We introduce machine learning-based solutions for differentiating wafers fabricated using trusted and untrusted mask-sets based on the typical metrology or wafer acceptance tests collected during semiconductor manufacturing and testing. Our methods leverage the systematic nature of process variation and capture the subtle causality between mask modifications and either physical dimensions or electrical characteristics of the produced silicon, which can then be used for the purpose of wafer-level mask-set attestation. Effectiveness of our solutions is demonstrated on a dataset of inline and e-test measurements from 8000 wafers fabricated with multiple variants of a mask-set in the GlobalFoundries 12LP FinFET technology node.

We demonstrate the value of integrating expert-level domain knowledge into Machine Learning (ML) model training, which becomes particularly important when modeling complex processes such as semiconductor manufacturing. Specifically, we discuss a machine learning-based methodology which correlates physical metrology measurements with process control monitoring electrical measurements by employing Multivariate Adaptive Regression Splines (MARS) and Non-Dominating Sorting Genetic Algorithm II (NSGA-II). Baseline effectiveness of this solution in predicting critical measurements for maintaining fabrication process integrity, such as yield shorts, ring oscillator active mode current (IDDA) and frequency differences, is assessed using actual High Volume Manufacturing (HVM) production data from an advanced FinFET technology node. Further improvements, however, can be obtained by leveraging domain-specific expertise. Indeed, as we demonstrate experimentally, model accuracy, training time, and explainability all improve when such expertise is integrated in the training process. Our results highlight the pitfalls of blindly applying machine learning and illustrate the value of including semiconductor experts in the development of machine learning models for process optimization-related tasks.

The integration of computer-supported virtual reality (VR) in collaborative learning environments is rapidly evolving, presenting new opportunities and challenges for educators and learners. This paper examines the current trends and developments in VR-enhanced collaborative learning, focusing on the technological advancements. pedagogical strategies, and educational outcomes. Through a comprehensive review of recent studies and practical applications, we identify key factors that influence the effectiveness of VR in fostering interactive and immersive learning experiences. The findings highlight the potential of VR to enhance student engagement, improve spatial understanding, and facilitate experiential learning. Additionally, the paper discusses the implications of VR for future educational practices and suggests areas for further research to address the existing limitations and optimize the use of VR in collaborative learning settings.

In the era of globalization, teachers primarily assist students in applying realistic learning environments, helping them integrate the knowledge acquired from textbooks with clinical practical issues and develop problem-solving skills. The advancement of mobile technology allows students to access learning resources and receive accurate guidance in a virtual environment, promoting safe and effective learning. This research proposes a computer-supported collaborative learning environment to support the learning of obstetrics and gynecology four-stage palpation professional courses. A quasi-experimental design is conducted to validate the impact of this method on students' learning achievements, engagement, and satisfaction. The experimental results demonstrate that the proposed approach enhances students' learning achievement, critical thinking, and problem-solving competence. Moreover, students engaged in scenario-based learning show more proactive learning behavior compared to the control group. Based on these findings, specific recommendations for developing effective learning strategies and incorporating computer-supported collaborative learning environments in medical and healthcare educational materials are suggested.

Computer-supported collaborative learning (CSCL) has undergone significant advancements in the past two decades, revolutionizing the way learners engage with educational content and peers. This abstract explores key trends in CSCL, including the evolution of online communication and collaboration platforms, the impact of mobile technologies, the integration of data analytics, and the emergence of virtual reality (VR) and augmented reality (AR) environments. Additionally, the ongoing pandemic has accelerated the adoption of online and blended learning models, further shaping CSCL. These trends have transformed collaborative learning, allowing learners to exchange ideas, co-create knowledge, and engage in meaningful discussions. The utilization of data analytics enables personalized instruction and targeted support, enhancing learners' engagement and motivation. Immersive VR/AR environments promote active participation and deeper learning. Looking ahead, hybrid models combining face-to-face and online collaboration are likely to shape the future of CSCL. As instructional practices adapt and technology advances, creating engaging and effective learning environments remains a crucial focus in CSCL.

Fetal appearance and proper position evaluation are crucial for determining the best labor treatments. Healthcare or midwifery nurses, require knowledge about the fetus's position and health to make decisions on whether to induce labor. In addition, attending to the patient's needs throughout labor is essential. The fetal positioning and monitoring process may be uncovered with the use of Leopold's techniques. Students find it challenging to apply what they learn in the classroom to real-world challenges since standard training lacks chances for clinical experience and application. This study aims to investigate the impact of integrating online game-based environments with gamification and a self-regulated learning (SRL) strategy on nursing students' Leopold's maneuvers skills. The use of games as a learning tool in education has gained popularity due to its ability to provide a playful and motivating experience for students, as well as fostering cooperation among them. However, the application of game-based learning and gamification in nursing skill instruction remains understudied. The researchers propose integrating an online game-based environment called Gather with gamification features and a self-regulated learning strategy for nursing students. The self-regulated learning approach in this study includes goal setting, performing tasks with gamification elements such as points, badge, leaderboards, and self-evaluation. An experiment will be conducted at a nursing school in a Taiwan university using a quasi-experimental design to investigate the learning performance, motivation, self-efficacy and perceptions.