by Emily Newton
In the fast-paced world of semiconductor manufacturing, staying ahead of the competition is crucial. Manufacturers must continually explore accessible ways to increase output, tighten quality control and tackle customers’ orders. Semiconductor manufacturing automation is the answer. By integrating it into existing workflows, employees unlock new levels of productivity and efficiency.
Achieving Semiconductor Manufacturing Automation With Robots
Many semiconductor fabrication facilities include advanced robots to support workflows. Although these high-tech machines usually do not replace humans, they can align with people’s roles, giving those individuals more time to handle the duties robots cannot do.
Some of this equipment features thoughtful designs that make them compatible with electronics manufacturers’ clean rooms. Those tightly controlled environments feature extremely low contaminant levels to prevent issues that could compromise component performance and quality.
Decision-makers often have high hopes for factory automation outcomes. In one example, the leaders of a Japanese semiconductor fab plan to use robots and artificial intelligence to accelerate factory processes. They anticipate that this change will allow the facility to achieve delivery times that are one-third faster than competitors can do.
Some facilities also use autonomous mobile robots to move materials between different parts of a factory. This strategy saves people time and lets them spend more time on value-added tasks that provide more meaning for employees and their companies. Letting robots handle the heavy lifting also reduces strain and injury-related risks that could require people to take time off to recover.
Leaders interested in bringing robots into semiconductor fabrication facilities should consider which tasks they want to automate and the expected benefits. They must also set budgets and implementation time frames to make technology installations go smoothly. Giving employees enough time to learn how robots fit into their roles will also help them embrace the technology and recognize how it can help them.
Automating the Semiconductor Characterization Process
The United States accounts for about 12% of worldwide semiconductor manufacturing capacity. Maintaining or growing that segment requires manufacturers to have resilient and responsive supply chains that allow them to get the necessary materials to keep production flowing.
However, before those entities confirm what they need and the particular quantities, people within research and development departments will oversee the semiconductor characterization process. It encompasses techniques to learn more about a semiconductor crystal’s physical properties.
This step usually occurs immediately after crystal synthesis, and the goal is to determine whether it would suit specific semiconductor applications. Those involved used structural, optical and electrical techniques to increase their understanding.
Researchers have developed an automated technique for semiconductor characterization. It involves using adaptive computer vision technologies that work 85 times faster than non-automated approaches. Additionally, experiments showed that automation could compute the band gaps of 200 compositions in six minutes or reveal the environmental stability of those options in 20 minutes.
This automation allows people to analyze sample data in parallel, shortening the overall timeframes and increasing decision-makers’ confidence about future semiconductors’ performance.
Exploring Process Improvements in Semiconductor Manufacturing
Executives also get excellent results when they examine how automation could change specific parts of processes commonly prone to errors or those that require extraordinary care to do correctly. One example is the gas-assist technology that ensures laser cutters have the gaseous mixtures to support the cutting process and influence the quality of the results.
Laser-cut semiconductors are common in small-form-factor products, such as smartphones. However, creating the appropriate gas mixture requires understanding material properties, the laser’s characteristics and other specifics. Evidence suggests automation and machine learning could enhance outcomes through data analysis. When algorithms can process vast amounts of information and provide recommendations in seconds, laser cutter operators can get the best results even with limited experience.
Some companies also offer autonomous control systems for semiconductor factories and other industrial facilities. Applying them to clean room environments reveals the exciting possibilities. Managers frequently use automated particle-counting equipment to verify that the environment stays within the necessary limits.
Another option is to automate the HVAC system so the equipment minimizes energy use and maintains the required conditions. One manufacturer installed automated technology that relies on AI to monitor the area and make the necessary adjustments. Statistics revealed that the company’s HVAC equipment accounted for 30% of its overall energy consumption, making leaders eager to improve things where possible. The factory is also in a relatively cool climate where temperatures vary seasonally.
The AI technology required approximately 20 iterations to develop a process model appropriate for running the system during production. It then continued making adjustments to reflect production volumes and seasonality. Eventually, the automation resulted in a 3.6% reduction in liquefied petroleum gas usage. That outcome was solely due to the AI automation and required no other major capital investments.
Testing Semiconductors With Automated Solutions
Applying automation to quality control processes in semiconductor manufacturing can improve productivity and reduce instances of missed flaws. One fully automated metrology solution introduced in 2023 shortens time frames associated with transmission electron microscopy, making it ideal for high-volume semiconductor manufacturing.
Estimates suggest it could boost productivity by up to 20% compared to previous generations of the metrology equipment. For example, users benefit from the automated sample insertion and removal capabilities. They eliminate the possibility of operator mistakes and allow people to perform high-resolution imaging more quickly.
This product also creates automated workflows, removing the need for people to develop time-consuming testing procedures. Moreover, an advanced detection system improves people’s results when working with the most challenging and beam-sensitive materials. Tests indicate users can collect data up to two times faster when using these features.
Semiconductor manufacturing professionals should strongly consider using such specialized tools to streamline their efforts and get more reliable results. These investments pay off in the short and long term, especially if teams have previously struggled to meet quality control standards.
Applying Automation to Modern Manufacturing Environments
The above examples will give decision-makers compelling use cases for using semiconductor manufacturing automation in their facilities. However, they must carefully study individual factors that could make advanced technologies more or less likely to succeed and adopt realistic expectations about products’ capabilities. Blending automation capabilities with humans’ skills is a practical way to optimize the outcomes.
