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盧靖文 Jing Wen Lu
From Tainan, I’m majoring in algorithm and automation, looking for a job about artificial intelligence, machine learning and algorithm development.
Tainan City , Taiwan
Email : [email protected] /[email protected]
Tel : +886 976208281
Education
Master, Institute of Manufacturing Information and Systems, Expected 2019.09 National Cheng Kung University (NCKU), Tainan
Major course
1. Intelligent Manufacturing System
2. Practical Applications of Artificial Intelligence
3. C# Programming
4. Theory and Practice of Advanced Automation
5. Database Management Systems and Practice
6. Reliability Management
Bachelor, Department of Logistics Management, 2013 - 2017
National Kaohsiung University of Science and Technology (NKUST), Kaohsiung
Major course
1. Statistical Methods
2. RFID System Design
3. Industrial Automation
4. Application of Software in Transportation Technology
Programming Skills
Python
Machine Learning Development
Deep Learning (Keras)
Image processing (OpenCV)
Data Pre-processing
Matlab
Classification and Clustering
Algorithm Development
Data Visualization
R Language
Statistical Analysis
Data Visualization
Data Pre-processing
Course Project 1：
Predictive Maintenance - Remaining Useful Life Prediction
Using the grey theory in control theory to predict the trend of the process parameter of single machine parts.
Set the Sick Spec and Dead Spec before prediction.
Start the prediction when the process parameter value reaches the Sick Spec.
And then Push back the Remaining Useful Life after predict process parameter value reaches to the Dead Spec.
Course Project 2：
Conduct the image recognition and object detection performed using the data set of the wafer packaging test socket to determine the defective hole of the part.
Industry-university cooperation Project 1：
Under the condition that the product has not been or can not be actually measured, the quality of the product is estimated (Y) by using the parameters of the process (X). The online and immediate prediction is made, so we able to achieve the full inspection effect of all products.
Industry-university cooperation Project 2：
IYM (Intelligent Yield Management) Implement for Semiconductor Industry
When a large number of Defects appear in the product, the machine path information is used to analyze and identify the key machines that affect the yield, and repair and improve the key machines to reduce the cost loss.
It can be analyzed in the absence of industry Domain Knowledge.
Dissertation :
Applying AVM for Wafer Sawing Processes
Abstract：
After the wafer Sawing process is completed, a full inspection of the entire batch of Top side Chipping and Back Side Chipping will be carried out to ensure the quality of the shipment. Since the whole batch is completed after the whole batch is completed, if the quality problem occurs in the Sawing process, it is still necessary to wait until the post-process is discovered, which will cause production loss.
Through the Implement of AVM, each piece of Wafer quality can be provided, and it is not necessary to wait until the entire batch is completed to the inspection site to obtain quality results. AVM provides instant production quality in the wafer dicing process, allowing immediate reflection when problems occur, reducing production loss.
This study divides AVM into two phases：
Stage I Establishing the Classifier：
Establish a classifier through historical data to pre-determine whether there is Chipping in the wafer.
Stage II AVM Prediction：
If Chipping occurs, the Chipping value is predicted by AVM, and the abnormal parameters affecting the production are judged by "Individual Similarity Index" analysis for the engineer to check.
盧靖文 Jing Wen Lu
From Tainan, I’m majoring in algorithm and automation, looking for a job about artificial intelligence, machine learning and algorithm development.
Tainan City , Taiwan
Email : [email protected] /[email protected]
Tel : +886 976208281
Education
Master, Institute of Manufacturing Information and Systems, Expected 2019.09 National Cheng Kung University (NCKU), Tainan
Major course
1. Intelligent Manufacturing System
2. Practical Applications of Artificial Intelligence
3. C# Programming
4. Theory and Practice of Advanced Automation
5. Database Management Systems and Practice
6. Reliability Management
Bachelor, Department of Logistics Management, 2013 - 2017
National Kaohsiung University of Science and Technology (NKUST), Kaohsiung
Major course
1. Statistical Methods
2. RFID System Design
3. Industrial Automation
4. Application of Software in Transportation Technology
Programming Skills
Python
Machine Learning Development
Deep Learning (Keras)
Image processing (OpenCV)
Data Pre-processing
Matlab
Classification and Clustering
Algorithm Development
Data Visualization
R Language
Statistical Analysis
Data Visualization
Data Pre-processing
Course Project 1：
Predictive Maintenance - Remaining Useful Life Prediction
Using the grey theory in control theory to predict the trend of the process parameter of single machine parts.
Set the Sick Spec and Dead Spec before prediction.
Start the prediction when the process parameter value reaches the Sick Spec.
And then Push back the Remaining Useful Life after predict process parameter value reaches to the Dead Spec.
Course Project 2：
Conduct the image recognition and object detection performed using the data set of the wafer packaging test socket to determine the defective hole of the part.
Industry-university cooperation Project 1：
Under the condition that the product has not been or can not be actually measured, the quality of the product is estimated (Y) by using the parameters of the process (X). The online and immediate prediction is made, so we able to achieve the full inspection effect of all products.
Industry-university cooperation Project 2：
IYM (Intelligent Yield Management) Implement for Semiconductor Industry
When a large number of Defects appear in the product, the machine path information is used to analyze and identify the key machines that affect the yield, and repair and improve the key machines to reduce the cost loss.
It can be analyzed in the absence of industry Domain Knowledge.
Dissertation :
Applying AVM for Wafer Sawing Processes
Abstract：
After the wafer Sawing process is completed, a full inspection of the entire batch of Top side Chipping and Back Side Chipping will be carried out to ensure the quality of the shipment. Since the whole batch is completed after the whole batch is completed, if the quality problem occurs in the Sawing process, it is still necessary to wait until the post-process is discovered, which will cause production loss.
Through the Implement of AVM, each piece of Wafer quality can be provided, and it is not necessary to wait until the entire batch is completed to the inspection site to obtain quality results. AVM provides instant production quality in the wafer dicing process, allowing immediate reflection when problems occur, reducing production loss.
This study divides AVM into two phases：
Stage I Establishing the Classifier：
Establish a classifier through historical data to pre-determine whether there is Chipping in the wafer.
Stage II AVM Prediction：
If Chipping occurs, the Chipping value is predicted by AVM, and the abnormal parameters affecting the production are judged by "Individual Similarity Index" analysis for the engineer to check.