Semiconductor Manufacturing Automation

By Taha KhanMar 15 2024Reviewed by Megan Craig, M.Sc.

Continue reading to explore the role of automation in semiconductor manufacturing, as well as its challenges, advantages, and future directions.

Image Credit: Macro photo/Shutterstock.com

Semiconductor manufacturing comprises several areas, including manufacturing wafers, chips, and integrated circuits (ICs). The process is delicate and requires sophisticated control of reliability, yield, variability, and quality. Manual control of the processes involved in semiconductor manufacturing can compromise the overall quality of the final products; therefore, automating the semiconductor manufacturing processes is imperative to ensure the effectiveness and correctness of these processes. ¹

Role of Automation in Semiconductor Manufacturing

Like in many industries, automation came in semiconductor manufacturing to complement or replace human operators in tedious and hazardous work. The main role of automation in semiconductor manufacturing is to minimize human involvement, especially in fabrication operations. ¹

The fabrication process in semiconductor manufacturing can be classified into three categories depending on the extent of automation: fully automated, that do not require any assistance from human operators; semiautomated, which may require human intervention, especially moving material from and towards tools; and manual which are operated with computer assistance and are rare in modern semiconductor manufacturing industry. The fully automated fabrication process has saved billions of dollars by improving operational efficiency and reducing misprocessed products, human time and overall cost. ¹

Automation Technologies and Solutions in Semiconductor Production

Automation plays a pivotal role in the semiconductor sector, relying on technologies such as Manufacturing Execution Systems (MES), Automated Test Equipment (ATE), and robotics to enhance productivity, efficiency, and precision. These integrated solutions collaborate to streamline the manufacturing process, enabling swift and efficient production. The absence of automation would pose significant challenges for the industry, making it difficult to meet the substantial demand for semiconductor devices and compromising the ability to maintain high-quality control standards. ³

There are several leading robotic companies that provide automation solutions for semiconductor manufacturing. For instance, KUKA is a robot manufacturing company that gives automation solutions for various industries. In semiconductor manufacturing, robots like KMR iiwa provide safe handling and transportation of sensitive and delicate semiconductor components like masks, chips, and wafers completely autonomously without any need for safety fencing. ⁴

Defect Detection Solution in Semiconductor Manufacturing

In a 2022 study on automation technologies in semiconductor production, researchers addressed the challenge of detecting minute manufacturing defects on semiconductor wafers. Traditional approaches using classical computer vision struggled with recognizing small defect patterns within high-resolution images. The study proposed a novel solution, a Hybrid Multistage System of Stacked Deep Neural Networks (SH-DNN that combines classical computer vision for the localization of fine structures with deep neural networks for classification.

The SH-DNN outperformed existing methods, achieving a 99.5% F1-score, an 8.6-fold improvement in fault detection capabilities. The study emphasizes the importance of real-time processing, utilizing low-complexity models for efficient defect identification. The proposed approach holds promise for improving yield and reducing manufacturing costs in semiconductor production. ²

Benefits and Advantages of Semiconductor Manufacturing Automation

Automated systems can work tirelessly around the clock, significantly boosting production output compared to manual processes. Automation enables precise control over various processing parameters, ensuring consistent product quality since even minor imperfections in a chip can render it unusable. ²

Automation removes humans from these potentially dangerous environments as the semiconductor manufacturing process involves handling hazardous chemicals and materials. Similarly, while the initial investment in automation technology can be high, the long-term benefits outweigh the costs by reducing labor costs, reducing waste, and improving process efficiency. ³

Challenges and Future Trends in Automated Semiconductor Manufacturing

There are some challenges associated with automation in semiconductor manufacturing. For instance, implementing cutting-edge automation technology requires a significant upfront investment, which can be a challenge for smaller companies. Automation can lead to job losses in specific areas of the semiconductor industry, necessitating workforce retraining and reskilling initiatives. Moreover, seamlessly integrating various automation systems and software platforms can be complex and requires specialized expertise. ⁶

Smart Solutions for Semiconductor Manufacturing Challenges

In a 2020 study, researchers focused on addressing challenges in automated semiconductor manufacturing through smart manufacturing techniques. The researchers proposed a dynamic algorithm that leverages genetic algorithms and neural networks for intelligent feature selection by utilizing machine learning and artificial intelligence. The objective of the study was to enhance control over manufacturing processes, addressing challenges such as data volume, quality, and merging. ⁵

The study emphasizes the significance of Industry 4.0, incorporating the Industrial Internet of Things (IIoT) and Machine Learning to optimize production operations. The proposed model aims to provide a solution for cost-effective and sustainable semiconductor manufacturing, demonstrating superiority over traditional methods in terms of accuracy and performance. The researchers highlight the importance of an integrated approach to feature extraction and classification for efficient production and fault detection. ⁵

The future of semiconductor manufacturing is tied to automation and robotics. Progress in Artificial intelligence and Machine learning algorithms is expected to play a prominent role in process optimization, predictive maintenance, and intelligent decision-making within semiconductor fabrication.

Semiconductor manufacturing factories will likely transform into smart factories where interconnected systems leverage data analytics and AI to optimize real-time production processes. ⁶ Similarly, collaborative robots, or cobots, like KUKA's KMR iiwa clean robot, working autonomously alongside human workers, assisting with specific tasks and enhancing safety, will help the semiconductor industry to evolve further. ⁴

See More: Semiconductor Failure Analysis Techniques

References and Further Reading

1. Liao, D. Y. (2010). Automation and integration in semiconductor manufacturing. Semiconductor Technologies. http://dx.doi.org/10.5772/8569
2. Schlosser, T., Friedrich, M., Beuth, F., & Kowerko, D. (2022). Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-021-01906-9
3. 3 Common Automation Solutions in the Semiconductor Industry. Solomon Vision with Intelligence. Retrieved on March 8, 2024 from https://www.solomon-3d.com/3-common-automation-solutions-in-the-semiconductor-industry/
4. KUKA. Clean room robot solutions for semiconductor manufacturing. Retrieved on March 8, 2024 from https://www.kuka.com/en-gb/industries/electronics-industry/automation-in-semiconductor-fabrication
5. Ghahramani, M., Qiao, Y., Zhou, M. C., O'Hagan, A., & Sweeney, J. (2020). AI-based modeling and data-driven evaluation for smart manufacturing processes. IEEE/CAA Journal of Automatica Sinica. https://doi.org/10.1109/JAS.2020.1003114
6. Moyne, J., & Iskandar, J. (2017). Big data analytics for smart manufacturing: Case studies in semiconductor manufacturing. Processes. https://doi.org/10.3390/pr5030039

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