Hardware : Sensor Systems : Metrology

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TYO: 6857

In the semiconductor industry, where technologies and business models change extremely fast, Advantest has always been on the cutting edge of progress with the world’s best product and solution development capabilities, and unmatched technical support delivered through global teamwork. Advantest offers solutions for a wide variety of semiconductors and test needs, from SoC and memory, to R&D evaluation and system level test. Among these sectors, we have established a dominant market position in the mass production test market for high-speed devices such as DRAM, computing devices, and communication processors, which demand the most reliable test technology performance. In addition, our modular test platforms, which make it easy to test a wide variety of devices, enable speedy and flexible support for customer businesses.

Assembly Line

Automated Optical Inspection

Big Payback For Combining Different Types Of Fab Data

📅 Date:

✍️ Author: Anne Meixner

🏭 Vertical: Semiconductor

🏢 Organizations: Advantest, KLA, proteanTecs

Collecting and combining diverse data types from different manufacturing processes can play a significant role in improving semiconductor yield, quality, and reliability, but making that happen requires integrating deep domain expertise from various different process steps and sifting through huge volumes of data scattered across a global supply chain.

Read more at Semiconductor Engineering

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