Contributions to statistical methods of process monitoring and adjustment

Ph.DDOCTOR OF PHILOSOPH

Novel techniques for the run by run process control of chemical-mechanical polishing

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.Includes bibliographical references (p. 139-143).by Taber H. Smith.M.S

Development of Soft Sensor Model Using Moving Window Approach

Soft sensors are used broadly in the industries to predict the process variables which are not measurable by sensors. The objective of this project is to develop a datadriven soft sensor using Moving Window approach with the selective regression techniques and to evaluate and validate the advantages and performances of Moving Window approach over the traditional soft sensor models. Time invariant and stationary process conditions are those assumptions made in developing soft sensors, and these assumptions causes degradations and limitations to the soft sensors in estimating process variables. Degradations of soft sensors are caused by process shift, catalyst performance lost and et cetera. Besides that, the restrictions of sensors in estimating difficult-to-measure variables and the delays during the laboratory tests have becomeone of the factors in developing soft sensor. This paper presents a study regarding the multivariate statistical process control techniques that can be used in developing soft sensors such as Least Square Regression method, Partial Least Square Regression method and Principle Component Analysis. The scope of study for the project includes understanding the concept andwhat are the adaptive schemes available to construct the soft sensors. Besides that further research on Moving Window approach together with MSPC techniques will be carried out which can be adapted into the adaptive models to develop the soft sensors. Systematic approach will be presented through this project in using Moving Window approach to construct the soft sensors and this includes an analysis of an appropriate case study where the approach can be implemented. Keywords: Multivariate Statistical Process Control techniques, Least Square Regression method, Partial Least Square Regression method and Principle Component Analysi

Virtual metrology for plasma etch processes.

Plasma processes can present dicult control challenges due to time-varying dynamics and a lack of relevant and/or regular measurements. Virtual metrology (VM) is the use of mathematical models with accessible measurements from an operating process to estimate variables of interest. This thesis addresses the challenge of virtual metrology for plasma processes, with a particular focus on semiconductor plasma etch. Introductory material covering the essentials of plasma physics, plasma etching, plasma measurement techniques, and black-box modelling techniques is rst presented for readers not familiar with these subjects. A comprehensive literature review is then completed to detail the state of the art in modelling and VM research for plasma etch processes. To demonstrate the versatility of VM, a temperature monitoring system utilising a state-space model and Luenberger observer is designed for the variable specic impulse magnetoplasma rocket (VASIMR) engine, a plasma-based space propulsion system. The temperature monitoring system uses optical emission spectroscopy (OES) measurements from the VASIMR engine plasma to correct temperature estimates in the presence of modelling error and inaccurate initial conditions. Temperature estimates within 2% of the real values are achieved using this scheme. An extensive examination of the implementation of a wafer-to-wafer VM scheme to estimate plasma etch rate for an industrial plasma etch process is presented. The VM models estimate etch rate using measurements from the processing tool and a plasma impedance monitor (PIM). A selection of modelling techniques are considered for VM modelling, and Gaussian process regression (GPR) is applied for the rst time for VM of plasma etch rate. Models with global and local scope are compared, and modelling schemes that attempt to cater for the etch process dynamics are proposed. GPR-based windowed models produce the most accurate estimates, achieving mean absolute percentage errors (MAPEs) of approximately 1:15%. The consistency of the results presented suggests that this level of accuracy represents the best accuracy achievable for the plasma etch system at the current frequency of metrology. Finally, a real-time VM and model predictive control (MPC) scheme for control of plasma electron density in an industrial etch chamber is designed and tested. The VM scheme uses PIM measurements to estimate electron density in real time. A predictive functional control (PFC) scheme is implemented to cater for a time delay in the VM system. The controller achieves time constants of less than one second, no overshoot, and excellent disturbance rejection properties. The PFC scheme is further expanded by adapting the internal model in the controller in real time in response to changes in the process operating point

Virtual metrology for plasma etch processes.

