A leading semiconductor manufacturing company wanted to improve their defect identification process. Their existing system was not only tedious and time-consuming but also inefficient to deliver the expected results. This further led to poor yield analysis and production planning thereby affecting the company’s revenue margins. Therefore, they were looking for a high-speed and high-precision defect detection system.
“Improving semiconductor manufacturing yields up to 30%, reducing scrap rates, and optimizing fab operations is achievable with machine learning.”
Softweb Solutions provided them with a backend tool for defect detection using deep learning. Our image analysis system differentiates between the defected and non-defected images of semiconductor wafers. The automated system helps the client to save both time and resources while achieving high levels of accuracy. Moreover, by collecting this defected and non-defected image data, the client is able to gain insights about the frequency and types of defects which can further be put in use to improve the overall production process.