Application of Neural Network-Based Oxide Deposition Models to CMP Modeling. (1st January 2019)

• Record Type: Journal Article
• Authors: Ghulghazaryan, Ruben; Piliposyan, Davit; Wilson, Jeff
• Is Part Of: ECS journal of solid state science and technology. Volume 8:Number 5(2019)
• Page Start: P3154
• Page End: P3162
• Publication Date: 2019-01-01
• Subjects: Microelectronics - Semiconductor Processing -- CMP modeling -- Machine Learning -- Neural Networks; Solid state chemistry -- Periodicals; Electronics -- Materials -- Periodicals; Electrochemistry -- Periodicals; 541.0421
• DOI: 10.1149/2.0231905jss
• Languages: English
• ISSNs: 2162-8777

Abstract

Abstract : Numerous deposition processes are used in modern semiconductor manufacturing, including high-density plasma chemical vapor deposition (HDP-CVD), spin-on dielectric (SOD), flowable CVD (FCVD), and enhanced high aspect ratio processes (eHARP). Generation of high quality post-deposition surface profiles is crucial for chemical-mechanical planarization (CMP) model building, due to the complex nature of the CMP process and long-range effects in CMP. Measurements show complicated post-deposition surface profile height dependence on the underlying pattern for these deposition processes. While high-quality compact models exist for HDP-CVD and SOD processes, building compact models for FCVD and eHARP is a challenge, since they include several deposition and annealing steps, and show complicated behavior width respect to the underlying pattern. In this paper, we present the application of neural networks (NNs) to post-deposition surface profiles modeling for the above-mentioned deposition processes. Experiments showed that NNs should have at least two hidden layers and 6–10 neurons per hidden layer to capture the complexity of deposited surface profiles. The application of NNs to modeling surface profiles of deposition processes has shown that NNs provide a general approach for modeling surface profiles of deposition processes without long-range effects for CMP modeling, irrespective of the complexity of the deposition process.