In the semiconductor industry, integrated circuit (IC) processes play a vital role, as the rising complexity and market expectations necessitate improvements in yield. Identifying IC defects and assigning IC testing tasks to the right engineers improves efficiency and reduces losses. While current studies emphasize fault localization or defect classification, they overlook the integration of defect characteristics, historical failures, and the insights from engineer expertise, which restrains their effectiveness in improving IC handling. To leverage AI for these challenges, we propose DeCo, an innovative approach for optimizing task assignment in IC testing. DeCo constructs a novel defect-aware graph from IC testing reports, capturing co-failure relationships to enhance defect differentiation, even with scarce defect data. Additionally, it formulates defect-aware representations for engineers and tasks, reinforced by local and global structure modeling on the defect-aware graph. Finally, a contrasting-based assignment mechanism pairs testing tasks with QA engineers by considering their skill level and current workload, thus promoting an equitable and efficient job dispatch. Experiments on a real-world dataset demonstrate that DeCo achieves the highest task-handling success rates in different scenarios, exceeding 80%, while also maintaining balanced workloads on both scarce or expanded defect data. Moreover, case studies reveal that DeCo can assign tasks to potentially capable engineers, even for their unfamiliar defects, highlighting its potential as an AI-driven solution for the real-world IC failure analysis and task handling.

Portfolio optimization is a paramount and important issue in the financial technology area. Constructing a portfolio requires simultaneous considerations of both return and risk, and the proposed system utilizes the trend ratio based on funds standardization, which can effectively evaluate the performance of a portfolio by its returns and risks, while the quantum-inspired tabu search (QTS) algorithm can be used to efficiently build the best portfolio with a stable uptrend. As it is difficult for users to observe a large amount of stock data, this system helps investors to deal with portfolio optimization issues and further provides an information visualization interface to analyze more investment situations. The user interface of the proposed system provides a new service for users to get started quickly in stock selection and clearly analyze the performance of portfolios in the different stock markets. The proposed method can also push notifications automatically when the system detects that the trend of the user’s portfolio is going to slow down, and reminds them to invest in new portfolios with a strong uptrend in a timely manner. The system also designs three games. The first two games make people familiar with stocks. The third game combines social computing with gameplay, which helps to simultaneously find a better portfolio in the highly complex data. In addition, our system provides the functions of general financial applications, such as stock price inquiry, financial news, etc.