Assembly Line

Metrology Primer

📅 Date:

✍️ Author: Doug Mule

🔖 Topics: Metrology

🏭 Vertical: Semiconductor

The cost of defect failures is starting to spiral out of control, and the cheapest insurance against this is more Metrology and Inspection. One of the changes the industry is adopting is advanced packaging as the primary driver to increasing semiconductor performance. The push to advanced packaging has an entire set of consequences, namely newer packaging technology and a new vector of failure.

Additionally, Metrology and Inspection spending tends to ramp before the rest of the tools, and that is why they should continue to grow so robustly in 2022 given that large fabs are just starting to come online. Metrology and inspection ramps are likely happening currently for the N3 and N5 nodes at TSMC and Intel.

Read more at Fabricated Knowledge

Fingerprinting liquids for composites

📅 Date:

🔖 Topics: Metrology, Machine Learning

🏢 Organizations: Collo, Kiilto

Collo uses electromagnetic sensors and edge analytics to optimize resin degassing, mixing, infusion, polymerization and cure as well as monitoring drift from benchmarked process parameters and enabling in-situ process control.

“So, the solution we are offering is real-time, inline measurement directly from the process,” says Järveläinen. “Our system then converts that data into physical quantities that are understandable and actionable, like rheological viscosity, and it helps to ensure high-quality liquid processes and products. It also allows optimizing the processes. For example, you can shorten mixing time because you can clearly see when mixing is complete. So, you can improve productivity, save energy and reduce scrap versus less optimized processing.”

Read more at Composites World

Additive Manufacturing: New Frontiers for Production and Validation

📅 Date:

✍️ Author: Peter de Groot

🔖 Topics: Additive Manufacturing, Metrology

🏢 Organizations: Zygo

Additive manufacturing (AM) is a uniquely disruptive technology; 25-30 years ago, it changed the manufacturing paradigm by altering the way that manufacturers produced prototypes. Today, it is disrupting the way that manufacturers produce end-use parts and components, and is increasingly seen as a truly viable production technique. Now, the conversation among manufacturers is around the most judicious use of AM for production, its advantages, the sweet spot is in terms of production volumes, key opportunities, and barriers to entry. Many of these barriers relate to precision quality control of AM parts, which challenge traditional methods of surface metrology.

Read more at Industrial Distribution

Optimized quality control data keep the automotive supply chain flowing

📅 Date:

🔖 Topics: metrology, quality assurance

🏭 Vertical: Automotive

🏢 Organizations: FARO Technologies, Taylor Metal Products

“What the FARO ScanArm allowed me to do was protect my company by proving to the customer that the issue started with their engineering print. With this particular issue, I provided a full layout to the customer with all of the profile call outs from the engineering drawing that showed where the issues were.”

Without FARO solutions and the more accurate data they provided, Taylor Metal Products might have been held financially responsible for these “no build conditions.” Thanks to the fact that the ScanArm was being used, however, Jason was able to “quickly address and correct these severe issues.”

“CAD is your perfect master; it can’t be refuted,” Jason explained. “The great thing about the FARO scans is that I can use color maps. One of the overseas manufacturers is really big about pulling those color maps because with the nature of our product, you’re taking a piece of metal and you’re bending it in different directions. The natural tendency of steel is to conform back to its original state. So, the stamping world is not like the machining world where you’re dealing with really tight tolerances, cutting and threading a hole, or boring out a hole. In the stamping world, you’re pushing metal. So that’s where the scans really come into play. The color maps show any deviation from CAD throughout the entire part. You can scan a profile with a fixed CMM, but it is a linear format, not 3D — and the CMM has to be programed to do this. With the FARO ScanArm after the CAD is locked in, it’s just one click to produce the color map. And the Japanese automotive manufacturers are big on using this technology.”

Read more at FARO Resource Library

Aiming for the Top in Industrial AI, SK’s First AI Company Gauss Labs

📅 Date:

🔖 Topics: metrology

🏭 Vertical: Semiconductor

🏢 Organizations: Gauss Labs, SK hynix

Gauss Labs has been developing AI solutions aimed at maximizing production efficiency using the massive amount of data generated at SK hynix’s production sites. SK hynix wishes to make the overall semiconductor production process more intelligent and optimized across all procedures including process management, yield prediction, equipment repair and maintenance, materials measurement, and defect testing and prevention.

Read more at Sk hynix Newsroom

AI In Inspection, Metrology, And Test

📅 Date:

✍️ Authors: Susan Rambo, Ed Sperling

🔖 Topics: AI, machine learning, quality assurance, metrology, nondestructive test

🏭 Vertical: Semiconductor

🏢 Organizations: CyberOptics, Lam Research, Hitachi, FormFactor, NuFlare, Advantest, PDF Solutions, eBeam Initiative, KLA, proteanTecs, Fraunhofer IIS

“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.”

Read more at Semiconductor Engineering