CART-based optimization of core and cladding layers in silicon nitride photonic integrated circuits towards propagation and bend loss minimization

In the burgeoning field of sensing, Photonic Integrated Circuits (PICs) are essential tools for precise, high-speed detection of biological markers and particles. The performance of these biosensors is intricately linked to the losses of PICs, which is largely determined by the configuration of their core and cladding layers. Recognizing this, the present study ventures into the optimization of these layers in Silicon Nitride (Si3N4) PICs, employing an innovative approach using Classification and Regression Trees (CART). The study identifies propagation and bend losses, two critical factors affecting PIC performance, as response variables. In contrast, the physical characteristics of the core and cladding layers are considered as input variables. To ensure the robustness and completeness of the study, an appropriate Design of Experiments (DOE) is implemented, meticulously exploring possible combinations of layer configurations. Following the DOE, the CART algorithm is then applied to this design space, whereas the losses act as response variables. The algorithm functions by partitioning the design space into regions associated with specific layer configurations and iteratively refines these partitions based on their corresponding impact on propagation and bend losses. The end results of this process is the statistical information about the layer stacks which come with significantly low propagation and bend losses, thereby enhancing PIC performance. This improvement in performance directly translates to heightened sensitivity and specificity in biosensors. Further, the application of the CART methodology has demonstrated its potential to streamline the PIC design process, enhancing its robustness, an aspect critical for practical implementation in fabrication environments.