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AI-Driven Practical Implementation Strategies for Manufacturing Industry (Electronics/Semiconductor Sector)

The electronics and semiconductor industry is a field where data-driven management and innovation are particularly crucial due to ultra-precision processes and complex supply chains. This course covers practical strategies that can be directly applied in electronics and semiconductor manufacturing, including defect detection, process optimization, predictive maintenance, and supply chain management using AI technology. Along with real-world cases from global companies, the course also presents low-cost, high-efficiency AI implementation methods that small and medium-sized enterprises can realistically utilize. Through this, students will be able to understand and apply AI-based manufacturing strategies that not only improve productivity and reduce costs, but also build future competitiveness.

6 learners are taking this course

  • 88888
AI 활용법
스마트팩토리
빅데이터
업무혁신
딥러닝머신러닝
AI
Big Data
Generative AI
Machine Learning(ML)
Deep Learning(DL)

What you will learn!

  • Trends in AI Utilization in the Semiconductor and Electronics Industry

  • Data-driven Process Management Techniques

  • AI-Powered Quality Inspection and Defect Detection

  • Equipment Predictive Maintenance and Production Stabilization

  • Understanding Smart Factory and Global Cases

AI-Driven Innovation in Semiconductor Manufacturing

What you can learn through this course

  • The Role of AI in Electronics and Semiconductor Manufacturing Processes – Understand how AI enhances efficiency and reduces defects in complex production stages (wafer processing, assembly, inspection, etc.).

  • Data-driven Process Optimization Techniques – Learn how to utilize sensor data and inspection data, and methods for process control and productivity improvement through these approaches.

  • AI Quality Inspection and Defect Detection – Learn computer vision-based micro defect detection and defect rate reduction case studies.

  • Equipment Predictive Maintenance – Learn strategies to predict failures and minimize production downtime through analysis of equipment vibration and temperature data.

  • Smart Factory Construction Strategy – Understand AI implementation processes and practical application considerations through global enterprise case studies.

Mainly used fields

  • Semiconductor Manufacturing: Wafer processing, chip inspection, defect detection, production scheduling optimization

  • Electronic Component Manufacturing: PCB inspection, assembly process quality control, surface defect detection

  • Smart Factory Operations: Production automation, supply chain management, equipment predictive maintenance

  • Global Case Companies: Not only semiconductor companies such as Samsung Electronics, TSMC, and Intel, but also electronics manufacturers in general

You'll learn this kind of content

A course specialized in electronics and semiconductor fields utilizing AI

Anyone can easily understand it through a systematic structure that enables the application of AI technology to the electronics and semiconductor fields.

The Complexity of Semiconductor Processes

Proactive Response to Unpredictable Supply Chain Risks

AI technology can be utilized to systematically manage unpredictable supply chain risks, enabling proactive responses in advance.

Supply Chain Risk Elimination

Pre-enrollment Reference Information

Practice Environment

  • Operating System and Version (OS): OS types and versions such as Windows, macOS, Linux, Ubuntu, Android, iOS, etc.

  • Tools Used: Software/hardware versions and billing plans required for practice, whether virtual machines are used, etc.

  • PC specifications: CPU, memory, disk, graphics card, and other recommended specifications for running the program

Learning Materials

  • Types of learning materials provided (PPT, cloud links, text, source code, assets, programs, example problems, etc.)

  • Volume and capacity, characteristics and precautions regarding other learning materials, etc.

Prerequisites and Important Notes

Required Prerequisites

  1. Understanding Basic Manufacturing

    • Having basic knowledge of semiconductor and electronics industry production processes (wafer manufacturing, assembly, inspection, etc.) will make learning much easier.

  2. Data·AI Basic Concepts

    • You need a basic understanding of what machine learning and deep learning are, and the fundamental flow of data analysis (collection → preprocessing → modeling → application).

  3. Basic Knowledge of Quality Control and Production Management

    • If you are familiar with concepts such as process management, quality variation, and defect rates, you can easily connect the lecture content with real-world situations.

Recommended for
these people

Who is this course right for?

  • Electronics and semiconductor manufacturing workers

  • Smart Factory & DX Manager

  • Learners interested in AI and data utilization

Need to know before starting?

  • Understanding Basic Manufacturing

  • Data & AI Fundamental Concepts

  • Quality Control and Production Management Fundamentals

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Curriculum

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18 lectures ∙ (3hr 44min)

Course Materials:

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