Demo Data from VLDB 2026 / reviewers in the wild / expert
Hsuan-Heng Huang
dblp:151/9262
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Electronic design automation
semiconductor manufacturing |
0.2 | 1 | 2014 | Preliminary study of an intelligent sampling decision scheme for the AVM system · ICRA 2014 |
Machine learning and data management
data management for machine learning |
0.1 | 1 | 2014 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | Preliminary study of an intelligent sampling decision scheme for the AVM systemabstractWafer 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 |
ICRA | 4 |