Stephanie Watts Butler

Supervisory run-to-run control of polysilicon gate etch using in situ ellipsometry

Polysilicon gate etch is a critical manufacturing step in the manufacturing of MOS devices because it determines the tolerance limits on MOS circuit performance. The etch used in the current study suffers from machine aging, which causes processing results to drift with time. Performing the etch for the same time with fixed process setpoints (recipe) for all wafers would produce unsatisfactory results. Thus, an in situ ellipsometer was employed with a new run-to-run supervisory controller, termed predictor corrector control (PCC), to eliminate the impact of machine and process drift. A novel modeling technique was used to predict uniformity from the ellipsometry data collected at a single site on the wafer. Predictive models are employed by the PCC supervisory controller to generate optimal settings (recipe) for every wafer which will achieve a target mean etch rate, while maintaining a spatially uniform etch. A 200 wafer experiment was conducted to demonstrate the benefits of process control. Implementation of PCC resulted in a 36% decrease in standard deviation from target for the mean etch rate. In addition, the data indicates that controlling etch rate may improve the control and uniformity of the line width change. >

A comparison of principal component analysis, multiway principal component analysis, trilinear decomposition and parallel factor analysis for fault detection in a semiconductor etch process

Multivariate statistical process control (MSPC) tools have been developed for monitoring a Lam 9600 TCP metal etcher at Texas Instruments. These tools are used to determine if the etch process is operating normally or if a system fault has occurred. Application of these methods is complicated because the etch process data exhibit a large amount of normal systematic variation. Variations due to faults of process concern can be relatively minor in comparison. The Lam 9600 used in this study is equipped with several sensor systems including engineering variables (e.g. pressure, gas flow rates and power), spatially resolved optical emission spectroscopy (OES) of the plasma and a radio‐frequency monitoring (RFM) system to monitor the power and phase relationships of the plasma generator. A variety of analysis methods and data preprocessing techniques have been tested for their sensitivity to specific system faults. These methods have been applied to data from each of the sensor systems separately and in combination. The performance of the methods on a set of benchmark fault detection problems is presented and the strengths and weaknesses of the methods are discussed, along with the relative advantages of each of the sensor systems. Copyright

Survey Automatic control in microelectronics manufacturing: Practices, challenges, and possibilities

Advances in modeling and control will be required to meet future technical challenges in microelectronics manufacturing. The implementation of closed-loop control on key unit operations has been limited due to a dearth of suitable in situ measurements, variations in process equipment and wafer properties, limited process understanding, non-automated operational practices, and lack of trained personnel. This paper reviews the state-of-the-art for process control in semiconductor processing, and covers the key unit operations of lithography, plasma etching, thin film deposition, rapid thermal processing, and chemical-mechanical planarization. The relationship of process (equipment) models to control strategies is elaborated because recently there has been a considerable level of activity in model development in industry and academia. A proposed control framework for integrating factory control and equipment scheduling, supervisory control, feedback control, statistical process control, and fault detection/diagnosis in microelectronics manufacturing is presented and discussed.

Development and Benchmarking of Multivariate Statistical Process Control Tools for a Semiconductor Etch Process: Improving Robustness through Model Updating

Abstract Multivariate Statistical Process Control tools have been developed for monitoring and fault detection on a Lam 9600 Metal Etcher. Application of these methods is complicated because the process data exhibits large amounts of normal variation that is continuous on some time scales and discontinuous on others. Variations due to faults can be minor in comparison. Several models based on principal components analysis and variants which incorporate methods for model updating have been tested for long term robustness and sensitivity to known faults. Model performance was assessed with about six month’s worth of process data and a set of benchmark fault detection problems.

Spatial characterization of wafer state using principal component analysis of optical emission spectra in plasma etch

Optical emission spectroscopy (OES) is often used to obtain in-situ estimates of process parameters and conditions in plasma etch processes. Two barriers must be overcome to enable the use of such information for real-time process diagnosis and control. The first barrier is the large number of measurements in wide-spectrum scans, which hinders real-time processing. The second barrier is the need to understand and estimate not only process conditions, but also what is happening on the surface of wafer, particularly the spatial uniformity of the etch. This paper presents a diagnostic method that utilizes multivariable OES data collected during plasma etch to estimate spatial asymmetries in commercially available reactor technology. Key elements of this method are: first, the use of principal component analysis (PCA) for dimensionality reduction, and second, regression and function approximation to correlate observed spatial wafer information (i.e., line width reduction) with these reduced measurements. Here we compare principal component regression (PCR), partial least squares (PLS), and principal components combined with multilayer perceptron neural networks (PCA/MLP) for this in-situ estimation of spatial uniformity. This approach has been verified for a 0.35-/spl mu/m aluminum etch process using a Lam 9600 TCP etcher. Models of metal line width reduction across the wafer are constructed and compared: the root mean square prediction errors on a test set withheld from training are 0.0134 /spl mu/m for PCR, 0.014 /spl mu/m for PLS, and 0.016 /spl mu/m for PCA/MLP. These results demonstrate that in-situ spatially resolved OES in conjunction with principal component analysis and linear or nonlinear function approximation can be effective in predicting important product characteristics across the wafer.

