Job description:
GlobalFoundries is seeking a motivated, self-driven intern to develop a Machine Learning implementation of MOSFET Compact (SPICE) Model extraction.
Compact, or SPICE models are used to model electrical characteristics (I-V, C-V etc.) of MOSFETs. The BSIM family of models is one such example. In recent years Machine Learning based approaches such as Neural Networks have been used as an alternative to traditional compact model parameter extraction / optimization approach, offering more accuracy and model extraction turn around time as benefits.
GlobalFoundries are looking for an intern who is currently doing PhD in the domain of Machine Learning based semiconductor compact models. The intern should have a strong knowledge of the machine learning domain (non-linear models, Neural Networks and the like), as well as knowledge of semiconductor physics and MOSFET compact models such as BSIM.
Job duties and responsibilities:
The intern should already be working in the above domain, should be familiar with the state of the art and future directions and should be able to bring in creative, innovative and workable ideas to implement machine learning based compact models.
It is important to have prior published work in this domain, and be part of a research group headed by faculty who are recognized experts who can guide the intern to meet the objectives within the duration of the internship, which is to develop an end to end ML based compact model of a MOSFET.
Other responsibilities:
- Perform all activities in a safe and responsible manner and support all Environmental, Health, Safety & Security requirements and programs
Qualifications required:
- Education Pursuing PhD in Semiconductor Devices, Machine Learning or related field.
Preferred qualifications:
Pursuing PhD in Semiconductor Devices, Machine Learning or related field
Job/Req. ID: JR-2300986
Company: GlobalFoundries
Location: Bangalore, KA
Job Category: VLSI Engineering
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