## 1 Introduction
These methods relied on plasma reactions for targeted removal of Si\({}_{3}\)N\({}_{4}\). Mechanical approaches, such as polishing and grinding, were occasionally employed, albeit with limitations in their precision. Although chemical and dry etching techniques demonstrated effectiveness, they often struggled to achieve the precise and selective Si\({}_{3}\)N\({}_{4}\) removal observed with laser ablation in CMOS-MEMS processing, particularly with regard to minimizing impacts on adjacent materials. The coalescence of advanced artificial intelligence (AI) with domain-specific engineering expertise is significantly redefining the trajectory of contemporary engineering practice. Recent advancements in temperature monitoring [5], indoor air quality monitoring [6], thermal noise decoupling [7; 8], and combustion monitoring [9] exemplify the transformative impact of AI across diverse engineering applications. The strategic integration of field-specific data and features within intelligent models unlocks unprecedented levels of automation and predictability. This synergistic dynamic empowers engineers to optimize resource allocation, enhance design and operational efficiency, automate routine tasks, and unveil novel insights through data-driven discovery. This study uses the XGBoost algorithm [10] compared with famous classifiers, which are Logistic Regression [11] and Random Forest [12] in supervised machine learning (ML) to categorize the quality of laser ablation on Si\({}_{3}\)N\({}_{4}\) film. The study imports the dataset into the unsupervised ML k-means algorithm to label the data by a k value. This intrinsic property of the k value is elaborated by the data science analysis. XGBoost, Logistic Regression, and Random Forest stand out as widely recognized and highly effective techniques in predictive modeling tasks. Developed by [PERSON] and [PERSON] (2016) [10], XGBoost is a gradient boosting algorithm known for its exceptional performance in a range of applications, including regression, classification, and ranking. XGBoost excels in its capacity to tackle diverse and high-dimensional datasets while mitigating overfitting, a ubiquitous challenge in machine learning. Leveraging an ensemble of decision trees, XGBoost iteratively optimizes a loss function, enabling it to capture complex relationships and achieve state-of-the-art performance. While other algorithmspossess distinct strengths, XGBoost offers a compelling combination of versatility and accuracy. Logistic Regression, for instance, presents a clear and interpretable model for binary classification tasks. Its simplicity facilitates understanding the underlying relationships within the data. Random Forest, conversely, capitalizes on an ensemble of decision trees to bolster predictive power and manage overfitting, offering an alternative approach to complex problems. Due to its applicability in contemporary booming industries such as system on chip (SoC), chip on wafer on substrate (CoWoS) packaging, and CMOS-MEMS chip fabrication, laser micromachining has garnered significant global attention and resources in semiconductor post-processing. The research led by [PERSON] [13; 14] at the Optoelectronics Research Centre, University of Southampton, UK, and [PERSON] [15; 16] at the University of Tokyo, Japan, stand at the forefront. Both teams employ deep learning to simulate images of laser ablation, significantly advancing the fundamental models of laser ablation technology. However, the major limitation of deep learning lies in the discernibility of features. Neither of the two teams has been able to propose distinctly identifiable features linking the practical operational parameters of laser ablation to the final ablation quality. In contrast, when using operational parameters as features for machine learning, the quality of laser ablation, whether good or bad, can be confirmed by more precise machine learning algorithms.