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Yuzo Taniguchi

dblp:53/842 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1988
—ORCID · none

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

Artificial intelligence and machine learning · 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 graphics and multimedia
1 paper
Image and video processing · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Hardware accelerators and domain-specific architectures · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Image and video processing › pattern detection › anomaly detection
defect detection
0.011988
An Automatic Wafer Inspection System Using Pipelined Image Processing Techniques · IEEE Trans. Pattern Anal. Mach. Intell. 1988
Image and video processing
industrial inspection
0.011988
An Automatic Wafer Inspection System Using Pipelined Image Processing Techniques · IEEE Trans. Pattern Anal. Mach. Intell. 1988
Hardware accelerators and domain-specific architectures › image processing accelerator
pipelined image processor
0.011988
An Automatic Wafer Inspection System Using Pipelined Image Processing Techniques · IEEE Trans. Pattern Anal. Mach. Intell. 1988

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

pipelined image processing · 0.0design pattern data comparison · 0.0
YearPublicationVenuePosition
1988 An Automatic Wafer Inspection System Using Pipelined Image Processing Techniques
abstract
An automatic wafer pattern inspection system has been developed that can detect defective patterns 6 mu m or larger in multilayered wafer patterns at a speed 30 times faster than that of a human inspector. The false-alarm rate is less than 0.5 occurrences/chip. This performance is achieved mainly by the use of a special comparison method between two adjacent patterns obtained through a single optical setup, and also by the use of digital design pattern data (CAD data). The main functions of the design pattern data are to specify the inspection area, to designate optimum parameters for inspection, and to separate defective portions into different layers, thereby facilitating the classification of the defects. All image processing is performed in one pass by a high-speed pipeline-structured image processor that can analyze an input image signal at a 7 MHz video rate.>
Haruo Yoda, Yozo Ohuchi, Yuzo Taniguchi, Masakazu Ejiri
IEEE Trans. Pattern Anal. Mach. Intell.3