Master Thesis Topic: Enhancement of existing ML pipeline via DL approaches for the detection of patterns in wafermaps

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Master Thesis Topic: Enhancement of existing ML pipeline via DL approaches for the detection of patterns in wafermaps

At a glance

At KAI Kompetenzzentrum Automobil- und Industrieelektronik GmbH, you will perform your thesis project in an industrial research environment, guided and supported by experienced researchers. We work in close cooperation with universities and research facilities supporting your academic education, whereas our industrial partner Infineon offers interesting opportunities for a prospective career path in the semiconductor industry. KAI GmbH in cooperation with Infineon Technologies Austria AG is involved in the EU project Arrowhead Tools for the efficient implementation and integration of decision support systems. Within the framework of this project we offer a masther thesis for motivated students (f/m/div*).

Quick info

Location

Villach

Entry level

0-1 year

Job ID

315716

Start

Jul 01, 2021

Type

Full time

Contract

Temporary

Job description

As essential components of safety-relevant equipment in vehicles, machines, etc., semiconductors are subject to very high quality requirements. At the end of the semiconductor frontend production, measurements (electrical wafer test data) for each chip are performed, which are visualized as so-called wafermaps. These wafermaps contain patterns which need to be detected. Such a pattern recognition technique (i.e. an ML pipeline) has already been developed, but due to the ever-increasing number of existing pattern types for hundreds of different products, classical approaches reach their limits. Therefore, DL techniques like CNNs in combination with suitable data augmentation techniques will be evaluated regarding their applicability.

In this master thesis you will:

  • Understanding of the problem and related data sources through the exchange with experts
  • Literature study on suitable d ata augmentation techniques
  • Neural network architectures (characteristics, applications, possibilities, ...)
  • Investigation and implementation of most promising methods
  • Integration of the methods into existing analysis pipeline
  • Documentation of the results and preparation of a master thesis

Further information
Type of employment: Temporary / Full-time
Start: July 2021
Duration: min. 8 months

This thesis has to be written in cooperation with an university.

Profile

You are a motivated student (f/m/div)* in the field of Mathematics, Statistics, Data Science, Computer Vision or similar. You are best equipped if you:

  • Good knowledge in statistics, mathematics
  • Experience in the field of machine learning and deep learning
  • Experience in data engineering is advantageous
  • Programming skills in e.g. R, Python
  • German and/or English skills

This position is subject to the collective agreement for workers and employees in the electrical and electronics industry. Master students receive a compensation of 2.242,19 Euro gross p.m. (full-time basis).

Please attach the following documents (german or english) to your application:

  • Motivation letter
  • CV
  • Certificate of matriculation at a university
  • Transcript of records
  • Highest completed educational certificate (Matura certificate for Bachelor students, Bachelor certificate for Master students)
  • Reference letter

About Us

Part of your life. Part of tomorrow.

We make life easier, safer and greener - with technology that achieves more, consumes less and is accessible to everyone. Microelectronics from Infineon is the key to a better future. Efficient use of energy, environmentally-friendly mobility and security in a connected world - we solve some of the most critical challenges that our society faces while taking a conscientious approach to the use of natural resources.

  • The term gender in the sense of the General Equal Treatment Act (GETA) or other national legislation refers to the biological assignment to a gender group. At Infineon we are proud to embrace (gender) diversity, including female, male and diverse.

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Julia Gabriel

Student Attraction Manager

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