Expectation-maximization machine learning model for micromechanical evaluation of thermally-cycled solder joints in a semiconductor
學年 111
學期 2
出版（發表）日期 2023-04-27
作品名稱 Expectation-maximization machine learning model for micromechanical evaluation of thermally-cycled solder joints in a semiconductor
作品名稱（其他語言）
著者 Tzu-Chia Chen
單位
出版者
著錄名稱、卷期、頁數 Journal of Physics-Condensed Matter 35, 305901
摘要 This paper aims to study the microstructural and micromechanical variations of solder joints in a semiconductor under the evolution of thermal-cycling loading. For this purpose, a model was developed on the basis of expectation-maximization machine learning (ML) and nanoindentation mapping. Using this model, it is possible to predict and interpret the microstructural features of solder joints through the micromechanical variations (i.e. elastic modulus) of interconnection. According to the results, the classification of Sn-based matrix, intermetallic compounds (IMCs) and the grain boundaries with specified elastic-modulus ranges was successfully performed through the ML model. However, it was detected some overestimations in regression process when the interfacial regions got thickened in the microstructure. The ML outcomes also revealed that the thermal-cycling evolution was accompanied with stiffening and growth of IMCs; while the spatial portion of Sn-based matrix decreased in the microstructure. It was also figured out that the stiffness gradient becomes intensified in the treated samples, which is consistent with this fact that the thermal cycling increases the mechanical mismatch between the matrix and the IMCs.
關鍵字 machine learning;micromechanical properties;nanoindentation;solder joint
語言 en
ISSN 1361-648X; 0953-8984
期刊性質 國外
收錄於 SCI EI Scopus
產學合作 國外
通訊作者
審稿制度 是
國別 GBR
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/125177 )