Exploring enhanced CNN for predicting semiconductor device health compared to support vector machine

The goal of this research is to improve the accuracy of semiconductor device health prediction by comparing the efficacy of Enhanced CNN with the Support Vector Machine technique. Support vector machines and Novel Enhanced CNN were the two groups that were used. The parameters were a G-Power of 80%, an alpha of 0.05, and a beta of 0.2. Two iterations were conducted for each group, for a total of 40 iterations and a sample size of 1641. Independent T-tests confirmed the significance of the hypothesis at a level of p = 0.003 (p < 0.05). Based on statistical study with SPSS, the Novel Enhanced CNN and the Support Vector Machine Algorithm had accuracy of 97.10% and 95.85%, respectively. These noteworthy accuracy numbers imply that the Enhanced CNN strategy (97.10% accuracy) works better than the Support Vector Machine methodology (95.85% accuracy) for estimating the health of semiconductor devices.