Plasma processes can present dicult control challenges due to time-varying dynamics and a lack of relevant and/or regular measurements. Virtual metrology (VM) is the use of mathematical models with accessible measurements from an operating process to estimate variables of interest. This thesis addresses the challenge of virtual metrology for plasma processes, with a particular focus on semiconductor plasma etch. Introductory material covering the essentials of plasma physics, plasma etching, plasma measurement techniques, and black-box modelling techniques is rst presented for readers not familiar with these subjects. A comprehensive literature review is then completed to detail the state of the art in modelling and VM research for plasma etch processes. To demonstrate the versatility of VM, a temperature monitoring system utilising a state-space model and Luenberger observer is designed for the variable specic impulse magnetoplasma rocket (VASIMR) engine, a plasma-based space propulsion system. The temperature monitoring system uses optical emission spectroscopy (OES) measurements from the VASIMR engine plasma to correct temperature estimates in the presence of modelling error and inaccurate initial conditions. Temperature estimates within 2% of the real values are achieved using this scheme. An extensive examination of the implementation of a wafer-to-wafer VM scheme to estimate plasma etch rate for an industrial plasma etch process is presented. The VM models estimate etch rate using measurements from the processing tool and a plasma impedance monitor (PIM). A selection of modelling techniques are considered for VM modelling, and Gaussian process regression (GPR) is applied for the rst time for VM of plasma etch rate. Models with global and local scope are compared, and modelling schemes that attempt to cater for the etch process dynamics are proposed. GPR-based windowed models produce the most accurate estimates, achieving mean absolute percentage errors (MAPEs) of approximately 1:15%. The consistency of the results presented suggests that this level of accuracy represents the best accuracy achievable for the plasma etch system at the current frequency of metrology. Finally, a real-time VM and model predictive control (MPC) scheme for control of plasma electron density in an industrial etch chamber is designed and tested. The VM scheme uses PIM measurements to estimate electron density in real time. A predictive functional control (PFC) scheme is implemented to cater for a time delay in the VM system. The controller achieves time constants of less than one second, no overshoot, and excellent disturbance rejection properties. The PFC scheme is further expanded by adapting the internal model in the controller in real time in response to changes in the process operating point

Application of First Principle Modeling in Combination with Empirical Design of Experiments and Real-Time Data Management for the Automated Control of Pharmaceutical Unit Operations

The U.S. Food and Drug Administration has accepted the guidelines put forth by the International Conference on Harmonization (ICH-Q8) that allow for operational flexibility within a validated design space. These Quality by Design initiatives have allowed drug manufacturers to incorporate more rigorous scientific controls into their production streams. Fully automated control systems can incorporate information about a process back into the system to adjust process variables to consistently hit product quality targets (feedback control), or monitor variability in raw materials or intermediate products to adjust downstream manufacturing operations (feedforward control). These controls enable increased process understanding, continuous process and product improvement, assurance of product quality, and the possibility of real-time release. Control systems require significant planning and an initial investment, but the improved product quality and manufacturing efficiency provide ample incentive for the expense. The fluid bed granulation and drying unit operation was an excellent case study for control systems implementation because it is a complex unit operation with dynamic powder movement, high energy input, solid-liquid-gas interactions, and difficulty with scale-up development. Traditionally, fluid bed control systems have either used first principle calculations to control the internal process environment or purely empirical methods that incorporate online process measurements with process models. This dissertation was predicated on the development of a novel hybrid control system that combines the two traditional approaches. The hybrid controls reduced the number of input factors for the creation of efficient experimental designs, reduced the variability between batches, enabled control of the drying process for a sensitive active pharmaceutical ingredient, rendered preconditioned air systems unnecessary, and facilitated the collection of data for the development of process models and the rigorous calculation of design spaces. Significant variably in the inlet airstream was able to be mitigated using feedforward controls, while process analytical technology provided immediate feedback about the process for strict control of process inputs. Tolerance surfaces provided the ideal tool for determining design spaces that assured the reduction of manufacturing risk among all future batches, and the information gained using small scale experimentation was leveraged to provide efficient scale-up, making these control systems feasible for consistent use