{"title":"用于 SAC305 激光焊接缺陷检测的新型多信息融合 CNN","authors":"","doi":"10.1016/j.microrel.2024.115519","DOIUrl":null,"url":null,"abstract":"<div><div>Identification of laser soldering of lead-free solder Sn-3.0Ag-0.5Cu (SAC305) in electronic packaging was still an enormous challenge. It was difficult to detect defects in large-scale production. This work proposed an identification model based on multi-information fusion convolutional neural network (MIFCNN) for inspecting laser soldering process. In this method, the forty images in chronological order and the temperature data were combined as the input to be utilized in detecting defects. The results demonstrated that MIFCNN had best accuracy for three types of joints with accuracy of 98.28 % due to the combination of images and temperature information. The ICNN and TCNN had poor recognition accuracy for the warpage defect with 73.9 % and the poor wetting defect with 66.5 %, respectively. This was because the images and temperature information were the key to identifying the poor wetting defects and warpage defects, respectively. The poor wetting defect could be recognized by difference of contact angle, while the warpage defect could be significantly detected by maximum temperature. This work could help detecting defects of laser soldering in the actual production and widen the application of MIFCNN in the field of laser soldering.</div></div>","PeriodicalId":51131,"journal":{"name":"Microelectronics Reliability","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel multi-information fusion CNN for defect detection in laser soldering of SAC305\",\"authors\":\"\",\"doi\":\"10.1016/j.microrel.2024.115519\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Identification of laser soldering of lead-free solder Sn-3.0Ag-0.5Cu (SAC305) in electronic packaging was still an enormous challenge. It was difficult to detect defects in large-scale production. This work proposed an identification model based on multi-information fusion convolutional neural network (MIFCNN) for inspecting laser soldering process. In this method, the forty images in chronological order and the temperature data were combined as the input to be utilized in detecting defects. The results demonstrated that MIFCNN had best accuracy for three types of joints with accuracy of 98.28 % due to the combination of images and temperature information. The ICNN and TCNN had poor recognition accuracy for the warpage defect with 73.9 % and the poor wetting defect with 66.5 %, respectively. This was because the images and temperature information were the key to identifying the poor wetting defects and warpage defects, respectively. The poor wetting defect could be recognized by difference of contact angle, while the warpage defect could be significantly detected by maximum temperature. This work could help detecting defects of laser soldering in the actual production and widen the application of MIFCNN in the field of laser soldering.</div></div>\",\"PeriodicalId\":51131,\"journal\":{\"name\":\"Microelectronics Reliability\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-10-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Microelectronics Reliability\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0026271424001999\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Microelectronics Reliability","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0026271424001999","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
A novel multi-information fusion CNN for defect detection in laser soldering of SAC305
Identification of laser soldering of lead-free solder Sn-3.0Ag-0.5Cu (SAC305) in electronic packaging was still an enormous challenge. It was difficult to detect defects in large-scale production. This work proposed an identification model based on multi-information fusion convolutional neural network (MIFCNN) for inspecting laser soldering process. In this method, the forty images in chronological order and the temperature data were combined as the input to be utilized in detecting defects. The results demonstrated that MIFCNN had best accuracy for three types of joints with accuracy of 98.28 % due to the combination of images and temperature information. The ICNN and TCNN had poor recognition accuracy for the warpage defect with 73.9 % and the poor wetting defect with 66.5 %, respectively. This was because the images and temperature information were the key to identifying the poor wetting defects and warpage defects, respectively. The poor wetting defect could be recognized by difference of contact angle, while the warpage defect could be significantly detected by maximum temperature. This work could help detecting defects of laser soldering in the actual production and widen the application of MIFCNN in the field of laser soldering.
期刊介绍:
Microelectronics Reliability, is dedicated to disseminating the latest research results and related information on the reliability of microelectronic devices, circuits and systems, from materials, process and manufacturing, to design, testing and operation. The coverage of the journal includes the following topics: measurement, understanding and analysis; evaluation and prediction; modelling and simulation; methodologies and mitigation. Papers which combine reliability with other important areas of microelectronics engineering, such as design, fabrication, integration, testing, and field operation will also be welcome, and practical papers reporting case studies in the field and specific application domains are particularly encouraged.
Most accepted papers will be published as Research Papers, describing significant advances and completed work. Papers reviewing important developing topics of general interest may be accepted for publication as Review Papers. Urgent communications of a more preliminary nature and short reports on completed practical work of current interest may be considered for publication as Research Notes. All contributions are subject to peer review by leading experts in the field.