Publication2026
Defect Detection in PCB Design Using Segmentation and Self-Supervised Learning
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- National
- Journal
- 2026 5th International Conference on Power Control and Computing Technologies Icpc2t 2026
- Pages
- 1-6
- Published
- 1 January 2026
- DOI
- 10.1109/icpc2t68221.2026.11645018
- Scopus ID
- 105048800543
- Authors
- 6
- Open access
- No
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Self-supervised learning for PCB defect detection represents novel research methodology in machine learning for industrial quality control applications
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
12 Ensure sustainable consumption and production patterns
Manufacturing quality control through reduced defects supports responsible consumption by improving product durability and reducing electronic waste
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-3.2 Digital technology and artificial intelligence
AI-based manufacturing quality control demonstrates digital technology and artificial intelligence application for industrial automation
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Defect Detection in PCB Design Using Segmentation and Self-Supervised Learning”. Kortex.