An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS
With the advancement of miniaturization in electronics and the ubiquity of micro-electro-mechanical systems (MEMS) in different applications including computing, sensing and medical apparatus, the importance of increasing production yields and ensuring the quality standard of products has become an...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-09-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/18/6141 |
| _version_ | 1797517195227430912 |
|---|---|
| author | Amin Amini Jamil Kanfoud Tat-Hean Gan |
| author_facet | Amin Amini Jamil Kanfoud Tat-Hean Gan |
| author_sort | Amin Amini |
| collection | DOAJ |
| description | With the advancement of miniaturization in electronics and the ubiquity of micro-electro-mechanical systems (MEMS) in different applications including computing, sensing and medical apparatus, the importance of increasing production yields and ensuring the quality standard of products has become an important focus in manufacturing. Hence, the need for high-accuracy and automatic defect detection in the early phases of MEMS production has been recognized. This not only eliminates human interaction in the defect detection process, but also saves raw material and labor required. This research developed an automated defects recognition (ADR) system using a unique plenoptic camera capable of detecting surface defects of MEMS wafers using a machine-learning approach. The developed algorithm could be applied at any stage of the production process detecting defects at both entire MEMS wafer and single component scale. The developed system showed an F1 score of 0.81 U on average for true positive defect detection, with a processing time of 18 s for each image based on 6 validation sample images including 371 labels. |
| first_indexed | 2024-03-10T07:13:22Z |
| format | Article |
| id | doaj.art-4969d702a2fd49459a236286bb92443a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T07:13:22Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4969d702a2fd49459a236286bb92443a2023-11-22T15:12:17ZengMDPI AGSensors1424-82202021-09-012118614110.3390/s21186141An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMSAmin Amini0Jamil Kanfoud1Tat-Hean Gan2Brunel Innovation Centre, Brunel University London, Uxbridge UB8 3PH, UKBrunel Innovation Centre, Brunel University London, Uxbridge UB8 3PH, UKBrunel Innovation Centre, Brunel University London, Uxbridge UB8 3PH, UKWith the advancement of miniaturization in electronics and the ubiquity of micro-electro-mechanical systems (MEMS) in different applications including computing, sensing and medical apparatus, the importance of increasing production yields and ensuring the quality standard of products has become an important focus in manufacturing. Hence, the need for high-accuracy and automatic defect detection in the early phases of MEMS production has been recognized. This not only eliminates human interaction in the defect detection process, but also saves raw material and labor required. This research developed an automated defects recognition (ADR) system using a unique plenoptic camera capable of detecting surface defects of MEMS wafers using a machine-learning approach. The developed algorithm could be applied at any stage of the production process detecting defects at both entire MEMS wafer and single component scale. The developed system showed an F1 score of 0.81 U on average for true positive defect detection, with a processing time of 18 s for each image based on 6 validation sample images including 371 labels.https://www.mdpi.com/1424-8220/21/18/6141MEMSdefect detectionmachine-learningdeep-learningCNN |
| spellingShingle | Amin Amini Jamil Kanfoud Tat-Hean Gan An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS Sensors MEMS defect detection machine-learning deep-learning CNN |
| title | An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS |
| title_full | An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS |
| title_fullStr | An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS |
| title_full_unstemmed | An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS |
| title_short | An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS |
| title_sort | artificial intelligence driven predictive model for surface defect detections in medical mems |
| topic | MEMS defect detection machine-learning deep-learning CNN |
| url | https://www.mdpi.com/1424-8220/21/18/6141 |
| work_keys_str_mv | AT aminamini anartificialintelligencedrivenpredictivemodelforsurfacedefectdetectionsinmedicalmems AT jamilkanfoud anartificialintelligencedrivenpredictivemodelforsurfacedefectdetectionsinmedicalmems AT tatheangan anartificialintelligencedrivenpredictivemodelforsurfacedefectdetectionsinmedicalmems AT aminamini artificialintelligencedrivenpredictivemodelforsurfacedefectdetectionsinmedicalmems AT jamilkanfoud artificialintelligencedrivenpredictivemodelforsurfacedefectdetectionsinmedicalmems AT tatheangan artificialintelligencedrivenpredictivemodelforsurfacedefectdetectionsinmedicalmems |