Data collection and analysis system

• US 20060015294A1
• Filed: 07/01/2005
• Published: 01/19/2006
• Est. Priority Date: 07/07/2004
• Status: Active Grant

• Current Assignee: KLA-Tencor Corporation
• Original Assignee: KLA-Tencor Corporation
• Inventors: Parker, Jeffrey M., Renken, Wayne G., Yetter, Forrest Gilbert Jr., Pieper, John B.
• Granted Patent: US 7,363,195 B2
• Time in Patent Office: Days
• US Class Current: 702/183
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks

Abstract

A sensor network collects time-series data from a process tool and supplies the data to an analysis system where pattern analysis techniques are used to identify structures and to monitor subsequent data based on analysis instructions or a composite model. Time-series data from multiple process runs are used to form a composite model of a data structure including variation. Comparison with the composite model gives an indication of tool health. A sensor network may have distributed memory for easy configuration.

Claims

1. A method of determining a condition of a process in a substrate processing module, comprising:

obtaining a first set of time-series data from measurement of a process parameter during a first occurrence of a process;

obtaining at least a second set of time-series data from measurement of the process parameter during at least a second occurrence of the process;

identifying a structure corresponding to a first portion of the first set of time-series data, the structure selected from a finite set of structures;

identifying a first portion in each of the at least a second set of time series data that correspond to the structure;

deriving a composite model from the first portion of the first set of time-series data and the first portion of each of the at least a second set of time series data;

obtaining a third set of time-series data from measurement of the process parameter during a third occurrence of the process; and

determining a degree of correlation between a portion of the third set of time-series data and the composite model.

2. The method of claim 1 wherein the first occurrence of the process is the processing of a semiconductor wafer that is subsequently processed to form semiconductor devices.

3. The method of claim 1 wherein the first occurrence of the process is the processing of a glass substrate that is subsequently processed to form a flat panel display.

4. The method of claim 1 wherein the degree of correlation is determined from comparison of the portion of the third set of time-series data and the model, the model including the variation in the first portion of the first set of time-series data and additional sets of time-series data.

5. The method of claim 1 wherein the first set of time-series data and the at least a second set of time-series data are collected using one or more sensors mounted to a process chamber, the one or more sensors in communication with a controller over a network.

6. The method of claim 1 further comprising determining the locations of the boundaries of the portion of the third set of time-series data, the locations determined by modifying the locations to obtain good correlation with structures on both sides of each boundary.

7. The method of claim 6 wherein a composite model for a process step of variable length has a range of acceptable time between boundaries.

8. The method of claim 1 wherein identifying the structure includes selecting a polynomial having an order from a plurality of polynomials of different orders, selecting the polynomial based on goodness of fit and the order so that the polynomial selected is not the polynomial that gives the best fit.

9. The method of claim 8 wherein individual polynomials of the plurality of polynomials are shifted Legendre polynomials.

10. The method of claim 1 further comprising deriving additional composite models from the additional portions of the first set of time-series data and additional portions of each of the at least a second set of time series data and determining additional degrees of correlation between additional portions of the third set of time-series data and the additional composite models.

11. The method of claim 1 wherein the degree of correlation between the portion of the third set of time-series data and the composite model is determined by calculating a Mahalanobis distance between a first polynomial that represents the third set of time-series data and a second polynomial that represents the composite model and dividing the scalar Mahalanobis distance by the number of terms in the first polynomial, or by a value derived from the first polynomial, to obtain a scaled Mahalanobis distance that is compared to a threshold distance.

12. A data logging system for collecting and analyzing data from a processing tool, comprising:

a plurality of sensors connected by a network, a first one of the plurality of sensors mounted to the processing tool;

a controller connected to the plurality of sensors by the network; and

a data analysis unit in communication with the controller, the data analysis unit receiving teaching time-series data from the first one of the plurality of sensors, via the controller and analyzing the teaching time-series data by correlating patterns in the teaching time series data that are similar to patterns in previously collected teaching time-series data, the patterns in the teaching time-series data and the previously collected teaching time-series data selected from a finite group of patterns.

13. The data logging system of claim 12 wherein the data analysis unit calculates the goodness of fit between a pattern of the time-series data and a pattern of the teaching data.

14. The data logging system of claim 12 wherein the goodness of fit is obtained from comparing the pattern of the time-series data with a composite model of the pattern from the multiple process runs of the teaching data, the composite model indicating an acceptable range for the pattern.

15. The data logging system of claim 12 wherein the data analysis unit forms a composite model from the correlating, the composite model sent to the controller, the controller comparing the composite model to additional time-series data and deriving an indication of tool health from the correlation between the composite model and additional time-series data.

16. A method of configuring a sensor network that includes a plurality of nodes that are connected to a network hub, the sensor network collecting data for a user terminal, a node including one or more sensors and a smart transducer interface module, comprising:

each of the plurality of nodes supplying identification data to the hub, the identification data for a sensor node including the number of sensors at the sensor node and the type of each sensor at the sensor node;

assembling the identification data for the plurality of nodes at the network hub; and

downloading the assembled identification data for the plurality of nodes from the network hub to the user terminal.

17. The method of claim 16 wherein the network is a DeviceNet network and the assembled identification data is an Electronic Data Sheet embedded in a DeviceNet file object.

18. The method of claim 16 further comprising using the assembled identification data for the plurality of nodes to configure a software program on the user terminal to reflect the number and types of sensors at each sensor node.

19. The method of claim 16 wherein the identification data further includes prior application information, device description and manufacturing information.

20. The method of claim 16 wherein the hub includes stored data and wherein the assembling includes assembling data stored at the hub.

21. A method of assigning network addresses to a plurality of nodes in a sensor network, comprising:

providing each of the plurality of nodes with an electrical connector for connecting to interchangeable address modules; and

connecting an address module to the electrical connector of a node, the address module providing the network address of the node and providing a visual indicator of the network address of the node.

22. The method of claim 21 wherein an address module is connected to each of the plurality of nodes, each of the address modules being preconfigured to have different addresses.

23. The method of claim 21 wherein the address module has a configurable address, the address configured using a configuring unit that communicates with the address module using a communication system other than the network.

24. A smart transducer interface module for converting an electrical signal from one form to another, comprising:

an input that receives a first electrical signal from a sensor of a first type indicating a condition at the sensor;

an output that generates a second electrical signal that is a function of the first electrical signal, the second electrical signal being equal to the electrical signal that would be provided by a transducer of a second type experiencing the condition at the sensor.

25. The smart transducer interface module of claim 24 wherein the sensor of a first type is a thermocouple of a first type and the transducer of the second type is a thermocouple of a second type.