Lam Research
Canvas Category 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.
Assembly Line
Lam Research Introduces the Semiconductor Industry's First Collaborative Robot for Fab Maintenance Optimization
Lam Research Corp. (Nasdaq: LRCX) introduced Dextro™, the semiconductor industry’s first collaborative robot (cobot) designed to optimize critical maintenance tasks on wafer fabrication equipment. Now deployed in multiple advanced wafer fabs around the world, Dextro enables accurate, high-precision maintenance to minimize tool downtime and production variability. It drives significant first-time-right (FTR) results that can enhance yield.
Today’s wafer fabrication equipment utilizes advanced physics, robotics and chemistry to create semiconductors at nanoscale. A typical fab has hundreds of process tools that each require regular, complex maintenance. Dextro is designed to improve the cost effectiveness of this equipment by performing critical maintenance tasks repeatability with sub-micron precision.
“When manufacturing equipment requires maintenance, the work must be done quickly and efficiently to avoid extended tool downtime and wasted cost,” said Young Ju Kim, vice president and head of the Memory Etch Technology Team at Samsung Electronics. “Error-free maintenance by Dextro helps drive improvements in production variability and yield. This is an exciting milestone in Samsung’s journey to the autonomous fab.”
Lam Research wins 2024 Best of Sensors Award in AI/Machine Learning
Lam’s Equipment Intelligence® Solutions aims to enable customers to achieve faster technology transitions at lower costs and with fewer resources while also reducing waste. This approach is built on four foundational pillars: digital twin/digital thread, virtual process development, smart tools, and digital services.
The digital twin and digital thread components weave together vast amounts of data collected through a system’s life cycle, from initial concept to end-of-life. This comprehensive data integration allows for more informed decision-making and process optimization.
Virtual process development leverages computable models that combine physics with machine learning and historical data mining. This approach has already shown significant benefits, reducing the design of experiment (DoE) count and cost by over 20% in its initial implementation phase.
Improving Semiconductor Yield Using Large Area Analysis
Lam Research’s SEMulator3D® virtual fabrication platform can be used to perform 3D modeling and rules-based metrology of semiconductor devices, and to identify hotspots (DRC violations) and potential failures faster and at a lower cost than silicon wafer-based experimentation.
Large area analysis (LAA) is a powerful concept in semiconductor engineering research and development. LAA are sets of experiments that can be used to explore the sensitivity of a potential hotspot and its effect on downstream process steps across a large chip area. A well-designed LAA can help an engineer develop an optimal semiconductor process using a limited number of experimental wafer runs.
Membrion Series B round closes at $12.5M
Electro-desalination membrane manufacturer, Membrion today announced the completion of their Series B funding round, raising a round total of 12.5 million dollars. The second close of 5.5 million dollars is anchored by Samsung Venture Investment Corporation and Lam Capital. They are joined by Indico Capital Partners, Harvard Business School New York Alumni Angel Group, New York Angels, and GiantLeap Capital.
SemiverseTM Solutions Tackle the Semiconductor Industry’s Greatest Challenges
Lam Research Corp. (Nasdaq: LRCX) made multiple announcements to usher in a new era of collaborative innovation, taking a leadership role in the creation of a virtual nano fabrication environment intended to significantly speed up and reduce the cost of industry breakthroughs. The Semiverse™ Solutions products support an interconnected ecosystem of virtual tools and digital twins, allowing researchers to explore promising ideas and refine new processes at significantly lower cost, thereby reducing time consuming trial-and-error physical experimentation. The virtual domain spans the global lab and engineering footprint to increase collaboration and technical contribution across the entire talent ecosystem.
🖨️ Fabric8Labs Closes $50M Series B Financing for Electrochemical Additive Manufacturing Technology
Fabric8Labs, pioneer of electrochemical additive manufacturing, today announced the close of a $50M Series B investment round led by New Enterprise Associates (NEA), with participation from existing investors, including Intel Capital, imec.XPAND, SE Ventures, TDK Ventures, and Lam Capital. The new infusion of capital will be used to scale the company’s proprietary Electrochemical Additive Manufacturing (ECAM) technology and establish a pilot production facility.
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.”