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Yu-Ming Hsieh

10 accepted papers

2025

Development of an Alarm Pattern Detection Scheme for Managing Alarm Floods in Bumping Process

RA-L 2025

The pursuit of high yield in semiconductor packaging manufacturing is hindered by the increasing complexity of monitoring and alarm systems, leading to alarm floods which not only interfere with operations but also mask critical issues affecting yield. Therefore, this letter proposes an Alarm Patter

Cited by 3SourceScholar
2024

Developing the Keep-Important-Samples Scheme for Training the Advanced CNN-Based Automatic Virtual Metrology Models

RA-L 2024

Virtual Metrology (VM) technology can convert offline sampling inspection into online and real-time total inspection. As the processes of high-tech industries (semiconductor or TFT-LCD) are getting more sophisticated, higher VM prediction accuracy is demanded. With regard to this requirement, the ad

Cited by 5SourceScholar
2023

A Distributed Framework for Knowledge-Driven Root-Cause Analysis on Evolving Alarm Data-An Industrial Case Study

RA-L 2023

Root-cause Analysis (RCA) of alarms is a well-established research area in automated Production Systems (aPS). Many RCA algorithms have been proposed and successfully evaluated and new ones are being developed. Recently, researchers focus on the incorporation of formalized information about the tech

Cited by 7SourceScholar
2023

An Intelligent Factory Automation System With Multivariate Time Series Algorithm for Chip Probing Process

RA-L 2023

Chip-probing is the key process for IC manufacturing to its ensure quality. As the number of tests increases, the test quality and the test yield will be affected because the needles on the probe card of the tester will be contaminated by external objects or worn out. Whether a needle polish of the

Cited by 1SourceScholar
2022

Convolutional Autoencoder and Transfer Learning for Automatic Virtual Metrology

RA-L 2022

To ensure stable processing and high-yield production, high-tech factories (e.g., semiconductor, TFT-LCD) demand product quality total inspection. Generally speaking, sampling inspection only measures a few samples and comes with metrology delay, thus it usually cannot achieve the goal of real-time

Cited by 7SourceScholar
2021

Convolutional Neural Networks for Automatic Virtual Metrology

RA-L 2021

To ensure stable manufacturing and high yield of production, factories (e.g., semiconductor or TFT-LCD fabs) conduct quality inspection on workpieces. They tend to adopt sampling inspection in consideration of reducing cost and cycle time, yet that fails to achieve real-time and online total inspect

Cited by 13SourceScholar
2020

Automated Classification Scheme plus AVM for Wafer Sawing Processes

RA-L 2020

For the current wafer sawing process, the wafers in the same lot are inspected at the end of the entire process. Therefore, a defect, such as chipping, occurs during processing will only be detected until the end of the process, which is too late and may cause massive defects. If Automatic Virtual M

Cited by 7SourceScholar
2019

Automatic Virtual Metrology for Carbon Fiber Manufacturing

RA-L 2019

Carbon fiber is currently one of the most popular composite materials in the world, with its wide applications ranging from bikes to space shuttles. However, there is no comprehensive method for the total quality inspection of carbon fiber products so far due to its feature of continuous production

Cited by 20SourceScholar
2019

Time Series Prediction Algorithm for Intelligent Predictive Maintenance

RA-L 2019

Predictive maintenance aims to find out when the target device (TD) is in the sick state and almost entering the dead state before its actual occurrence to conduct just-in-time maintenance, so as to avoid unexpected TD down time. In this way, not only tool availability and manufacturing quality are

Cited by 53SourceScholar