Feasibility Study of Predicting Semiconductor Thin Film Thickness Based on 1D Convolutional Neural Network

Recently, the increased requirement of data processing and reduced data latency has driven the demand for 3D stacks for high-performance semiconductors. The process of stacking hundreds of layers of semiconductor thin film is essential. However, there is a problem that the uniformity of thickness is degraded due to the defect of the semiconductor thin film. This causes deformation of the device structure and is a significant factor in the performance degradation of the semiconductor. To prevent this, it is important to measure the thickness of the thin film quickly and accurately. Optical spectrum analysis is widely used for measuring the thickness of semiconductor thin films. In this paper, we proposed a method to measure the thickness of semiconductor thin films by learning the reflectance spectrum according to the thickness and wavelength of four-layer thin films using a modified model of the well-known deep learning structure (VGG-16 Network). Our semiconductor thin film thickness prediction method performance of about 0.589 MAE was shown. Experimental results show that the proposed model can be a baseline method for predicting the thickness of semiconductor thin films.