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Hsuan-Heng Huang

dblp:151/9262 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Electronic design automation · 100%
Databases, data mining, and information retrieval
1 paper
Machine learning and data management · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Electronic design automation
semiconductor manufacturing
0.212014
Preliminary study of an intelligent sampling decision scheme for the AVM system · ICRA 2014
Machine learning and data management
data management for machine learning
0.112014
Preliminary study of an intelligent sampling decision scheme for the AVM system · ICRA 2014

Methods — techniques the papers use, named apart from their topics

intelligent sampling decision · 0.4
YearPublicationVenuePosition
2014 Preliminary study of an intelligent sampling decision scheme for the AVM system
abstract
Wafer inspection plays a significant role in monitoring the quality of production wafers. However, it requires measuring tools and additional cycle time to do real metrology, which is costly and time-consuming. Therefore, reducing sampling rate to as low as possible is a high priority for many factories to reduce production cost. The most common way for inspecting process quality is to apply periodic sampling. If a manufacturing process is stable, then virtual metrology (VM) may be applied for monitoring the quality of wafers while real metrology is unavailable. Nevertheless, if a production variation occurs between periodic samplings, no real metrology is available during this period for updating the VM models, which may result in un-reliable VM predictions. The authors have developed the automatic virtual metrology (AVM) system for various VM applications. Therefore, this paper focuses on applying various indices of the AVM system to develop an Intelligent Sampling Decision (ISD) scheme for reducing sampling rate while VM accuracy is still sustained.
Chun-Fang Chen, Fan-Tien Cheng, Chu-Chieh Wu, Hsuan-Heng Huang
ICRA4