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Fan-Tien Cheng

23 accepted papers

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
2024

Near-Optimal Scheduling for IC Packaging Operations Considering Processing-Time Variations and Factory Practices

RA-L 2024

Due to the short life cycles of electronic products, trial run lots of new products are crucial in IC packaging for production verification and engineering adjustments. The processing time of trial run lots may differ significantly from production lots due to engineering adjustments and is difficult

Cited by 3SourceScholar
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
2021

MPI-Based System 2 for Determining LPBF Process Control Thresholds and Parameters

RA-L 2021

Determining thresholds of the primary control loops (System 1) of an additive manufacturing (AM) process is challenging when realizing System 1 with its fast and intuitive capability for adapting to different metal powers, machine configurations, and process parameters. Based on the convolution neur

Cited by 6SourceScholar
2020

An Automated Dynamic-Balancing-Inspection Scheme for Wheel Machining

RA-L 2020

Wheel balance plays an important role in vehicle safety. The existing inspection method for wheel balance mainly relies on the off-machine measurement technique, which is time- and manpower-consuming as the worldwide requirement of the automated production system gradually increases. However, the mu

Cited by 6SourceScholar
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

A Novel Efficient Big Data Processing Scheme for Feature Extraction in Electrical Discharge Machining

RA-L 2019

Electrical discharge machining (EDM) can machine hard conductive workpieces that are difficult to machine using traditional machining techniques. For monitoring the EDM process using virtual metrology (VM), probes with a very high sampling rate are needed to acquire the voltage and current signals o

Cited by 11SourceScholar
2019

An Intelligent Metrology Architecture With AVM for Metal Additive Manufacturing

RA-L 2019

The capability of measuring melt pool variation is the key evaluating metal additive manufacturing quality. To measure the variation, a metrology architecture with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> melt pool measurement and

Cited by 18SourceScholar
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
2018

A Novel Automated Construction Scheme for Efficiently Developing Cloud Manufacturing Services

RA-L 2018

Cloud manufacturing (CMfg) has emerged as a next-generation manufacturing paradigm that has potential to revolutionize the manufacturing industry. In further promotion of CMfg, how to build CMfg services in an automatic and efficient manner is an essential and challenging subject. Currently, there i

Cited by 10SourceScholar
2018

Automatic Virtual Metrology and Deformation Fusion Scheme for Engine-Case Manufacturing

RA-L 2018

Industry 4.0 paves the way to smart factory through integrating Internet of Things, cyber-physical systems, cloud manufacturing, and big data analytics for various industrial fields. Especially, the machinery industry significantly benefits from these automation techniques in machine-tool quality in

Cited by 20SourceScholar
2017

A Scheme of High-Dimensional Key-Variable Search Algorithms for Yield Improvement

RA-L 2017

Product yield directly affects production cost. Thus manufacturers seek to quickly enhance product yield during the development and mass-production processes. In other words, when a yield loss occurs, the root causes should be found rapidly in both the development and mass-production phases. When a

Cited by 17SourceScholar
2017

Automatic Virtual Metrology and Target Value Adjustment for Mass Customization

RA-L 2017

One of the core values of Industry 4.0 targets to integrate people's demand into manufacturing for enhanced products, systems, and services for a wider variety of increasingly personalized customization of products. Thus, Industry 4.0 advances the traditional manufacturing techniques from mass produ

Cited by 16SourceScholar
2017

Blind-Stage Search Algorithm for the Key-Variable Search Scheme

RA-L 2017

Yield enhancement is crucial for the companies' profitability performance, especially during the development and mass production phases. So, the root causes of yield loss should be quickly identified in these stages for saving the production cost. To identify the root causes, traditional yield enhan

Cited by 3SourceScholar
2017

Development of Advanced Manufacturing Cloud of Things (AMCoT) - A Smart Manufacturing Platform

RA-L 2017

As semiconductor manufacturing processes are becoming more and more sophisticated, how to maintain their feasible production yield becomes an important issue. Also, how to build a smart manufacturing platform that can facilitate realizing smart factories is essential and desirable for current manufa

Cited by 75SourceScholar
2016

Industry 4.1 for Wheel Machining Automation

RA-L 2016

Industry 4.0 is set to be one of the new manufacturing objectives. The technologies involved to achieve Industry 4.0 are Internet of Things (IoT), cyber physical systems (CPS), and cloud manufacturing (CM). However, the current objectives defined by Industry 4.0 do not include zero defects; it only

Cited by 55SourceScholar
2015

Dynamic ISD scheme for the AVM system - a preliminary study

ICRA 2015poster

Reducing the sampling rate to as low as possible is a high priority for many factories to reduce production cost. Automatic-Virtual-Metrology (AVM) based Original Intelligent Sampling Decision (Original ISD) scheme had been previously developed for reducing the sampling rate and sustaining the VM ac…

Cited by 3SourceScholar