Gaussian Process Regression for Virtual Metrology of Plasma Etch

Plasma etch is a complex semiconductor manufacturing process in which material is removed from the surface of a silicon wafer using a gas in plasma form. As the process etch rate cannot be measured easily during or after processing, virtual metrology is employed to predict the etch rate instantly using ancillary process variables. Virtual metrology is the prediction of metrology variables using other easily accessible variables and mathematical models. This paper investigates the use of Gaussian process regression as a virtual metrology modelling technique for plasma etch data

Real-time virtual metrology and control for plasma etch

Plasma etch is a semiconductor manufacturing process during which material is removed from the surface of semiconducting wafers, typically made of silicon, using gases in plasma form. A host of chemical and electrical complexities make the etch process notoriously difficult to model and troublesome to control. This work demonstrates the use of a real-time model predictive control scheme to control plasma electron density and plasma etch rate in the presence of disturbances to the ground path of the chamber. Virtual metrology (VM) models, using plasma impedance measurements, are used to estimate the plasma electron density and plasma etch rate in real time for control, eliminating the requirement for invasive measurements. The virtual metrology and control schemes exhibit fast set-point tracking and disturbance rejection capabilities. Etch rate can be controlled to within 1% of the desired value. Such control represents a significant improvement over open-loop operation of etch tools, where variances in etch rate of up to 5% can be observed during production processes due to disturbances in tool state and material properties

Global and Local Virtual Metrology Models for a Plasma Etch Process

Virtual metrology (VM) is the estimation of metrology variables that may be expensive or difficult to measure using readily available process information. This paper investigates the application of global and local VM schemes to a data set recorded from an industrial plasma etch chamber. Windowed VM models are shown to be the most accurate local VM scheme, capable of producing useful estimates of plasma etch rates over multiple chamber maintenance events and many thousands of wafers. Partial least-squares regression, artificial neural networks, and Gaussian process regression are investigated as candidate modeling techniques, with windowed Gaussian process regression models providing the most accurate results for the data set investigated

Global and Local Virtual Metrology Models for a Plasma Etch Process

Virtual metrology (VM) is the estimation of metrology variables that may be expensive or difficult to measure using readily available process information. This paper investigates the application of global and local VM schemes to a data set recorded from an industrial plasma etch chamber. Windowed VM models are shown to be the most accurate local VM scheme, capable of producing useful estimates of plasma etch rates over multiple chamber maintenance events and many thousands of wafers. Partial least-squares regression, artificial neural networks, and Gaussian process regression are investigated as candidate modeling techniques, with windowed Gaussian process regression models providing the most accurate results for the data set investigated

Real-time virtual metrology and control for plasma etch

Plasma etch is a semiconductor manufacturing process during which material is removed from the surface of semiconducting wafers, typically made of silicon, using gases in plasma form. A host of chemical and electrical complexities make the etch process notoriously difficult to model and troublesome to control. This work demonstrates the use of a real-time model predictive control scheme to control plasma electron density and plasma etch rate in the presence of disturbances to the ground path of the chamber. Virtual metrology (VM) models, using plasma impedance measurements, are used to estimate the plasma electron density and plasma etch rate in real time for control, eliminating the requirement for invasive measurements. The virtual metrology and control schemes exhibit fast set-point tracking and disturbance rejection capabilities. Etch rate can be controlled to within 1% of the desired value. Such control represents a significant improvement over open-loop operation of etch tools, where variances in etch rate of up to 5% can be observed during production processes due to disturbances in tool state and material properties

Gaussian Process Regression for Virtual Metrology of Plasma Etch

Plasma etch is a complex semiconductor manufacturing process in which material is removed from the surface of a silicon wafer using a gas in plasma form. As the process etch rate cannot be measured easily during or after processing, virtual metrology is employed to predict the etch rate instantly using ancillary process variables. Virtual metrology is the prediction of metrology variables using other easily accessible variables and mathematical models. This paper investigates the use of Gaussian process regression as a virtual metrology modelling technique for plasma etch data

Weighted windowed PLS models for virtual metrology of an industrial plasma etch process

Virtual metrology is the prediction of metrology variables using easily accessible process variables and mathematical models. Because metrology variables in semiconductor manufacture can be expensive and time consuming to measure, virtual metrology is beneficial as it reduces cost and throughput time. This work proposes a virtual metrology scheme that uses sliding-window models to virtually measure etch rates in an industrial plasma etch process. The windowed models use partial least squares (PLS) regression and a sample weighting scheme to combat the effects of both process drifts due to machine conditioning and process shifts due to maintenance events. An industrial data set is examined and the weighted windowed PLS models outperform global models and non-weighted windowed models

Real-time Virtual Metrology and Control of Plasma Electron Density in an Industrial Plasma Etch Chamber

Plasma etching is a semiconductor manufacturing process during which material is removed from the surface of silicon wafers using gases in plasma form. A host of chemical and electrical complexities make the etch process notoriously dicult to model and troublesome to control. This work demonstrates the use of a real-time model predictive control scheme to maintain a consistent plasma electron density in the presence of disturbances to the ground path of the chamber. The electron density is estimated in real time using a virtual metrology model based on plasma impedance measurements. Recursive least squares is used to update the controller model parameters in real time to achieve satisfactory control of electron density over a wide operating space

Global and Local Virtual Metrology Models for a Plasma Etch Process

Virtual metrology (VM) is the estimation of metrology variables that may be expensive or difficult to measure using readily available process information. This paper investigates the application of global and local VM schemes to a data set recorded from an industrial plasma etch chamber. Windowed VM models are shown to be the most accurate local VM scheme, capable of producing useful estimates of plasma etch rates over multiple chamber maintenance events and many thousands of wafers. Partial least-squares regression, artificial neural networks, and Gaussian process regression are investigated as candidate modeling techniques, with windowed Gaussian process regression models providing the most accurate results for the data set investigated

Weighted windowed PLS models for virtual metrology of an industrial plasma etch process

Virtual metrology is the prediction of metrology variables using easily accessible process variables and mathematical models. Because metrology variables in semiconductor manufacture can be expensive and time consuming to measure, virtual metrology is beneficial as it reduces cost and throughput time. This work proposes a virtual metrology scheme that uses sliding-window models to virtually measure etch rates in an industrial plasma etch process. The windowed models use partial least squares (PLS) regression and a sample weighting scheme to combat the effects of both process drifts due to machine conditioning and process shifts due to maintenance events. An industrial data set is examined and the weighted windowed PLS models outperform global models and non-weighted windowed models