Data Analytics for Semiconductor Industry Applications: Yield and Reliability Analysis

Application of data analytics in semiconductor industry and analysis of large industrial data contribute to improved operational efficiency and business processes. This course equips participants with knowledge about applications of data analytics in the field of yield and reliability analysis in semiconductor devices/systems.

Yield optimisation has been regarded as one of the critical goals in the semiconductor operations and IC manufacturing. Reliability issues in semiconductor devices/systems lead to failure of devices/systems in turn resulting in poor yield. Big data proves to be imperative in semiconductor industry and data processing requires specific data analytics with specific models for data visualisation and analysis.

Data analytics approach provides the ability to evaluate the impact of many variables affecting the reliability and yield in the semiconductor manufacturing process. Hands-on sessions with data analysis models/tools help participants to improve operational tasks and increase work productivity.

Who Should Attend

Engineers/Professionals from Semiconductor Industries
Graduates/Post-graduates/Poly graduates with Electrical/Electronics background
Educators teaching VLSI and semiconductor courses in Electrical/Electronics Engineering
Administrative staff and Marketing staff working in the Semiconductor Industry

Prerequisites

Basic knowledge of programming
Electronics/Semiconductor knowledge is beneficial

Programme Outline

Learning Objectives

Equip participants with knowledge of applications of data analysis in semiconductor industry applications
Learn to apply the data analysis models/tools for yield and reliability study and its optimisation
Provide hands-on sessions with the data analysis tools
Apply the skills and knowledge to develop relevant tools required for industrial data

Day 1

Introduction to data science and analytics
Relevance of data analytics in semiconductor industry applications
Yield analysis semiconductor manufacturing process
Data modelling/analysis tools for yield optimisation
Hands-on session in data visualisation and analysis

Day 2

Reliability and failure analysis of semiconductor devices and IC systems
Statistics and models related to reliability and failure analysis
Data modelling/analysis tools for reliability study
Hands-on session to analyse the impact of different variables

Assessment

Assignment
Activity session/project work
Presentation

Course Fees and Funding

Full course fee inclusive of prevailing GST	You pay S$2,180.00
SkillsFuture Course Fee subsidy (up to 70%) For Singapore Citizens < 40 years old For Permanent Residents	You pay as low as S$654.00
Mid-Career Enhanced Subsidy (up to 90%) For Singapore Citizens ≥ 40 years old	You pay as low as S$254.00
Enhanced Training Support for SMEs (up to 90%) For SME - Sponsored employees	You pay as low as S$254.00

The above module fee payable is inclusive of prevailing GST of 9%.
This course is not eligible for the $4,000 credit under SkillsFuture Credit (Mid-Career) top-up scheme.

Instructors

Shubhakar Kalya
Lecturer, SMT
SUTD

Currently working as a Lecturer at Singapore University of Technology and Design, Shubhakar obtained his Master’s degree in Microelectronics from Indian Institute of Science (IISc), Bangalore, India in 2007 and PhD degree from Nanyang Technological University (NTU), Singapore in 2012/13.

During his doctoral studies at NTU, he worked on Nanoscale characterisation of High-κ gate dielectrics for reliability and failure analysis. He has also worked as a researcher at Institute of Materials and Research Engineering (IMRE), Singapore, from July 2009 to December 2011, where he was involved in research related to characterisation of High-κ gate dielectrics using scanning tunneling microscopy and atomic force microscopy. His research interests are in nanoscale characterisation and analysis of High-κ gate dielectrics for logic and memory devices, and failure analysis of nanoscale electronic devices. He was a Visiting Scientist at Massachusetts Institute of Technology (MIT), Cambridge, USA in Electrical Engineering and Computer Science (EECS) Department during January-June 2017.

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Nagarajan Raghavan
Associate Professor, EPD, Co-Director, NEED Programme
SUTD

Nagarajan is a tenured Associate Professor at the Singapore University of Technology and Design (SUTD) in the Engineering Product Development (EPD) pillar. He is also the Co-Director of the Nano-Electronic Engineering and Design (NEED) double master’s degree programme. He has been a faculty at SUTD since 2015. Prior to that, he was a postdoctoral fellow at the Massachusetts Institute of Technology (MIT) in Cambridge and at IMEC in Belgium, in joint association with the Katholieke Universiteit Leuven (KUL). He obtained his PhD (Microelectronics, 2012) at the Division of Microelectronics, Nanyang Technological University (NTU), Singapore.

His work focuses on reliability modelling of nanoelectronic devices (especially logic and non-volatile memory device technologies), prognostics and health management for batteries and LEDs, design for reliability, physics of failure, AI-enabled optimisation of electromechanical properties of polymer nanocomposites and AI-enabled design for additive manufacturing. He is the recipient of the SUTD Outstanding Researcher Award for 2022, IEEE EDS Early Career Award for 2016, Asia-Pacific recipient for the IEEE EDS PhD Student Fellowship in 2011 and the IEEE Reliability Society Graduate Scholarship Award in 2008. To-date, he has authored / co-authored more than 270 international peer-reviewed publications with 4300+ citations and a h-index of 31 and five invited book chapters as well. He served as the General Chair for IEEE IPFA 2021 at Singapore and has consistently served on the review committee for various IEEE journals and conferences including IRPS, IIRW, IPFA and ESREF. He is an Associate Editor of the IEEE Access and Microelectronic Engineering journals and sits in the Editorial Advisory Board for APL Machine Learning. He is currently a Member of IEEE (2005-present) and was an invited member of the IEEE GOLD committee (2012-2014).

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Data Analytics: Semiconductor Industry