Integrated photonic sensors on silicon nitride platforms are at the forefront of developments for decentralized, real-time detection of biological and chemical substances. Their increasingly precise sensitivity enables broad applications in the medical, pharmaceutical, and chemical sectors. However, attention to the adverse effect of optical loss is often inadequately addressed, limiting the available intensity in the transmission spectrum, thereby impinging on the achievable detection limits. In this computational study, we address the optimization of a microring-based resonator system, harnessing a recently formulated semi-analytical model of surface-roughness-induced scattering for waveguide-based loss optimization. Our focus extends beyond considering sensitivity to propose a comprehensive figure of merit for resonator design by including the quality factor influenced by the coupling coefficient. Simultaneously, we demonstrate how strategic adjustments of gap-coupling metrics can result in significant enhancements in resonator performance. The wavelength range of the target application is set to be around 850nm, focusing on transverse-electric polarization, as well as transverse-magnetic polarization of the input light. The dimensions of the waveguides have been established considering previous studies, with a width falling within 300 and 800nm. Furthermore, the nitride layer thickness will vary between 200 and 400nm, the ring radius between 20 and 60μm and the gap between 200 and 800nm. These parameters form the resonator’s design’s foundational geometry and layer properties. This work emphasizes the need for a holistic design process to fully leverage the potential of integrated photonic devices.
This paper presents a detailed investigation into the propagation loss characteristics of silicon nitride strip waveguides at an 850 nm wavelength, utilizing a random forest model. The primary aim is to optimize low-loss conditions in photonic integrated circuits (PICs). To achieve this, a systematic 2x3 full factorial design of experiments is implemented, focusing on different layers within the PIC framework. The study revolves around a critical examination of how the waveguide width influences propagation loss. Leveraging the random forest model, known for its high precision in complex data analysis, we delve into the correlation between various design elements and their impact on loss. This methodology not only aids in pinpointing the pivotal factors affecting loss but also elucidates their interplay, particularly emphasizing the role of waveguide width. One of the key contributions of this research is the identification of optimal material configurations that significantly reduce loss. This is instrumental in enhancing the efficiency of PICs, a crucial aspect for their performance in applications such as optical communications and photonic computing. Our approach uniquely combines empirical data analysis with machine learning techniques, offering a novel perspective in photonic engineering research. The findings of this study not only shed light on the complex dynamics of waveguide design but also pave the way for the development of more efficient and effective photonic systems. This research stands to make a significant impact in the field, presenting a comprehensive methodology for designing low-loss silicon nitride strip waveguides, thereby contributing to the advancement of photonic technologies.
This study introduces an innovative chip-upending technique to enhance the quality of silicon nitride waveguides by minimizing sidewall roughness, which is critical for reducing optical propagation losses. By reflowing the resist after development, we effectively control the resist expansion effect typically observed in standard procedures. Our analysis indicates that this method can decrease line edge roughness (LER). We employ atomic force microscopy (AFM) to measure the LER of the resist and the waveguide sidewalls via a special tilting technique, ensuring precise characterization of the surface topography. The fabrication is carried out by the choice of a suitable DOE for ensuring the statistical robustness in the evaluation process. Additionally, we conduct propagation and bend loss measurements at 850 nm across waveguides of various widths, with thicknesses ranging from 200 to 400 nm. These measurements in correlation with the roughness analysis confirms that sidewall roughness is indeed smoothed, resulting in notable improvements in light propagation efficiency. The versatility of the low-temperature reflow process is further demonstrated by its compatibility with several waveguide geometry alterations . The inclusion of tree-based modeling in optimizing relevant parameters in the fabrication, foremost the reflow parameters, thereby contributing to a more efficient and controlled reflow process. The findings suggest that our approach can be universally adopted for the fabrication of low-loss waveguides in photonic integrated circuits, with necessary wafer-to-wafer stability.
This study provides an in-depth evaluation of two fundamental techniques for fabricating sensing windows on silicon nitride platforms: a traditional etching strategy using reactive ion etching (RIE) combined with wet etching, and a lift-off-based process in which the top cladding material is deposited onto a suitable resist which is subsequently stripped of the distinct sensing waveguides. The analysis, based on a side-by-side comparison, meticulously examines the effectiveness of these methods. Key evaluation metrics include propagation and bending loss in the sensing windows, process robustness, and uniformity of critical dimensions and heights across the wafer. This will provide a comprehensive understanding of the strengths, weaknesses, and potential application limitations of each technique. An integral part of the study is the careful revision of the waveguide material stack to address specific challenges and applications. This precise tuning and adaptation of the material stack serve as a proxy for the demands likely to be encountered in real-world applications. The conservative etching technique has the advantage that it can be easily combined with subsequent facet etching processes for edge coupling approaches. Conversely, the lift-off resist based approach, despite its relative complexity and sensitivity to high-temperature deposition on the resist, reduces the negative impact of the process on surface roughness and sidewall angles. The knowledge gained from this research provides valuable guidance in the selection of appropriate fabrication techniques for specific silicon nitride sensor applications to increase the robustness of the processing steps for potential mass production stability.
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.
Micro-ring resonators (MRR) are basic photonic components, which serve as crucial building blocks for a variety of devices, e.g. integrated sensors, external cavity lasers, and high speed photonic data transmitters. Silicon nitride photonic platforms are particularly appealing in this field of application, since this waveguide material enables on-chip photonic circuitry with (ultra-) low losses in the NIR as well as across the whole visible spectral range. In this contribution we investigate key performance properties of MRRs in the wavelength range around 850 nm, such as free spectral range (FSR), quality factor (Q factor) and extinction ratio. We systematically investigate a large parameter space given by the MRR radii, coupling gaps between ring and bus waveguide, as well as waveguide width. Furthermore, we compare key properties such as the Q factor between low pressure chemical vapor deposition (LPCVD) and plasma enhanced chemical vapor deposition (PECVD) Si3N4 platforms and find enhanced values for LPCVD ring resonators reaching nearly a Q factor of 106.The fabrication is carried out with standard CMOS foundry equipment, utilizing photolithography and reactive ion etching on 250 nm thick silicon nitride films. As cladding material, high density PECVD silicon oxide is deposited prior to the waveguide onto bare silicon and a sputtered oxide serves as upper cladding. With this process toolbox full CMOS backend compatibility is achieved when considering only PECVD Si3N4 waveguide material. In terms of manufacturability, special focus is put on the die-to-die as well as on wafer-to-wafer variability of the performance parameters, which is crucial when considering mass production of MRR devices. Finally, the experimental findings are compared to finite difference time domain (FDTD) simulations of the MRR circuits revealing excellent agreement when considering the manufacturing variability.
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