PDF Solutions

Consultancy : Company : Information Technology

Website

Santa Clara, California, United States

NASDAQ: PDFS

PDF Solutions, Inc. (NASDAQ: PDFS) turns semiconductor manufacturing and test data into foresight that you can act upon. Organizations around the globe are using our products and solutions to break down data silos, unleash innovation and solve their toughest big data challenges. The mission of PDF Solutions is to empower every semiconductor and electronics company to break down the data silos within their supply chains and leverage all of their manufacturing and test data to improve every key performance indicator (KPI) that is important to their business.

Assembly Line

Making The Most Of Data Lakes

Date:

Author: Anne Meixner

Topics: MLOps

Vertical: Semiconductor

Organizations: PDF Solutions, Synopsys

Data management and data analysis necessitates understanding the data storage and data compute options to design an optimal solution. This is made more difficult by the sheer volume of data generated by the design and manufacturing of semiconductor devices. There are more sensors being added into equipment, more complex heterogeneous chip architectures, and increased demands for reliability — which in turn increase the amount of simulation, inspection, metrology, and test data being generated.

Connecting different data sources is extremely valuable. It allows feed-forward decisions on manufacturing processes (package type, skipping burn-in), and feedback in order to trace causes of excursions (yield, quality, and customer returns).

“An understanding of the semiconductor manufacturing process and relationships throughout are essential for some applications,” said Jeff David, vice president of AI solutions at PDF Solutions. “For example, how can I use wafer equipment history and tool sensor data to predict the failure propensity of a chip at final test? How does time delay between process and test steps determine what data is useful in finding a root cause of a failure mode? What failure modes are predictable with which datasets? How do preceding process steps affect the data collected at a given process step?”

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