Machinery : Special Purpose : Semiconductor
At Lam, we believe you can’t identify an innovator through innovation alone—it’s through collaboration, precision, and delivery. As a fundamental enabler of the fourth industrial revolution and trusted partner to the world’s leading semiconductor companies, we welcome challenges and promise to deliver. How do we get there? By combining superior systems engineering, technology leadership, a strong values-based culture, and an unwavering commitment to prove our customers’ next big thing.
Geminus.AI Announces the Completion of $5.9M Seed Round Led by Lam Capital and SK Inc.
Geminus.AI, global leader in physics-informed AI, announced completion of $5.9M in seed funding led by Lam Capital, the venture capital arm of a leading semiconductor equipment manufacturer and South Korean industrial conglomerate SK, Inc. Additional investors include SkyRiver Ventures and Sentiero Ventures, who joined existing investors The Hive and Darling Ventures.
Semiconductor Manufacturing: Making Impossibly Small Features
Deposition, litho, and etch are interdependent processes tightly linked together. Technology continues to press forward to meet the incredible opportunity of 5G, cloud, and IoT, and these processes enable our customers to go further by reducing feature sizes and improving pattern fidelity to get a better feature. The litho process can produce lines that have some edge roughness, which can have a negative impact on the variability of the devices you make because it affects what are called “critical dimensions,” such as the transistor gate length. We can also take litho printed lines and improve the sidewall smoothness by typically 30% or more.
AI In Inspection, Metrology, And Test
“The human eye can see things that no amount of machine learning can,” said Subodh Kulkarni, CEO of CyberOptics. “That’s where some of the sophistication is starting to happen now. Our current systems use a primitive kind of AI technology. Once you look at the image, you can see a problem. And our AI machine doesn’t see that. But then you go to the deep learning kind of algorithms, where you have very serious Ph.D.-level people programming one algorithm for a week, and they can detect all those things. But it takes them a week to program those things, which today is not practical.”
That’s beginning to change. “We’re seeing faster deep-learning algorithms that can be more easily programmed,” Kulkarni said. “But the defects also are getting harder to catch by a machine, so there is still a gap. The biggest bang for the buck is not going to come from improving cameras or projectors or any of the equipment that we use to generate optical images. It’s going to be interpreting optical images.”