The Urgency of Moving from Lean Semiconductor Manufacturing to Smart Semiconductor Manufacturing

As competition intensifies in the semiconductor industry, manufacturers face unprecedented challenges. While traditional lean manufacturing models have improved production efficiency and cost control to some extent, their limitations are becoming increasingly apparent when facing rapidly changing market demands and complex and ever-changing supply chain environments. Therefore, it is time to move from lean semiconductor manufacturing to smart semiconductor manufacturing.

Limitations of Lean Manufacturing

Maintaining lean manufacturing without moving towards smart manufacturing will severely limit the competitiveness, innovation, agility, and risk management capabilities of semiconductor manufacturers.

Specifically, lean manufacturing has significant shortcomings in the following aspects:

**Production planning relies on manual labor:**

Manual scheduling calculations are slow and lack rationality, resulting in limited delivery accuracy and stability, extended order turnaround times, and low factory capacity utilization. These problems directly impact customer satisfaction and the company's market competitiveness.

**Lagging quality control:**

Under the lean manufacturing model, the sampling and measurement of many processes are delayed, making timely feedback and adjustments difficult. Furthermore, relying on manual experience and empirical formulas to adjust production parameters makes it difficult to fully consider the complex relationships between production factors and their impact on quality, leading to poor product consistency. **Production planning relies on manual labor:**

In the lean manufacturing model, the sampling and measurement of many processes are delayed, making timely feedback and adjustments difficult. At the same time, relying on manual experience and empirical formulas to adjust production parameters makes it difficult to fully consider the impact of complex relationships between production factors on quality, resulting in poor product consistency.

**Fragmented Quality Management Platform:

Quality-related systems are scattered, data is not integrated, and there is a lack of a unified QMS system covering all factories and processes. This not only consumes a lot of manpower but also causes problems with data accuracy and information lag, seriously affecting the efficiency and effectiveness of quality management.

**Lack of Data Support for Operational Analysis:

Operational analysis data relies heavily on manual aggregation and analysis, resulting in poor data accuracy and timeliness. Manual reports make it difficult to perform drill-down analysis of indicators, and sensitive data information is difficult to obtain, making it difficult for enterprises to make scientific and accurate decisions.

Digital Transformation Strategies for a Smooth Transition to a Smart Manufacturing Environment

To achieve a smooth transition from lean manufacturing to smart manufacturing while minimizing disruption to current processes, we propose solutions along three digital transformation tracks. These solutions aim to reduce risk, increase product success rates, boost throughput, and accelerate the rapid development of the semiconductor business.

1. Virtual Manufacturing Process

Core Strategy: Utilize virtual representations to simulate all nodes and lines of the manufacturing process to gain insights for optimizing production, eliminating defects, and redesigning, thereby increasing NPI throughput and shortening time-to-market.

Implementation:

• Create a virtual manufacturing environment that simulates each stage of the actual production process.

• Continuously perform "what-if" analyses based on the current model to evaluate performance and results under different production conditions. • Optimize production processes through a virtual environment, reducing defects and the need for redesign. **Advantages:** • Minimize disruption to current lean manufacturing processes while achieving a smooth transition to a smart manufacturing environment. • Reduce production risks and increase first-pass yield.

2. Real-time Manufacturing and Reporting Analysis**Core Strategy:** Execute advanced semiconductor manufacturing operations by collecting manufacturing execution and data in real time to achieve continuous learning and closed-loop quality improvement. Implementation: • Deploy a real-time data collection system to monitor key indicators and parameters in the production process. • Utilize real-time reporting and analysis tools to conduct in-depth analysis and mining of the collected data. • Adjust production parameters and processes based on the analysis results to achieve manufacturing optimization. Advantages: • Utilize continuously updated simulation models for manufacturing optimization, improving production efficiency and quality. • Quickly identify and predict production and quality problems before they occur, reducing losses and risks.

3. Signatureless Collaboration

Core Strategy: Achieve seamless collaboration between the virtual and real worlds, supporting continuous current data flow across all design and engineering disciplines.

Implementation:

• Establish a unified platform for storing and managing all relevant design and manufacturing data.• Support real-time data sharing and collaboration across different departments and teams.• Leverage AI and analytics to provide predictive insights, creating new opportunities to optimize manufacturing processes.

Advantages: • Eliminate data silos, achieving end-to-end visibility throughout the entire production process.

• Improve decision-making efficiency and accuracy to support intelligent business, engineering, and manufacturing decisions.

Intelligent Transformation and Digital Development of the Semiconductor Manufacturing Industry

To improve the efficiency and quality of semiconductor manufacturing, we propose a comprehensive intelligent solution aimed at optimizing semiconductor production across multiple aspects, including advanced planning, Industrial Internet of Things (IIoT), Manufacturing Execution Management (MEM), application development platforms, manufacturing process planning, and quality management.

