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Artificial Intelligence (AI) is changing the world and improving our lives in many ways. The semiconductor industry is a key part of the AI revolution, as it provides powerful computing chips for AI applications and leverages AI to optimize business operations and management, customer service, office workflows, as well as the intricate realms of system, product, circuit design, and manufacturing processes. In this article, we will delve into profound ways in which AI contributes to enhancing manufacturing automation.

What is Artificial Intelligence (AI) Computing?

AI is a field of computer science that focuses on creating systems and machines capable of performing tasks that typically require human intelligence. AI aims to simulate human cognitive functions such as learning, reasoning, problem-solving, perception, language understanding, and beyond. It encompasses various subfields, ranging from machine learning and deep learning to natural language processing, computer vision, robotics, and more.

How does AI computation work?

We often hear that AI relies on machine learning and deep learning computations, which not only require significant hardware resources but also involve highly complex neural network calculations.

The challenge lies in understanding how these complex computations align with practical applications in the real world. To avoid the complex mathematics, we provide a conceptual explanation as shown in Figure 1, presenting a relatively straightforward way to understand it. That is, regardless of the complexity of the underlying calculations, one crucial concept in AI is the notion of “Latent Space in AI Computation“. A latent space represents a lower-dimensional space where complex high-dimensional data is transformed and condensed. This transformation captures essential features or patterns present in the data while reducing its dimensionality, i.e., retaining the important features or patterns in the data while minimizing the data size. The latent space contains a condensed and meaningful representation of the data. Each point in this latent space represents a feature or combination of features found in the original data. The latent space is often found in various subfields. For example, common techniques such as Dimensionality Reduction, Information Compression, Representation Learning, and Data Generation are easily linked to the use of the “Latent Space”. The space then can be broadly connected to real-world human knowledge space encompassing Numerical Data, Image Data, Text Data, and even the intersection of these three domains.

When AI Meets Manufacturing Automation

In recent times, AI has gradually been applied in the manufacturing industry, and its applications are steadily expanding. AI in semiconductor manufacturing often uses traditional algorithms, machine learning or deep learning, and data analytics to optimize and automate various processes in the production line. Here are the summarized applications for the key manufacturing practices within our factory operations:

1. Predictive Maintenance: AI, when integrated with automation systems, enables predictive maintenance. By analyzing data from sensors on machines and equipment, AI can predict when maintenance is needed, allowing for proactive maintenance scheduling, thereby minimizing unplanned downtime. This results in a reduction in machine-related downtime and heightened production efficiency. Regression-type models play a pivotal role in this domain, particularly in handling the variables (predictors) from sensors on machines, recording time series, process methods, material types, and other pertinent variables. Once this data is collected, during the learning process, the model will transform these variables into a latent space for computation to make predictions in the real world, alerting potential issues that the machine may encounter in the future.
2. Quality Assurance: AI-powered automation systems can perform real-time quality control by analyzing data from cameras sensors to detect defects or deviations in products during the manufacturing process. Techniques like supervised learning for Object Detection and Semantic Segmentation, as well as unsupervised learning for Anomaly Detection, are widely used in visual analysis. These image-based models often utilize deep learning’s neural network layers to transform data into a latent space, subsequently providing probabilistic predictions to pinpoint the areas of defects and their corresponding categories. These advanced techniques can perform visual inspections, detect product defects, and automatically remove abnormal items, thereby ensuring overall product quality.
3. Manufacturing Process Parameter Optimization: AI algorithms can optimize manufacturing processes by analyzing a diverse range of datasets to eliminate inefficiencies. For instance, prior to the commencement of manufacturing process, AI can simulate and analyze a vast amount of historical data to generate the most effective design solutions, thereby reducing product development time. Interpretable AI models are widely used at this stage, providing decision-making support to users. During the manufacturing process, AI meticulously analyzes extensive data, encompassing machines, recipes, operators, and environmental factors, to ascertain the most efficient parameter settings. In addition to common models like numerical prediction models and ensemble learning, large-transformed neural network models that consider various factors are also frequently used. The more complex models often involve a larger latent space. This thorough analysis significantly enhances both production efficiency and product quality.
4. Robotic Process Automation (RPA): AI-driven automation can be used to automate repetitive and rule-based tasks through RPA. This can include tasks like data entry, order processing, financial reports, and other administrative functions, making human resources available for more strategic and creative roles. Common AI tools for image and text recognition, such as OCR technology, are particularly effective in assisting humans. These tools often help administrative personnel with the swift conversion of figures, tables, and text into data, seamlessly integrating them into reporting systems, and monitor changes in data. These automations can help in prompt identification and exclusion of anomalies.
5. Supply Chain Optimization: AI extends its impact to optimizing the entire supply chain, including procurement, demand forecasting, orders, logistics, and financial management. The insights derived from AI analysis can forecast market trends and strategize production flows, resource allocation, and inventory management. Numerical regression models, pricing models, and time series models are most frequently employed. This ultimately increases workflow efficiency and reduce expenses.

What are the Benefits of AI-Enhanced Automation?

When AI is integrated with automation, it brings many benefits across diverse industrial applications, enhancing efficiency, productivity, accuracy, and fostering innovation. Here is the summary of the key benefits:

• Improved Accuracy and Precision: AI can enhance the accuracy and precision of automated processes by continuously learning and adjusting based on data analysis. This minimizes errors, enhances quality, and ensures a consistent level of performance.
• Increased Efficiency and Productivity: AI-powered automation streamlines processes, reduces manual intervention, and optimizes workflows. This results in faster task completion, heightened output, and overall increase in efficiency throughout the organization.
• Cost Savings: By automating routine and repetitive tasks, businesses can significantly reduce labor costs and operational expenses. AI-enhanced automation optimizes resource allocation and minimizes wastage, leading to substantial savings overall.

AI in ASE: The Future of Smart Manufacturing

AI and automation synergize to create smart, efficient, and agile manufacturing systems. AI contributes immense value to automation by providing intelligence, learning capabilities, and predictive analytics, thereby elevating decision-making processes and optimizing overall operations. Our long term goal is to build a more competitive and productive smart manufacturing ecosystem. It is important to recognize that humans are the brains behind artificial intelligence and machine learning. Our aspiration is to apply AI to ensure precision and accuracy in decision making, and facilitate greater automation.