In situ spectral ellipsometry for real-time thickness measurement : Etching multilayer stacks

A polarization modulated spectroscopic ellipsometer is used in situ to measure the thicknesses of films in real time during semiconductor plasma etch processing. Utilizing the speed of phase modulation multichannel detection, and digital signal processing techniques, this ellipsometer is capable of acquiring spectral data in less than 75 ms. Efficient algorithms were developed for determining layer thicknesses from the measured spectra of multilayer film stacks in real time with a typical solution time of a few seconds. The measured thicknesses and etch rates are used to anticipate interfaces in multiple layer stacks and control process end points. The ability of the spectral ellipsometer to measure multilayer stacks during etching is demonstrated by the etching of a stack consisting of silicon nitride, polycrystalline silicon, and silicon dioxide. Such stacks are commonly used as masks for field oxidation for electrical isolation in memory device fabrication. An isotropic plasma etch is used to remove this film in a single‐wafer process environment.

Correlation of logical failures to a suspect process step

Traditional yield enhancement efforts have long relied on memory bitmapping techniques. With the industry marching toward system-on-a-chip technology, the importance of logic products has increased exponentially. This necessitates the development of innovative techniques to perform logic yield enhancement. In this paper the authors present a novel technique that can be used to perform logic yield enhancement. The paper concentrates on logic bitmapping at Texas Instruments. Results obtained from a few production samples of a graphics processor are also presented.

Intelligent model‐based control system employing in situ ellipsometry

An intelligent model‐based control system provides a framework for the efficient application of in situ sensors to semiconductor manufacturing. This article discusses the components of such a system. The specific example presented in this article is a system utilizing in situ ellipsometry developed for a polysilicon gate etch. The ellipsometer was used for robust endpointing, process modeling, fault detection, and run‐to‐run supervisory control. The methodology for each sensor application is presented. The coordination of the various applications is also discussed and implementation issues are covered. The goal of the controller for polysilicon gate etch is to achieve a target mean etch rate, while maintaining a spatially uniform etch. This process suffers from machine aging, which degrades the etch rate and final film uniformity. Experimental results demonstrating that the controller compensates for process drift are shown. Implementation of this system resulted in a 36% decrease in standard deviation fr...

Development and Benchmarking of Multivariate Statistical Process Control Tools for a Semiconductor ETCH Process: Impact of Measurement Selection and Data Treatment on Sensitivity

Abstract Multivariate Statistical Process Control (MSPC) tools have been developed for monitoring a Lam 9600 TCP Metal Etcher at Texas Instruments. These tools are used to determine if the etch process is operating normally or if a system fault has occurred. Application of these methods is complicated because the etch process data exhibits a large amount of normal systematic variation. Variations due to faults of process concern can be relatively minor in comparison. The Lam 9600 used in this study is equipped with several sensor systems including engineering variables (e.g. pressure, gas flow rates and power), spatially resolved Optical Emission Spectroscopy (OES) of the plasma and a Radio Frequency Monitoring (RFM) system to monitor the power and phase relationships of the plasma generator. A variety of analysis methods and data preprocessing techniques have been tested for their sensitivity to specific system faults. These methods have been applied to data from each of the sensor systems separately and in combination. The performance of the methods on a set of benchmark fault detection problems will be presented and the strengths and weaknesses of the methods will be discussed, along with the relative advantages of each of the sensor systems.

Integration of in situ spectral ellipsometry with MMST machine control

The transformation of desired physical effects to process settings has been achieved through extensive use of appropriate in situ sensors for the MMST program. In situ sensors like an ellipsometer can be used to monitor film thicknesses as well as etch or growth rate in real time, and to determine endpoint time. This capability is opposed to conventional processing, which uses pilot wafers and ex situ statistical process control. In situ sensors enable real-time wafer-to-wafer compensation for equipment and process drifts and also diagnosis of equipment status for preventive maintenance. This paper discusses the use of a spectral ellipsometer (SE) developed at Texas Instruments and the issues involved in the integration of this SE to the machine control as well as to the factory-wide computer integrated manufacturing (CIM) environment for feedforward and feedback control. >