**I. IC Manufacturing Preparation**

Through advanced planning, scheduling, and factory and process simulation technologies, we provide comprehensive preparation for semiconductor production. These technologies ensure seamless execution of the manufacturing process at every stage and all sites, thereby improving production efficiency and product quality.

**II. Industrial Internet of Things (IIoT)**

We leverage IIoT technology to connect production assets, collect data in real time, and provide deep insights to optimize operations.

Industrial Internet of Things (IIoT) technologies can significantly transform semiconductor manufacturing processes by improving production efficiency and product quality, and supporting new business models.

III. Manufacturing Execution Management (MEM)

MEM provides real-time visibility, helping to optimize production processes and improve flexibility and control. By monitoring operations, orchestrating resources, tracking products, and ensuring compliance, MEM systems ensure the smooth operation of semiconductor production.

IV. Application Development Platform

Our application development platform enables experts to create custom applications with minimal coding, thereby extending traditional software and accelerating innovation. Leveraging data, we can improve the efficiency of semiconductor product development, manufacturing, and supply chain processes.

V. Manufacturing Process Planning

Through simulation, verification, and process simplification, we improve semiconductor manufacturing processes to increase efficiency, shorten time-to-market, and enhance team collaboration. These optimizations help semiconductor manufacturers maintain a competitive edge.

VI. Quality Management

We integrate quality management throughout the entire semiconductor product lifecycle to ensure products exceed customer expectations. By improving product reliability, reducing costs, and ensuring compliance, we are committed to protecting brand reputation and customer satisfaction.

VII. Hanpeng Industrial AI Application Platform

The Hanpeng Industrial AI Application Platform provides powerful technical support for the semiconductor industry through its intelligent knowledge base, AI+ tool integration, and data integration and analysis functions. It helps enterprises achieve digital transformation, improve production efficiency and product quality, reduce costs, and enhance market competitiveness.

1. Intelligent Knowledge Base and Automated Document Processing: The Hanpeng AI Application Empowerment Platform helps semiconductor companies build their own enterprise-specific knowledge bases, upload documents of various formats for training, and automatically organize language to generate logically clear and structurally complete working documents. This helps improve the efficiency and quality of semiconductor companies in areas such as technical document management and product design specifications.

2. AI+ Tool Integration and Multimodal Task Implementation: The platform provides integration with AI+ tools, including online search, multimodal large models, and enterprise-owned platforms, enabling multimodal tasks such as speech recognition, image processing, video analysis, and natural language understanding. In the semiconductor industry, these functions can be used to improve the automation level of production processes, such as chip inspection through image processing technology or optimizing customer service using natural language processing technology. 3. Data Integration and Analysis: Hanpeng AI Platform helps semiconductor companies integrate scattered data resources, providing a one-stop AI development and deployment solution, thereby improving the efficiency and accuracy of data analysis. This is crucial for solving the problems of reliance on manual labor and data silos in the semiconductor industry in areas such as production planning, quality control, and operational management. 4. Improve Production Efficiency and Quality: Through AI technology, semiconductor companies can optimize production parameters, reduce reliance on human experience, and improve product consistency. AI can help analyze complex production data, predict and adjust production parameters to achieve more efficient production processes and more stable product quality. 5. Predictive Maintenance and Production Line Optimization: Combined with a CIM system, the Hanpeng AI platform can achieve predictive maintenance and production line optimization. It utilizes collected product data, equipment data, and environmental data for real-time analysis, adjusting processes promptly to achieve intelligent manufacturing. This helps reduce equipment failure rates and improve production efficiency and product quality. 6. Chip Design and Manufacturing: The application of AI technology in chip design and manufacturing, such as EDA+AI tools, can improve design efficiency and optimize power consumption, performance, and area (PPA). Furthermore, AI can also be used for chip inspection, improving inspection efficiency and accuracy.

Leading Technology is committed to driving the digital transformation of the semiconductor industry.

**1. Improve Decision-Making Efficiency:**

By introducing intelligent systems, companies can obtain production data and market information in real time, enabling better decision-making. This helps achieve faster NPI (New Product Introduction) and time-to-market, while increasing output and quality, approaching zero defects.

**2. Reduce Costs and Improve Efficiency:**

Investing in digital manufacturing solutions and technologies can help companies reduce costs and improve efficiency. Intelligent systems can optimize production planning, reduce errors and time consumption in manual scheduling, and improve factory capacity utilization and customer satisfaction.

**3. Drive Innovation and Development:**

Smart manufacturing brings unprecedented innovation opportunities to the semiconductor industry.

By leveraging cutting-edge technologies, talent, and collaboration, businesses can thrive in this dynamic environment, driving industry transformation and progress. 4. Ensuring Supply Chain Security: Faced with the rapid growth in semiconductor demand from downstream industries such as automotive, smart manufacturing helps protect the security and stability of the chip supply chain. By working closely with semiconductor manufacturers, businesses can navigate this rapidly evolving ecosystem and deliver next-generation smart solutions.