Job Title : Computational Scientist : Feature Scale Modeling Job Description : Applied Materials is looking for a computational scientist to help create the next generation semiconductor material processes and reactors via plasma modeling.
The feature scale modeling expert will calculate the material and geometry changes which occur at feature length scales during the many process steps in a semiconductor fabrication process.
As the member of a larger Computer Aided Engineering (CAE) team you will play a key role in the development of Applied Materials chambers and processes.
Statement of Responsibilities and Duties :
Develop reactor / feature models of chambers and processes being developed at Applied Materials
Apply developed feature models (coupled with reactor models) to predict profile evolution based on reactor settings, to understand and propose new unit processes, new process sequences, new chemistries, solve chamber design issues, propose new chamber designs, and trouble shoot current production issues.
Develop and maintain feature scale scientific software through algorithm development, implementation of new capabilities, identifying and fixing bugs.
Conduct High Performance and distributed computing.
Maintain awareness of technical literature, publish research results in peer-reviewed scientific or technical journals and present results at external conferences, seminars and / or technical meetings.
Perform other duties as assigned. Qualifications :
PhD in physics, electrical engineering, chemistry, or related field, or the equivalent combination of education and related experience.
Knowledge of semiconductor processes such as etch (ex : plasma, wet), deposition (ex : atomic layer deposition, chemical vapor deposition, physical vapor deposition), epitaxy, electroplating, thermal, doping, and other surface modification process (ex : nitridation, oxidation).
Knowledge of surface chemistry
Knowledge of low temperature plasmas and reactive flows
Scientific software development experience including modern code best practices, version control, test-driven development, algorithm development, parallelization techniques.
Coding proficiency in languages such as C / C++, Fortran77 / 90, Python, R, or similar languages
Familiar with level-set or Lattice Monte Carlo methods.
Familiarity with techniques such as molecular dynamics, particle-in-cell, and direct simulation Monte Carlo
Experience using Linux cluster for computations
Interacts effectively with a broad range of colleagues such as hardware engineers, process engineers, program managers, and computational scientists.