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KLA

The candidate will perform research to design a suite of algorithms to perform defect detection in patterned semiconductor wafers inspected by KLA’s optical and electron beam microscopy tools. Machine learning is the main line of research, while other methods in machine vision are also investigated for specialized applications.

 

The sensitivity requirements in chipmaking place the performance metrics of these algorithms well beyond the algorithms in the public domain. Thus an ideal candidate is expected to comfortably challenge existing techniques, and to engineer novel solutions and demonstrate their efficacy through data analysis. Working effectively in this group requires solid grounding in physics and scientific computing as well as good understanding of probability theory.

 

The candidate is expected to form close collaborations with hardware teams to understand the physics of image formation in microscopy tools, incorporate it into the research, and provide feedback to guide the development of future systems.

 

Depending on the candidate’s background, the role will expand into development of future technologies for KLA via ab-initio modeling of light scattering for inspecting electronic devices at various fabrication steps, and modeling the performance of optical sub-systems in KLA’s tools.

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