MEMS High Aspect Ratio Trench Three-Dimensional Measurement Using Through-Focus Scanning Optical Microscopy and Deep Learning Method

Abstract

High-aspect-ratio structures have become increasingly important in MEMS devices. In situ, real-time critical dimension and depth measurement for high-aspect-ratio structures is critical for optimizing the deep etching process. Through-focus scanning optical microscopy (TSOM) is a high-throughput and inexpensive optical measurement method for critical dimension and depth measurement. Thus far, TSOM has only been used to measure targets with dimension of 1 μm or less, which is far from sufficient for MEMS. Deep learning is a powerful tool that improves the TSOM performance by taking advantage of additional intensity information. In this work, we propose a convolutional neural network model-based TSOM method for measuring individual high-aspect-ratio trenches on silicon with width up to 30 μm and depth up to 440 μm. Experimental demonstrations are conducted and the results show that the proposed method is suitable for measuring the width and depth of high-aspect-ratio trenches with a standard deviation and error of approximately a hundred nanometers or less. The proposed method can be applied to the semiconductor field.