2Manufacturing precision
If advanced freeform surfaces are designed for high-performance systems, then imaging quality improves, but finding suitable starting points becomes difficult
Solution Approach 1:
The patent implements a self-service mechanism where the database automatically stores and organizes optimal surface parameters generated from previous designs. The system serves itself by providing ready-to-use starting points for new designs, eliminating the need for manual intervention to find suitable initial configurations for complex freeform surfaces.
Solution Approach 2:
The patent changes the approach from manually adjusting starting parameters to querying pre-computed parameter sets from the database. The method transforms the design process by switching from iterative parameter tuning to direct parameter selection based on object-image relationship queries, making the process of finding starting points systematic and automated.
3Extent of automation
If conventional optimization algorithms are used, then design adaptability is maintained, but automation level remains low
Solution Approach 1:
The patent implements feedback by continuously updating the database with newly computed optimal surfaces. When a new surface is designed and validated, its parameters are fed back into the database, improving future query results. This creates a self-improving system that becomes more automated and adaptive over time.
Solution Approach 2:
The patent creates a universal database that stores freeform surface parameters applicable to multiple different optical design scenarios. A single pre-computed surface can serve as a starting point for various imaging systems with different requirements, making the automation process versatile and broadly applicable across different design problems.
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
This approach enables the design of freeform surface imaging optical systems with improved accuracy and reduced human effort, achieving high image quality and minimizing distortion, thus overcoming the limitations of conventional methods.
Implementation Method 1
solving a plurality of first intersections of the plurality of feature rays Ri (i=1, 2 . . . K) with the first freeform surface a point by point based on a given object-image relationship and a vector form of Snell's law
Data Source
AI summary
An initial system and a constraint condition are established. All freeform surfaces are obtained by surface fitting the feature data points to form a first freeform surface imaging optical system. The first freeform surface imaging optical system is taken as the initial system for multiple iterations to obtain a second freeform surface imaging optical system. The second freeform surface imaging optical system is taken as a first base system. A first surface freedom of the first base system is selected, the values nearby the first surface freedom is selected, and surface positions and tilts of the first base system are changed to obtain a third freeform surface imaging optical system that satisfies the constraint condition. A second base system is selected and the method above is repeated. The freeform surface imaging optical system is obtained until all freedoms for surface positions and tilts have been used.