PSMC formally established the "Digital Transformation Committee" and is actively developing smart manufacturing technologies and applications, to focus on production intelligence, process intelligence, terminal intelligence and diagnostic intelligence, combining experts from various fields, paired with a deepened infrastructure. The AIoT project connects machines and sub-system parameters to execute digital transformation projects, mainly in optimizing production dispatching and scheduling, automating transmission paths, production quality prediction and monitoring, machine abnormalities detection and major parts residual life-span warning, personnel safety monitoring, energy-saving, improving personnel efficiency and machine productivity, and product yield quality.

In terms of quality control, the production quality system will use machine parameters (FDC/ED data) to conduct real-time anomaly monitoring and checkpoint control, then measuring machines will be used to confirm product quality. In addition, AI and big data technologies have been introduced into products' pre-production, in-production, and post-production stages to achieve machines' and products' 3P (Prediction, Prevention, and Protection). By flexibly utilizing AI technologies, the anomalous conditions of production machines and product quality can be predicted or controlled in real time for improvement measures to be taken in a timely manner.

PSMC started the implementation of smart-manufacturing-related industry-academia collaborative projects with the semiconductor academies at National Tsing Hua University and National Cheng Kung University in 2024. A total of 19 projects have been presented (including completed ones and those still in development), which will provide great help for the Company's product manufacturing process, quality, and energy efficiency. The concept of ESG green manufacturing is highly emphasized during the development of smart manufacturing technologies. Topics encompassed in the projects included using big data analysis and AI algorithms to construct air compressor prognosis-based maintenance and machine scheduling models, methods to optimize transmission order/allocation under limited transmission resources, and more. The Automatic Virtual Metrology (AVM) conduct production quality control and application Prognostics and Health Management (PHM) improve equipment maintenance efficiency, hoping that the academia's research and technology achievements can be introduced through the collaboration, and also apply big data analysis to different fields to cultivate more professional and research talents, and to help PSMC build a comprehensive smart factory environment.