Course Overview

Domain
Engineering
Format
Short Course
Duration
13 days over 3 months
Fee Subsidy
Up to 90% SF Funding

Semiconductor manufacturing poses intricate challenges stemming from its complex processes, machinery, and parameters.

Manufacturing companies often struggle with the early detection of anomalies and optimising process parameters due to the high costs associated with traditional methods like Design of Experiment (DOE). Additionally, inadequate machine maintenance can lead to quality issues and financial losses, exacerbated by existing maintenance routines that may not effectively prevent breakdowns. To address these challenges, the semiconductor manufacturing sector requires Artificial Intelligence (AI) driven solutions.

This course is designed to empower industry professionals with advanced data analytics methodologies in AI and Machine Learning (ML). Through a combination of theoretical exploration, relevant case studies, and hands-on Python programming sessions, learners will develop a deep understanding of AI technologies and their application in semiconductor manufacturing.

By delving into real semiconductor data examples and utilising sample code, learners will gain practical experience in AI data analytics, enabling them to identify and address areas for improvement within their industries. Whether it's enhancing defect detection, optimising process parameters, or implementing predictive maintenance systems, this course equips learners with the tools and knowledge needed to drive meaningful change.

Who Should Attend

Professionals in the semiconductor manufacturing industry, including:

  • Engineers
  • Process, maintenance, and project managers
  • Equipment designers and builders
  • Professionals involved in the manufacturing process, quality control, and equipment maintenance 
Prerequisites

  • Learners must have basic Python programming skills and statistical knowledge

What You Will Learn

Understanding, processing, and cleaning the data

  • Performing data cleaning and manipulation
  • Understanding data using plotting tools

Compressing and selecting the data dimensions

  • Analysing the correlation and identifying the major factors
  • Compressing and reducing the data dimensions

Building and evaluating supervised learning models

  • Using the classification models method

Building and evaluating unsupervised learning models

  • Using the clustering models method

Building and evaluating neutral network models

  • Using neutral network models

Applying machine learning to the applications

  • Understanding machine learning and its application
  • Applying the methods learned to the application and evaluating the model's performance

Teaching Team

Zhou Junhong
Zhou Junhong

Associate Professor, Engineering, Singapore Institute of Technology

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Tan Rui Zhen
Tan Rui Zhen

Associate Professor, Engineering, Singapore Institute of Technology

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Wang Yu
Wang Yu

Senior Professional Officer, Singapore Institute of Technology

View profile

Schedule

Course Run Dates Time
February – May 2025 Run 

7, 14, 21 & 28 Feb

7, 14, 21 & 28 Mar

4, 11, 18, 25 Apr

2 May

 1:00 pm – 5:00 pm

 

Certificate and Assessment

A Certificate of Participation will be issued to participants who:

  • Attend at least 75% of the course
  • Undertake and pass non-credit bearing assessment during the course

Fee Structure

The full fee for this course is S$7,793.50.

Category After SF Funding
Singapore Citizen (Below 40) S$2,338.05
Singapore Citizen (40 & Above) S$908.05
Singapore PR / LTVP+ Holder S$2,338.05
Non-Singapore Citizen S$7,793.50 (No Funding)


Note: All fees above include GST. GST applies to individuals and Singapore-registered companies.

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Funding
Learn more about funding schemes available

Course Runs

February 2025
calendar-day
07 Feb 2025 - 02 May 2025
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13 days over 3 months
delivery-mode
In-person
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SIT@Dover, 10 Dover Drive S138683
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SGD $7,793.50
Up to 90% SF Funding
Apply By:
16 Jan 2025 23:59
Apply Now

Frequently Asked Questions

  1. chevron--up
    Do I need to have coding skills for this course?

    Basic Python programming skills are necessary, and you must install the Jupyter notebook software on your laptop.

Related Insights

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He may have already traded in his student card for a staff pass, but SIT alumni and staff member Seaw Ker Boon chose to head back to the classroom to become a student once again. The aspiring software architect shares why he attended the Machine Learning courses under the SITLEARN programme.

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