Gabriel G. Barna

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

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.

Statistical feedback control of a plasma etch process

This paper presents the methodology developed for the automatic feedback control of a silicon nitride plasma etch process. The methodology provides an augmented level of control for semiconductor manufacturing processes, to the level that the operator inputs the required process quality characteristics (e.g. etch rate and uniformity values) instead of the desired process conditions (e.g., specific RF power, pressure, gas flows). The optimal equipment settings are determined from previously generated process/equipment models. The control algorithm is driven by the in-situ measurements, using in-line sensors monitoring each wafer. The sensor data is subjected to Statistical Quality Control (SQC) to determine if deviations from the required process observable values can be attributed to noise in the system or are due to a sustained anomalous behavior of the equipment. Once a change in equipment behavior is detected, the process/equipment models are adjusted to match the new state of the equipment. The updated models are used to run subsequent wafers until a new SQC failure is observed. The algorithms developed have been implemented and tested, and are currently being used to control the etching of wafers under standard manufacturing conditions. >

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.

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.

Apparatus for optical characterization of displays

An apparatus has been designed and constructed for the measurement of the optical characteristics of passive displays. It provides isotropic illumination to the top surface of the display and generates data on the contrast ratio of individual segments as they are viewed from any direction above the display. Typical results on the brightness, contrast ratio, and the angle dependence of this contrast ratio are shown for a commercially available LCD.

MMST manufacturing technology-hardware, sensors, and processes

This paper describes the equipment and processes utilized in the Microelectronics Manufacturing Science and Technology (MMST) program. The processes were carried out in a combination of testbeds (AVP, the TI designed and built Advanced Vacuum Processor) and commercial equipment, all in the single-wafer mode. All AVP processing was performed with the wafers in an inverted, face-down, configuration. All the processing equipment was connected to a Computer-Integrated Manufacturing (CIM) system, which both collected the designated data and communicated the process parameters from the CIM database to the particular processing unit. Where available, in situ sensors were utilized for monitoring the process parameters, with measurements made on a metrology die in the center of the wafer. Many of these processes were controlled by the model-based process control algorithms in the CIM system. Otherwise, the processes were controlled by standard statistical process control (SPC) methods. This paper emphasizes the processing methodology that was developed and followed in order to operate in this CIM environment and successfully execute an approximately 150 step 0.35 /spl mu/m CMOS process in less than 72 hours. >

Automatic problem detection and documentation in a plasma etch reactor

IC manufacturing involves a large number of complex process steps. There is a probability of a misprocessing error, which may entail severe consequences, in each of these steps. The author describes two software tools, in a single-wafer plasma etcher, that have been developed to minimize the occurrence and maximize the efficiency of diagnosing misprocessing. The first examines the endpoint trace of every wafer and determines if that wafer has seen anomalous processing. If so, the software can terminate the processing of subsequent wafers. The second records the analog values of all the process control parameters during the entire etch process. When these two routines are linked appropriately, it is possible to record these analog values for only those wafers that have seen anomalous processing. This feature provides data for the analysis of the process problem. The procedures minimize the number of wafers that are misprocessed and provide pertinent diagnostic information for those wafers. >