Dong Ni

Real-time carbon content control for PECVD ZrO/sub 2/ thin-film growth

We present a methodology for real-time control of thin-film carbon content in a plasma-enhanced metal-organic chemical vapor deposition process using combination of online gas phase measurements obtained through optical emission spectroscopy and off-line (ex situ) measurements of film composition obtained via X-ray photoelectron spectroscopy (XPS). Initially, an estimation model of carbon content of ZrO/sub 2/ thin films based on real-time optical emission spectroscopy data is presented. Then, a feedback control scheme, which employs the proposed estimation model and a proportional-integral controller, is developed to achieve carbon content control. Using this approach, a real-time control system is developed and implemented on an experimental electron cyclotron resonance high-density plasma-enhanced chemical vapor deposition system to demonstrate the effectiveness of real-time feedback control of carbon content. Experimental results of depositions and XPS analysis of deposited thin films under both open-loop and closed-loop operations are shown and compared. The advantages of operating the process under real-time feedback control in terms of robust operation and lower carbon content are demonstrated.

Construction of stochastic PDEs for feedback control of surface roughness in thin film deposition

In this work, we develop a systematic method for the construction of linear stochastic partial differential equation (PDE) models for feedback control of surface roughness in thin film deposition. The method is applied to a representative deposition process and is successfully validated through simulations.

Construction of Stochastic PDEs and Predictive Control of Surface Roughness in Thin Film Deposition

In this work, we develop a systematic method for the construction of linear stochastic partial differential equation (PDE) models for feedback control of surface roughness in thin film deposition using kinetic Monte-Carlo simulations. The method is applied to a representative deposition process and is successfully validated through simulations.

Real-Time Feedback Control of Carbon Content of Zirconium Dioxide Thin Films Using Optical Emission Spectroscopy

Abstract In this work, we present a methodology for real-time carbon content feedback control of a plasma-enhanced metal organic chemical vapor deposition process using optical emission spectroscopy. Initially, an estimation model of carbon content of ZrO2 thin films based on real-time optical emission spectroscopy data is presented. Then, a feedback control scheme, which employs the proposed estimation model and a proportional-integral controller, is developed to achieve carbon content control. Using this approach, a real-time control system is developed and implemented on an experimental electron cyclotron resonance high density plasma-enhanced chemical vapor deposition system at UCLA to demonstrate the effectiveness of real-time feedback control of carbon content. Experimental results of the deposition process under both open-loop and closed-loop operations are shown and compared. The advantages of operating the process under real-time feedback control in terms of higher productivity, reduced process variation and lower carbon content are demonstrated

Predictive Control of Thin Film Surface Microstructure in a Complex Deposition Process

Abstract In this work, a complex deposition process including two types of molecules whose growth behaviors are very different and influenced by long range interactions is investigated. The study of this process is motivated by recent experimental results on the growth of high-κ dielectric thin films using plasma-enhanced chemical vapor deposition (PECVD). A multi-component kinetic Monte-Carlo (kMC) model is developed for the deposition. The dependence of the surface microstructure of the thin film, such as island size and surface roughness, on substrate temperature are studied. The surface morphology is found to be strongly influenced by these two factors and growth regimes governed by short and long range interactions are observed. Furthermore, a kMC model-predictive control scheme which uses the substrate temperature to control the final surface roughness of the thin film is proposed. The closed-loop simulation results demonstrate that robust deposition with controlled thin film surface roughness can be achieved under the proposed kMC model-predictive controller.