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http://nopr.niscpr.res.in/handle/123456789/3786Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Chen, Shih-Hung | - |
| dc.contributor.author | Liao, Te-Tan | - |
| dc.date.accessioned | 2009-04-13T04:01:38Z | - |
| dc.date.available | 2009-04-13T04:01:38Z | - |
| dc.date.issued | 2009-05 | - |
| dc.identifier.issn | 0022-4456 | - |
| dc.identifier.uri | http://hdl.handle.net/123456789/3786 | - |
| dc.description | 361-366 | en_US |
| dc.description.abstract | This study presents a new automated system for inspecting markings on surface mounted devices (SMDs) prior to packaging. In proposed design, marking region is identified using a normalized cross-correlation template-matching scheme. A multiresolution pyramid image processing approach is used to enhance speed of search process. Target image is filtered using a hybrid digital logic filter (DLC) / mean and standard deviation gray scale (MSDGS) algorithm for noise filtering. Individual characters in marking are segmented and fed to a neural network for automatic recognition. DLC / MSDGS filtering scheme is found more straightforward and far more robust toward a noise filtering than conventional image processing schemes. System achieves a recognition rate of 99.14% with identifying each IC chip marking within 0.05 sec. System provides an ideal solution for real-time inspection of IC markings in high-throughput SMD packaging applications. | en_US |
| dc.language.iso | en_US | en_US |
| dc.publisher | CSIR | en_US |
| dc.source | JSIR Vol.68(05) [May 2009] | en_US |
| dc.subject | Learning vector quantization | en_US |
| dc.subject | On-line inspection | en_US |
| dc.subject | Surface mounted device | en_US |
| dc.title | An automated IC chip marking inspection system for surface mounted devices on taping machines | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | JSIR Vol.68(05) [May 2009] | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| JSIR 68(5) 361-366.pdf | 119.95 kB | Adobe PDF | View/Open |
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