Deep learning-enabled framework for automatic lens design starting point generation.

Area of Science:

• Optical Engineering
• Computational Optics
• Artificial Intelligence in Optics

Background:

• Traditional lens design is iterative and requires expert knowledge.
• Automating the initial stages of lens design can accelerate the development process.
• Existing methods may struggle with complex, real-world lens configurations.

Purpose of the Study:

• To develop a modular deep neural network (DNN) framework for automated lens design starting point inference.
• To create a system capable of handling diverse and complex lens structures.
• To provide a practical tool for lens designers to generate high-quality initial designs.

Main Methods:

• Implementation of a modular deep neural network (DNN) architecture.
• Training the model on a dataset of 80 different lens structures.
• Validation of inferred designs against established optical performance benchmarks.
• Development of a web application utilizing the trained DNN model.

Main Results:

• The DNN framework successfully infers lens designs for complex systems like Cooke Triplets and Double Gauss lenses.
• Inferred designs using realistic glass materials show optical performance comparable to literature reference designs.
• The model demonstrates versatility across 80 distinct lens structures.
• A web application is released, offering direct access to design starting points based on user specifications.

Conclusions:

• The proposed DNN framework offers a powerful and flexible approach to automating lens design.
• The tool provides valuable, high-quality starting points, complementing traditional lens design workflows.
• The accessible web application democratizes access to advanced lens design assistance for the optics community.