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

Data-driven modelling and monitoring of industrial processes with applications in nuclear waste vitrification

PhD ThesisProcess models are critical for process monitoring, control, and optimisation. With the increasing amount of process data and advancements in computational hardware, data-driven models are a good alternative to mechanistic models, which often have inaccuracies or are too costly to develop. One problem with data-driven models is the difficulty in ensuring that the models perform well on new data and produce accurate predictions in complex situations, which are frequently encountered in the process industry. Within this context, part of this thesis explores developing better data-driven models through using a latent variable technique, known as slow feature analysis, as a pre-processing step to regression. Slow feature analysis extracts slow varying features that contain underlying trends in the data, which can improve model performance through providing more meaningful information to regression, reducing noise, and reducing dimensionality. Firstly, the effectiveness of combining linear slow feature analysis with a neural network is demonstrated on two industrial case studies of soft sensor development and is compared with conventional techniques, such as neural networks and integration of principal component analysis with a neural network. It is shown that integration of slow feature analysis with neural networks can significantly improve model performance. However, linear slow feature analysis can fail to extract the driving forces behind data in nonlinear situations such as batch processes. Therefore, using kernel slow feature analysis with a neural network is proposed to further enhance process model performance. A numerical example was used to demonstrate the effective extraction of driving forces in a nonlinear case where linear slow feature analysis cannot. Model generalisation performance was improved using the proposed method on both this numerical example, and an industrial penicillin process case study. Dealing with radioactive nuclear waste is an important obstacle in nuclear energy. Sellafield Ltd have a nuclear waste vitrification plant which converts high-level nuclear waste into a more stable, lower volume glass form, which is more appropriate for long term storage in sealed containers. This thesis presents three applications of data-driven modelling to this nuclear waste vitrification process. A predictive model of the pour rate of processed nuclear waste into containers, an early detection system for blockages in the dust scrubber, and a model of the long-term chemical durability of the stored glass waste. These applications use the previously developed slow feature analysis methods, as well as other data-driven techniques such as extreme learning machine and bootstrap aggregation, for enhancing the model performance.Engineering and Physical Sciences Research Council (EPSRC) and Sellafield Lt