Semiconductor wafer production is exceedingly complex. Every year, chips are expected to be smaller, while also producing greater amounts of power. If you’re looking to buy silicon wafers, you may be wondering: how do manufacturers plan to keep up with that demand?
The answer is multifaceted, but AI and machine learning are important pieces of the puzzle. Let’s discuss how AI and machine learning are shaping the future of wafer production, by helping manufacturers anticipate their clients’ needs, design chips, perform quality assurance, and more.
As the COVID-19 pandemic showed, semiconductor cycle times necessitate understanding how much demand there’s going to be in the market.
Even if we know that our clients are going to want more wafers, we must understand which wafers will best suit their needs. AI has proven effective here, improving supply chain decision-making by up to 50%.
When we understand what chips our clients want, we’re better able to serve them. This means that, instead of having to wait the 24 weeks that it can take to start producing a specific wafer at scale, we can already have the chips they want in our inventory.
Every year, we receive more data regarding semiconductor supply chains. This makes it easier for AI to optimize the supply chain.
Wafer development is costly and time-consuming, which makes it tricky for clients to get chips in a timely fashion.
Once the initial design of a chip has been created, there need to be different iterations. These give researchers the chance to determine where any failures occur, as well as helping them understand how to improve the yield of the semiconductor.
Just as artificial intelligence is currently used to detect defects during the manufacturing process, it can also be used to determine where the faults and opportunities are during the initial iterations.
By spotting these things more quickly, AI empowers semiconductor companies to get products to market faster and cheaper. These savings are then passed down to the client, who can either improve their profit margins or pass the savings down to their own clients.
It’s also environmentally friendly, both because of the higher yield AI enables, and because less iterations are required to arrive at the final product.
Quality Assurance is essential for ensuring that customers get high-quality semiconductors. To understand why, it’s useful to break down the three different kinds of semiconductor defects:
Only major and critical defects can be seen at first glance. Even if minor defects don’t prevent clients from using a semiconductor, they can significantly reduce performance, which in turn negatively impacts our customers’ products.
Therefore, it’s important to examine each semiconductor closely, looking for any potential defects. As semiconductors grow more complicated, the process needs to be more rigorous.
The most common QA method is to send power through a semiconductor, ensuring that it works. This requires physical contact with the semiconductor. Given the sensitivity of these wafers, the situation is not ideal.
Current wafer QA systems have begun using AI to increase their speed. We expect this development to continue as time goes on.
Despite all the promise of AI in the semiconductor industry, it has yet to be fully tapped. Only 30% of semiconductor companies currently use AI in a way that impacts their bottom line. That said, the industry is still further ahead of many others.
77% of C-Suite executives report trying to incorporate AI into their company, which is significantly higher than the 41% average of C-Suite executives who reported the same across 20 different industries.
This gap can be explained by the fact that many companies are currently working on pilot programs to determine how best to use AI in their production process.
Incorporating this technology requires a buy-in from the company. As with so many things in the semiconductor industry, it takes time. Customers therefore benefit from foresight on the part of wafer manufacturers.
Semiconductors have been able to get smaller and more powerful at a semi-consistent rate due to Moore’s Law, which states that transformer density doubles roughly every eighteen months.
While this isn’t a scientific law, Moore’s observation has proven true for many decades. That said, experts predict that this won’t be the case forever. As semiconductors get denser, they also get exceedingly complex.
It’s long been expected that we would reach a point where it made less fiscal sense to continue shrinking the size of semiconductors. While we haven’t reached that point yet, we’ll need to find different ways to make semiconductors more powerful if we want to keep up with the pace of innovation.
Essentially, Moore’s Law has allowed us to reap the advantages of evolutionary innovation (i.e., the hard work that goes into the gradual and systematic improvement of a technology).
AI and machine learning allow us to harness revolutionary innovation, which is less predictable but nonetheless able to help us improve semiconductors and therefore better serve our customers.
You’ve come to the right place. Wafer World has been manufacturing wafers for decades. Throughout that time, we’ve proudly stayed at the cutting edge of the industry, ensuring that our facilities are state-of-the-art and well-equipped to serve our customers.
Whether you have additional questions you’d like to ask us or you’re ready to request a quote, please reach out to us today. We’re always happy to help business owners see the value our wafers can add to their business.
