• blurriness
• float zone method
• image quality assessment
• silicon wafers

Nowadays we find ourselves surrounded by electronic devices and it is highly likely they
use integrated circuits and chips produced from monocrystalline silicon wafers. The process
of producing silicon wafers starts with polycrystalline silicon that is first melted and
then recrystallized in a monocrystalline structure. Usually, in an industrial setting, this
process can be achieved with either the Czochralski or the Float Zone method.
The growth process of the ingots is a time-consuming batch process and, in order to
reduce the cost per unit of the final products, it is necessary to increase crystal yield.
This can be achieved by properly regulating the parameters that affect the quality of the
monocrystalline silicon obtained, avoiding anomalies that would lead to defective and
low-quality ingots. Obtaining high-quality ingots also reduces material waste and energy
consumption. Parameters regulation is based on the information acquired through different sensors and
data from previous runs stored in an archive. The camera vision system is particularly
important as it monitors the growth chamber throughout the whole process. The frames
captured are constantly processed to extract important geometric parameters.
This project is aimed at implementing an algorithm that ensures consistent high-quality
images across the production plant, allowing a coherent feature extraction required for
a proper process control. The high variation in the content of images captured across
multiple cameras makes being able to properly tune camera focus a challenge. The focal
length of the camera is manually adjusted with the aid of an algorithm that scores the blur
content of the image. Properly focusing the camera ensures that the images acquired during the growth process are of high enough quality to allow feature extraction and coherent control of different
process parameters.