Enhanced DBR mirror design via D3QN: A reinforcement learning approach

Area of Science:

• Optical Engineering
• Artificial Intelligence
• Materials Science

Background:

• Optical systems are crucial for modern electronics and communications.
• Traditional design methods for optical components like distributed Bragg reflectors (DBRs) are simulation-intensive and time-consuming.
• Innovations in optical system design are driven by the need for enhanced performance and miniaturization.

Purpose of the Study:

• To introduce a novel deep reinforcement learning approach for designing distributed Bragg reflectors (DBRs).
• To optimize the multilayer structure of DBRs for improved reflectance and reduced size.
• To compare the efficiency and performance of the proposed method against traditional techniques.

Main Methods:

• Utilized a deep reinforcement learning algorithm, D3QN (Dueling Architecture and Double Q-Network).
• Applied D3QN to optimize the multilayer structure of distributed Bragg reflectors.
• Compared D3QN-designed DBRs with those designed using the transfer matrix method (TMM).

Main Results:

• DBRs designed with D3QN exhibited 20.5% higher reflectance compared to TMM-derived designs.
• The size of DBRs designed using D3QN was reduced by 61.2%.
• The D3QN approach demonstrated superior efficiency over traditional iterative simulation methods.

Conclusions:

• Deep reinforcement learning, specifically the D3QN methodology, presents a promising and efficient alternative for optical design.
• The D3QN method significantly enhances reflectance performance and compactness of DBRs.
• Future work can extend D3QN to complex 2D and 3D optical design structures.