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KLA

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Enabling the movement toward advanced chip design, KLA’s Measurement, Analytics and Control group (MACH) is looking for the best and brightest research scientists, software engineers, application development engineers and senior product technology process engineers to join our team.

The MACH team’s mission is to collaborate with our customers to innovate technologies and solutions that detect and control highly complex process variations—at their source—rather than compensate for them at later stages of the manufacturing process.

With over 40 years of semiconductor process control experience, chipmakers around the globe rely on KLA to ensure that their fabs ramp next-generation devices to volume production quickly and cost-effectively. Our MACH team develops leading-edge solutions for patterning process analytics and control technologies, thereby providing customers with critical insight at the feature level, field level and cross-wafer analysis. Our teams also develop advanced modeling simulation, data analytics and process control modeling technologies.

As a member of the MACH team, you’ll be joining the most sophisticated and successful process-control company in the semiconductor industry–working across functions to solve the most complex technical problems in the digital age.

Responsibilities

In this position, you will have several responsibilities.  First, work on a team to develop new models for lithography and etch patterning processing and to develop numerical algorithms to implement these models in commercial software.  Second, work with customers to develop and to support new applications for computational patterning, working both on mask layout and fab use-cases.  Potential applications include rigorous OPC (optical proximity correction), weak point analysis, and control and optimization of new patterning schemes.  Third, work with advanced algorithms to greatly accelerate existing models on parallel compute and GPU platforms.  Finally, work with advanced data analysis to determine model parameters by fitting to experimental datasets, which will include image-based metrology such as SEM images.

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