Chemical mechanical polishing (CMP) has become one of the most important process stages in the fabrication of advanced integrated circuits (IC). The CMP pattern effect strongly influences the planarization of the chip surface morphology after CMP, degrading the performance and the yield of the circuits. In this paper, we introduce a method to predict the post-CMP surface morphology with a convolutional neural network (CNN)-based CMP model. Then, CNN-based, density step height (DSH)-based, and common neural-network-based CMP models are built to compare the accuracy of the predictions. The test chips are designed and taped out and the predictions of the three models are compared with experimental results measured by an atomic force profiler (AFP) and scanning electron microscope (SEM). The results show that CNN-based CMP models have better accuracy by taking advantage of the CNN networks to extract features from images instead of the traditional equivalent pattern parameters. The effective planarization length (EPL) is introduced and defined to make better predictions with real-time CMP models and in dummy filling tasks. Experiments are designed to show a method to solve the EPL.

An effective method has been developed to facilitate the synthesis of amides and esters at ambient temperature within 5 min, in which a stable and easily accessible triflylpyridinium reagent is used. Remarkably, this method not only has a wide range of substrate compatibility but also could realize the scalable synthesis of peptide and ester via a continuous flow process. Moreover, excellent chirality retentions are presented during activation of carboxylic acid.