Recognizing Beam Profiles from Silicon Photonics Gratings using Transformer Model
This work addresses a specific challenge in ion trap quantum computing for researchers, but it is incremental as it applies an existing transformer model to a new domain with modest accuracy improvements.
The paper tackled the problem of determining height categories of beam profiles from silicon photonics gratings using infrared cameras, by developing transformer models with input patches and sequences, achieving recognition accuracies up to 0.938 and 0.936 respectively.
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.