Data processing: generic control systems or specific application – Specific application – apparatus or process – Product assembly or manufacturing
Reexamination Certificate
1999-07-22
2002-07-23
Picard, Leo (Department: 2125)
Data processing: generic control systems or specific application
Specific application, apparatus or process
Product assembly or manufacturing
C700S051000, C702S183000
Reexamination Certificate
active
06424876
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates generally to process control, and, more particularly, to a system for performing statistical process control using normalized control charts.
2. Description of the Related Art
Statistical process control techniques are commonly used to monitor the operation of manufacturing processes, systems, or individual manufacturing tools. Commonly, various measurements related to the process being monitored are compiled and plotted on a control chart. The control chart has control limits, that, if violated, indicate an error condition requiring investigation. Certain error conditions result from special causes, such as a defective tool, operator error, material defect, etc., while other errors may indicate common causes, such as process changes or trends that may be corrected by process optimization or redesign.
Generally, the data gathered is evaluated against various rules to determine if an error condition has occurred. Although, various rules may be used, many companies have adopted the “Western Electric Rules,” originally developed by the Western Electric Company. The rules specify that an error occurs if:
Rule 1: One measurement exceeds three standard deviations from the target (i.e., 1>3&sgr;);
Rule 2: Two out of three consecutive measurements exceed two standard deviations from the target (i.e., 2/3>2&sgr;),
Rule 3: Four out of Five consecutive measurements exceed one standard deviation from the target (i.e., 4/5>&sgr;); and
Rule 4: Eight consecutive points on one side of the target.
Referring to
FIG. 1
, a graph of a typical control chart
10
is provided. The control chart may represent any number of measurements pertaining to a particular line, process, or tool, for example. On the x-axis
12
, the measurement dates are recorded. The y-axis
14
represents the measurement values. The target for the process being measured is designated by a target line
16
(e.g., 175). Upper and lower control limits
18
,
20
are also shown. In the control chart of
FIG. 1
, exceeding the control limits
18
,
20
, corresponds to a rule 1 violation, i.e., the measurement deviating from the target by more than three standard deviations. The data point
22
represents the eighth consecutive data point on the positive side of the target, resulting in a rule 4 violation. Also, the data point
24
represents the fourth data point out of five exceeding one standard deviation from the target, a rule 3 violation.
In some applications, a particular line, process, or tool may be used with various operating parameters to accomplish different tasks. For example, a tool commonly used in the manufacture of semiconductor devices is a furnace. Semiconductor wafers are baked in the furnace using different parameters to control the formation of an oxide layer, for example. Typically, for each lot of wafers, measurements are taken in different positions in the furnace (e.g., sets of top, center, bottom, left, and right measurements at front, middle, and rear positions in the furnace—5 measurements×3 positions=15 total measurements). The individual lot measurements are averaged to determine a single data point for inclusion on the control chart. Control charting is conducted on various measurable parameters, such as oxide thickness. A particular furnace may use numerous recipes (i.e., sets of operating parameters), depending on the specific desired qualities of the oxide layer being grown. The particular recipe used by the furnace may be changed frequently.
Control charting the performance of the furnace using multiple recipes is burdensome, because the targets and control limits are different for each recipe. Accordingly, measurement data is independently charted for each recipe, yielding a large number of control charts requiring review. The problem with the large number of charts is exacerbated by the fact that in a manufacturing environment, such as a semiconductor fabrication facility, there are commonly multiple furnaces being tracked and also multiple types of other tools being tracked. Collectively, the number of control charts requiring updating and review can become resource intensive.
Another problem arising from the tracking of tools using multiple recipes is that, for those recipes that are infrequently used, the control chart has limited data, and thus limited information. Also, for a tool that changes recipes frequently, it may be difficult to identify certain long-term trends. Additionally, because the data for each particular recipe is less temporally related, certain multi-sample rule violations (e.g., rules 3 and 4) may be missed.
The present invention is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above.
SUMMARY OF THE INVENTION
One aspect of the present invention is seen in a method for monitoring the performance of a manufacturing entity. Metrology data indicating an output parameter of the manufacturing entity is retrieved. The output parameter has an associated target value. The metrology data is normalized based on the target value to generate normalized performance data points. A performance rule violation is determined based on the normalized performance data.
Another aspect of the present invention is seen in a manufacturing system including a metrology tool, a first database, and a processor. The metrology tool is adapted to measure an output parameter of a manufacturing entity to generate metrology data. The output parameter has an associated target value. The first database is adapted to receive the metrology data. The processor is adapted to retrieve the metrology data from the database, normalize the metrology data based on the target value to generate normalized performance data points, and determine a performance rule violation based on the normalized performance data.
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Cusson Brian K.
Giguere Justin
Oshelski Anatasia
Sharier James
Advanced Micro Devices , Inc.
Cabrera Zoila
Picard Leo
Williams Morgan & Amerson P.C.
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