0
Research Papers

On-Line Quality Detection of Ultrasonic Wire Bonding via Refining Analysis of Electrical Signal From Ultrasonic Generator

[+] Author and Article Information
Wuwei Feng1

Theory of Lubrication and Bearing Institute, College of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, Chinafengwuwei@163.com

Qingfeng Meng, Youbo Xie, Hong Fan

Theory of Lubrication and Bearing Institute, College of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China

1

Corresponding author.

J. Electron. Packag 132(4), 041002 (Nov 19, 2010) (11 pages) doi:10.1115/1.4002900 History: Received February 01, 2010; Revised October 11, 2010; Published November 19, 2010; Online November 19, 2010

A technique for on-line quality detection of ultrasonic wire bonding is developed. The electrical signals from the ultrasonic generator supply, namely, voltage and current, are picked up by a measuring circuit and transformed into digital signals by a data acquisition system. A new feature extraction method is presented to characterize the transient property of the electrical signals and further evaluate the bond quality. The method includes three steps. First, the captured voltage and current are filtered by digital bandpass filter banks to obtain the corresponding subband signals such as fundamental signal, second harmonic, and third harmonic. Second, each subband envelope is obtained using the Hilbert transform for further feature extraction. Third, the subband envelopes are, respectively, separated into three phases, namely, envelope rising, stable, and damping phases, to extract the tiny waveform changes. The different waveform features are extracted from each phase of these subband envelopes. The principal components analysis method is used for the feature selection in order to remove the relevant information and reduce the dimension of original feature variables. Using the selected features as inputs, an artificial neural network is constructed to identify the complex bond fault pattern. By analyzing experimental data with the proposed feature extraction method and neural network, the results demonstrate the advantages of the proposed feature extraction method and the constructed artificial neural network in detecting and identifying bond quality.

Copyright © 2010 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 1

Structure of a typical ultrasonic ball bonder

Grahic Jump Location
Figure 2

Bonding quality monitoring principle

Grahic Jump Location
Figure 3

Principle of measuring circuit

Grahic Jump Location
Figure 4

Bonding electrical signals and FFT spectrums

Grahic Jump Location
Figure 5

Flow chart of feature extraction method

Grahic Jump Location
Figure 6

Filtered results of the bonding electrical signals shown in Fig. 4: (a) fundamental signal of voltage, (b) second harmonic signal of voltage, (c) third harmonic signal of voltage, (d) fundamental signal of current, (e) second harmonic signal of current, and (f) third harmonic signal of current

Grahic Jump Location
Figure 7

Envelopes of the subband signals shown in Fig. 6: (a) fundamental envelope of voltage, (b) second harmonic envelope of voltage, (c) third harmonic envelope of voltage, (d) fundamental envelope of current, (e) second harmonic envelope of current, and (f) third harmonic envelope of current

Grahic Jump Location
Figure 8

Segmentation principle of fundamental signal envelope of voltage

Grahic Jump Location
Figure 9

The subband signal envelopes of good bonding state and wire break bonding state: (a) fundamental envelope of voltage, (b) second harmonic envelope of voltage, (c) third harmonic envelope of voltage, (d) fundamental envelope of current, (e) second harmonic envelope of current, and (f) third harmonic envelope of current

Grahic Jump Location
Figure 10

The segmental results of every subband envelope shown in Fig. 9

Grahic Jump Location
Figure 11

The classifying result of two bonding conditions with first 2 PCs

Grahic Jump Location
Figure 12

Sketch of a precision bond shear strength tester

Grahic Jump Location
Figure 13

SEMs of the bonds with different shear strengths: (a) good bond, shear strength=13 gf and (b) weakly adhered bond, shear strength=2.6 gf

Grahic Jump Location
Figure 14

The subband envelopes of good bond and weakly adhered bond: (a) fundamental envelope of voltage, (b) second harmonic envelope of voltage, (c) third harmonic envelope of voltage, (d) fundamental envelope of current, (e) second harmonic envelope of current, and (f) third harmonic envelope of current

Grahic Jump Location
Figure 15

The segmentation results of every subband signal shown in Fig. 1

Grahic Jump Location
Figure 16

The structure of ANN

Grahic Jump Location
Figure 17

ANN predicting bond shear strength

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In