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Optical multilayer thin film structures are widely used in many photonic applica- tions, including filters, absorbers, photovoltaics, display devices. The important part to enable these applications is the inverse design, which seeks to identify a suitable structure that satisfy desired optical responses. Recently, a Foundation model-based OptoGPT is proposed and has shown great potential to solve a wide range of inverse design problems. However, OptoGPT fails to design certain types of optical responses that are important to practical applications. The major rea- son is that the training data is randomly sampled and it is highly probable that these design targets are not selected in training, leading to the out-of-distribution issue. In this work, we propose a self-improving data augmentation technique by leveraging neural networks’ extrapolation ability. Using this method, we show sig- nificant improvement in various application design tasks with minimum fine-tuning. The approach can be potentially generalized to other inverse scientific foundation models. 

Optical multilayer thin film structures have been widely used in numerous photonic applications. However, existing in- verse design methods have many drawbacks because they either fail to quickly adapt to different design targets, or are difficult to suit for different types of structures, e.g., designing for different materials at each layer. These methods also cannot accommodate versatile design situations under different angles and polarizations. In addition, how to benefit practical fabrications and manufacturing has not been extensively considered yet. In this work, we introduce OptoGPT (Opto Generative Pretrained Transformer), a decoder-only transformer, to solve all these drawbacks and issues simultaneously.