KLA Careers

Research Scientist ( Imaging | Algorithms | Data Analysis )

Company Overview

Calling the adventurers ready to join a company that's pushing the limits of nanotechnology to keep the digital revolution rolling. At KLA, we're making technology advancements that are bigger—and tinier—than the world has ever seen.

Who are we?  We research, develop, and manufacture the world's most advanced inspection and measurement equipment for the semiconductor and nanoelectronics industries. We enable the digital age by pushing the boundaries of technology, creating tools capable of finding defects smaller than a wavelength of visible light. We create smarter processes so that technology leaders can manufacture high-performance chips—the kind in that phone in your pocket, the tablet on your desk and nearly every electronic device you own—faster and better. We're passionate about creating solutions that drive progress and help people do what wouldn't be possible without us.  The future is calling. Will you answer?


The AI & Modeling Center of Excellence, centered in KLA’s R&D facility in Ann Arbor, MI, was setup with the mission of advancing KLA’s traditional strengths in physics and data and providing implementation solutions for multiple KLA Inspection and Metrology products targeted at the semiconductor manufacturing industry.
As a part of this group, you will be part of a world class team of physicists, HPC system designers, machine learning and application engineers who build cutting edge solutions for modeling complex imaging techniques and semiconductor processes.  You will also work with a data scientists and AI infrastructure engineers whose mission is to build and scale machine learning based solutions for our semiconductor customers.
We are looking for engineers in a few different fields.  If you are passionate about Physics Modeling, High Performance Computing - HPC (including GPU), ML, Data, or Cloud technologies – this is the place for you!


We have an opening for a research scientist to work on process control for edge placement error (EPE).  Edge Placement Error analysis is an expansion of CD uniformity (CDU) and overlay control to account for the more complex interactions in leading-edge multi-patterning and process integration schemes. This research position will have three focus areas.  First, the research scientist will work on a team to develop statistics-based analysis techniques for data streams from KLA's metrology tools, such as the Archer (overlay), SpectraShape (CD), and PWG (wafer shape) tools.  This analysis will require standard techniques such as noise filtering and linear regression, as well as more advanced methods such as machine learning, dimensionally reduction, and big data analytics.  Second, physics-based models will be developed that are related to the physical processes used in semiconductor manufacturing, in order to combine measurements in a meaningful way.  For example, analyzing how wafer bending modes due to film stress can induce overlay errors.  Finally, these models will be combined with feed-forward and feed-back control methods, and the researcher will analyze these process control loops for stability and effectiveness in reducing manufacturing errors.


Candidate should be familiar with statistical analysis, algorithm development, and semiconductor manufacturing, and should be comfortable with scripting, Matlab or Python, and statistics packages such as JMP. Familiarity with at least one of the programming languaes such as C/C++ or C# will be a plus. Should be an independent self-starter, work well on teams, and have good communication skills.
Doctorate (Academic)ORMaster's Level Degree with at least 2 years of experience.ORBachelor's Level Degree with at least 3 years of experience.

Minimum Qualifications

Doctorate (Academic)ORMaster's Level Degree with at least 2 years of experience.ORBachelor's Level Degree with at least 3 years of experience.

Equal Employment Opportunity

KLA is an Equal Opportunity Employer. Applicants will be considered for employment without regard to age, race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other characteristics protected by applicable law.

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