Wire bonding defect detection apparatus and operation method thereof
AssigneeLG Energy Solution, Ltd.
InventorJee Hoon Choi
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Abstract
An apparatus and method are disclosed for detecting a bonding defect occurring during an ultrasonic wire bonding process between a battery cell and a busbar connected to each other by ultrasonic wire bonding. A bonding parameter collector, coupled to an ultrasonic wire bonding machine, may collect ultrasonic bonding parameters continuously during the ultrasonic wire bonding process, machine learning training may be performed by a convolutional neural network, and a bonding defect may be detected based thereon.
Claims17
An apparatus for detecting a bonding defect occurring during an ultrasonic wire bonding process between a battery cell and a busbar connected to each other by ultrasonic wire bonding, the apparatus comprising:
- a bonding parameter collector configured to continuously collect a plurality of ultrasonic bonding parameters;
- a data generator configured to generate data by applying wavelet transform to the plurality of ultrasonic bonding parameters so as to convert the plurality of ultrasonic bonding parameters into images having a two-dimensional array;
- a convolutional neural network configured to perform machine-learning training using the data generated by the data generator;
- and
- a defect determination component configured to determine a defect due to the ultrasonic wire bonding using the convolutional neural network that is trained,
- wherein the plurality of ultrasonic bonding parameters comprise a measured value of wire deformation, and
- wherein the measured value of the wire deformation includes a measured value of a height of a wire changed as the wire is melted.
A method of detecting a bonding defect occurring during an ultrasonic wire bonding process between a battery cell and a busbar connected to each other by ultrasonic wire bonding, the method comprising:
- continuously collecting a plurality of ultrasonic bonding parameters;
- generating a training data by converting the plurality of ultrasonic bonding parameters into images having a two-dimensional array using wavelet transform;
- training a convolutional neural network with the generated training data;
- and
- determining a defect due to the ultrasonic wire bonding using the convolutional neural network,
- wherein the plurality of ultrasonic bonding parameters comprise a measured value of wire deformation, and
- wherein the measured value of the wire deformation includes a measured value of a height of a wire changed as the wire is melted.
Dependencies are inferred from the claim text.