Shane A. Lynn

Virtual metrology for plasma etch using tool variables

This paper presents work carried out with data from an industrial plasma etch process. Etch tool parameters, available during wafer processing time, are used to predict wafer etch rate. These parameters include variables such as power, pressure, temperature, and RF measurement. A number of variable selection techniques are examined, and a novel piecewise modelling effort is discussed. The achievable accuracy and complexity trade-offs of plasma etch modelling are discussed in detail.

Estimation and Control in Semiconductor Etch: Practice and Possibilities

Semiconductor wafer etching is, to a large extent, an open-loop process with little direct feedback control. Most silicon chip manufacturers rely on the rigorous adherence to a ¿recipe¿ for the various etch processes, which have been built up based on considerable historical experience. However, residue buildup and difficulties in achieving consistent preventative maintenance operations lead to drifts and step changes in process characteristics. This paper examines the particular technical difficulties encountered in achieving consistency in the etching of semiconductor wafers and documents the range of estimation and control techniques currently available to address these difficulties. An important feature of such an assessment is the range of measurement options available if closed-loop control is to be achieved.

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.

Identifying key process characteristics and predicting etch rate from high-dimension datasets

In semiconductor manufacturing advanced process control (APC) refers to a range of techniques that can be used to improve process capability. As the dimensions of electronic devices have decreased, the application of APC has become more and more important for the critical stages of production processes. However, the economic disadvantage of employing APC is that it requires feedback information in the form of downstream metrology data, which is both time consuming and costly to obtain.

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 ?

Abstract 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 difficult 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.

Real-time virtual metrology and control of etch rate in an industrial plasma chamber

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 etch rate in the presence of disturbances to the ground path of the chamber, which are representative of maintenance events. Virtual metrology (VM) models, using plasma impedance measurements, are used to estimate the plasma etch rate in real time for control, with a view to eliminating the requirement for invasive measurements. The VM 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.