Forecasting Outputs of the Patterned Sapphire Substrate Processes

Abstract

The invention of Light Emitting Diode (LED) to replace the original lighting equipment is the original goal of human beings However from 2012 to 2016 electronic terminal products have become the most important demand of the LED industry The update rate of electronic terminal products is once a year so cost and time become the key Patterned Sapphire Substrate (PSS) process can directly increase brightness by 10% to 25% and has changed the original LED manufacturing process PSS helps accurately and quickly adjust the process parameters according to the specifications of the final products This allows for the reduction of the testing time and the production cost However there is an inherent trial and error phenomenon in the adjustment method Even though the traditional adjustment is supplemented by the personal experience of the process engineer years of experience is required for accurate production results And different process engineers often have different suggestions for the parameter adjustments This thesis implements three predictive models for the PSS results using Artificial Neural Network (ANN) Support Vector Machine (SVM) and Multiple Regression Analysis (MRA) The real production data of a PSS company is used to demonstrate the predictive performance of the proposed model According to the experimental results ANN SVM and MRA all have good prediction capabilities of the PSS results During process adjustment and new product development the process engineers can utilize our prediction model for process parameter tuning

Date of Award	2020
Original language	English
Supervisor	Tai-Yue Wang (Supervisor)