by Emily Newton
Though semiconductor fabrication facilities have included data-generating processes for years, the ability to collect and analyze data to enhance internal processes is a relatively recent development. Artificial intelligence has been a significant driver behind the improvement, since algorithms can find patterns in vast amounts of data and allow people to discover what to change to gain competitive advantages.
Instead of merely gathering data, decision-makers can extract insights from it. Additionally, they can use that information to set continuous improvement goals that align with the fast-moving semiconductor industry’s needs. Beyond the promise of AI semiconductor manufacturing, some parties even use the technology before production begins, such as to design cutting-edge prototypes. How can leaders harness AI to analyze and optimize processes with data?
Reducing Chip Costs and Design Time Frames
Many chip manufacturing facilities are at the forefront of technological progress because they create large quantities of pioneering chips used in some of the world’s most demanding applications. Design decisions shape what eventually happens in fabrication facilities, but they typically require extensive time and money to reach. AI has dramatically addressed those aspects, helping electronics engineers get highly effective results in less time and with lower budgets.
One collaboration between two universities showed that AI allowed people to accomplish highly skilled design work in hours instead of weeks. More specifically, artificial intelligence creates complex electromagnetic designs and the associated circuits for chips offering faster speeds or other superior characteristics compared to their conventionally designed counterparts. The researchers also said the AI tool suggested intuitive and highly unusual designs. That capability could inspire humans to think outside the box as they assess how to push the boundaries with pioneering designs.
Another advantage of using AI this way is that it can synthesize new chip structures that are impossible to produce through other methods. The researchers also said that the former methods of electromagnetic simulation were finite. However, this AI application has expanded the possibilities.
The researchers recognized some downsides that require human involvement. For example, though artificial intelligence can propose more efficient designs, it could also generate non-feasible ones. Those potential outcomes mean humans should continue applying their expertise while allowing AI to tackle the costly or time-consuming aspects.
Additionally, as researchers use artificial intelligence this way, they will naturally accumulate data to determine the most practical and efficient examples instead of those that are unlikely to work. That information could save additional time by narrowing possibilities based on desirable properties or other necessities.
Improving AI Semiconductor Manufacturing With Digital Twins
People have discovered increasingly creative ways to use digital twins, such as to improve the availability of shared bikes and increase the effectiveness of allergy treatments. Some teams have also created AI-enabled digital twins to boost outcomes. Digital twins are particularly valuable when used during the construction of semiconductor facilities.
Workers at a new regional research facility hope to reduce uncertainties by using AI-powered digital twins to help users assess various parameters before committing to specific possibilities. This approach will let them map out the entire manufacturing process to identify where errors might occur. Such improved awareness allows people to proactively prevent those outcomes rather than deal with the ramifications.
Semiconductor production processes include hundreds of steps and could take half a year to finish. Ripple effects can also occur that affect every phase. Failing to catch faulty designs early enough could cost companies billions of dollars. If enough customers complain about the unsatisfactory goods, the feedback could harm a brand’s reputation and require spending years to regain the public’s trust.
This regional hub should reduce that uncertainty because the people working on it are gathering semiconductor manufacturing data to feed into the digital twin. They believe the associated resources will strengthen the United States’ position in the global chip fabrication economy, including by allowing participating companies to produce more goods domestically rather than outsourcing.
These AI-enabled digital twins are also useful when manufacturers want to expand existing facilities. Numerous advancements have occurred to help them do that faster. In one case, a client needed numerous clean room spaces meeting stringent classifications. The result was a 6,000-square-foot addition featuring modular components. AI digital twins could help managers decide whether to expand their facilities.
Embracing AI Semiconductor Manufacturing for Process Improvements
Artificial intelligence can process vast quantities of data much more efficiently than humans, which makes it ideal for identifying shortcomings and suggesting improvements within semiconductor fabs. Change can feel daunting — especially when the outcome is uncertain. Fortunately, AI can highlight patterns in years of data, showing the probable outcomes of necessary changes. That information should help managers feel more confident about enforcing process innovations and explaining their reasoning to workers.
One researcher has brought his machine learning expertise to semiconductor manufacturing, aiming to build algorithms that evaluate production processes and reveal the effects of specific changes. He believes this approach will accelerate chip creation, lower costs and reduce defects. Eventually, the algorithms could show what happens in plasma chambers during production, giving managers the information needed to optimize outcomes.
People might also apply artificial intelligence to specific aspects that have historically caused costly downtime. One possibility is to use predictive analytics algorithms to detect when critical equipment will fail. A common approach is only to address issues once people notice them. However, artificial intelligence detects abnormalities that are still too minor for technicians to spot.
Instead of performing maintenance at fixed intervals, semiconductor fabs can now use predictive algorithms to anticipate equipment issues. This proactive approach gives teams more time to act, minimizing disruptions and cutting downtime.
That is an excellent example of a practical process change that could bring immediate and long-term improvements. Similarly, executives may use artificial intelligence to determine which processes are the most error-prone and worth automating. Artificial intelligence and data-backed approaches encourage leaders to avoid guesswork, enabling better decisions.
Prioritizing AI Semiconductor Manufacturing
These promising examples show why semiconductor fabrication executives have numerous compelling reasons to bring artificial intelligence into their workflows and establish data-driven workflows. Though adopting the technology takes time and dedication, the efforts should pay off by allowing tech-centered leaders to bring their facilities into the future and position them to meet new demands.
