Recognizing Beam Profiles from Silicon Photonics Gratings using Transformer Model

TL;DR

This paper presents transformer models to recognize the height categories of silicon photonics beam profiles from IR images, achieving high accuracy and potential applications in beam auto-focusing and alignment.

Contribution

The study introduces transformer-based recognition models trained with input patches and sequences for silicon photonics beam profile analysis, demonstrating their effectiveness.

Findings

Model trained with input patches achieved 93.8% accuracy.

Model trained with input sequence achieved up to 93.6% accuracy after multiple cycles.

Transformer models can be applied to auto-focus and auto-adjust optical systems.

Abstract

Over the past decade, there has been extensive work in developing integrated silicon photonics (SiPh) gratings for the optical addressing of trapped ion qubits in the ion trap quantum computing community. However, when viewing beam profiles from infrared (IR) cameras, it is often difficult to determine the corresponding heights where the beam profiles are located. In this work, we developed transformer models to recognize the corresponding height categories of beam profiles of light from SiPh gratings. The model is trained using two techniques: (1) input patches, and (2) input sequence. For model trained with input patches, the model achieved recognition accuracy of 0.938. Meanwhile, model trained with input sequence shows lower accuracy of 0.895. However, when repeating the model-training 150 cycles, model trained with input patches shows inconsistent accuracy ranges between 0.445 to 0.959, while model trained with input sequence exhibit higher accuracy values between 0.789 to 0.936. The obtained outcomes can be expanded to various applications, including auto-focusing of light beam and auto-adjustment of z-axis stage to acquire desired beam profiles.