Machine Learning based computational lithography

Leuven - Master projects/internships
|
Meer dan twee weken geleden

Machine learning for computational lithography

The volume of data to be processd in the world increases exponentially every year at every corner of the life. 'Big Data' often refers to the data that are so large that traditional data processing schemes are inadequate, which the current semiconductor industry faces. In order to handle the data properly, machine learning and deep learning are heavily explored in the industry. In semiconductor industry, chip scaling continues by pitch and design caling to move to next technology node. Computational technology comprises physical design, design verification, optical proximity correction (OPC), design for manufacturability (DFM), data preparation and mask preparation. The data appearing in those area dramatically increases per technology node, and becomes extremly intensive to carry out with current computataionl power (e.g 1000 cores takes 3 week for single layer OPC). Even more, introduction of new design concept and models (mask, optical, resist, etch, topography-aware and CMP) even more complicates the computation process.The student will develop a new algorithm and technique in machine learning, and will use  the machine learning in defining new technology and tools in semiconductor technology. Knowledge in programming is a prerequisite. By joinging the internship program, the student will learn physical design, verification, OPC and DFM, and work together with research engineers to define cutting edge technology. The goal of this internship is  1) development of new algorithm, 2) reduction in number of computational iteration, and 3) optimization/minimization of DOE  to shorten turn-around-time (TAT). Machine learning and optimization may include study of  a. Pattern recognition, b. Pattern extraction, and c. Classification. Optimization incorporates mathematical and statistical concepts (effect, global/local min. search algorithm), or a new concept.              ​



Type of project: Internship, Thesis, Combination of internship and thesis

Duration: minimum 6 months

Required degree: Master of Engineering Technology, Master of Science, Master of Engineering Science

Required background: Computer Science, Nanoscience & Nanotechnology, Physics

Supervising scientist(s): For further information or for application, please contact: Ryan Ryoung han Kim (Ryan.Ryoung.han.Kim@imec.be)

Imec allowance will be provided

Share this on

truetrue

Deze website maakt gebruik van cookies met als enige doel het analyseren van surfgedrag, zonder enige commerciële insteek. Lees er hier meer over. Lees ook ons privacy statement. Sommige inhoud (video's, iframes, formulieren,...) op deze website zal pas zichtbaar zijn na het accepteren van de cookies.

Accepteer cookies