Ball bonding inspections using a conjoint framework with machine learning and human judgement

TY - JOUR

T1 - Ball bonding inspections using a conjoint framework with machine learning and human judgement

AU - Chan, Kit Yan

AU - Yiu, Ka Fai Cedric

AU - Lam, Hak Keung

AU - Wong, Bert Wei

PY - 2021/4

Y1 - 2021/4

N2 - Ball bonding inspections with human vision are essential in manufacturing processes of semiconductors devices and integrated circuits (ICs). The inspections are an intensive task which involves human labours to detect poor bonds. Prolonged visual inspections cause poor inspection integrity due to eye-fatigue. However, inspections nowadays are mostly conducted manually by humans which cannot satisfy the demanding productions. Motivated by the extraordinary performance of machine learning for manufacturing inspections, a detection framework integrated with machine learning and human judgement is proposed to aid bonding inspections based on visual images. The detection framework is incorporated with the convolution neural network (CNN), support vector machine (SVM) and circle hough transform algorithm (CHT); human judgement is only used when the detection uncertainty is below the threshold. The novel machine learning integration is proposed on the detection framework to improve the generalization capabilities. The CNN architecture is redeveloped by incorporating with the SVM which is generally more effective than the fully connected network in the classical CNN. Also a novel training function is proposed based on the CHT to ensure the inspection reliability; the function not only takes into account real image captures, but also locates important features using pattern analysis of the ball bondings. Experimental results show that significantly better classifications can be achieved by the proposed framework compared with the classical CNN and other commonly used classifiers. Only the machine learning determinations below the threshold are reassessed by human judgements.

AB - Ball bonding inspections with human vision are essential in manufacturing processes of semiconductors devices and integrated circuits (ICs). The inspections are an intensive task which involves human labours to detect poor bonds. Prolonged visual inspections cause poor inspection integrity due to eye-fatigue. However, inspections nowadays are mostly conducted manually by humans which cannot satisfy the demanding productions. Motivated by the extraordinary performance of machine learning for manufacturing inspections, a detection framework integrated with machine learning and human judgement is proposed to aid bonding inspections based on visual images. The detection framework is incorporated with the convolution neural network (CNN), support vector machine (SVM) and circle hough transform algorithm (CHT); human judgement is only used when the detection uncertainty is below the threshold. The novel machine learning integration is proposed on the detection framework to improve the generalization capabilities. The CNN architecture is redeveloped by incorporating with the SVM which is generally more effective than the fully connected network in the classical CNN. Also a novel training function is proposed based on the CHT to ensure the inspection reliability; the function not only takes into account real image captures, but also locates important features using pattern analysis of the ball bondings. Experimental results show that significantly better classifications can be achieved by the proposed framework compared with the classical CNN and other commonly used classifiers. Only the machine learning determinations below the threshold are reassessed by human judgements.

KW - Ball bonding

KW - Human judgement

KW - Machine learning

KW - Manufacturing inspection

KW - Manufacturing of electronic products

KW - Threshold detection

UR - http://www.scopus.com/inward/record.url?scp=85099704862&partnerID=8YFLogxK

U2 - 10.1016/j.asoc.2021.107115

DO - 10.1016/j.asoc.2021.107115

M3 - Article

AN - SCOPUS:85099704862

SN - 1568-4946

VL - 102

JO - APPLIED SOFT COMPUTING

JF - APPLIED SOFT COMPUTING

M1 - 107115

ER -