Patent Application
20080033589
The foregoing and other aspects and advantages of this invention are explained and described below with reference to the accompanying drawings, in which:
The sets of tool stages 12-1 to 12-4, which include a deposit metal stage 12-1, a deposit dielectric stage 12-2, a patterning stage 12-3 and an RIE stage 12-4. Each of those stages 12-1 to 12-4 may include two or more similar tools which can process a given workpiece W. The two or more similar tools at a single stage are provided so that when one or more tools at a stage is/are otherwise occupied with processing or in need of repair another available tool at that stage can process the workpiece W without delay. The workpieces W enter the deposit metal stage 12-1 on conveyor line 11A. After processing at deposit metal stage 12-1, workpiece W moves on conveyor line 11B to dielectric deposition stage 12-2. After processing at stage 12-2, workpiece W moves on conveyor line 11C to patterning stage 12-3. After processing at stage 12-3, workpiece W moves on conveyor line 17A to RIE stage 12-4, which may include where three RIE tools A, B and C in a single stage as in
The identification of workpieces W processed by individual tools is supplied on lines 13-1 to 13-4 to the DCP 15. In particular, each of the tools in stage 12-1 is connected to send workpiece identification data on line 13-1 to the DCP 15. Each of the tools in stage 12-2 is connected to send workpiece identification data on line 13-2 to the DCP 15. Each of the tools in stage 12-3 is connected to send workpiece identification data on line 13-3 to the DCP 15. In stage 12-4 the RIE tools are connected to send workpiece identification data from line 13-4 to cable 13 to the DCP 15. There may also be some test data collected which is supplied to the DCP 15, but there is no overall test data supplied on lines 13-1 to 13-4 as to the effects of processing by each individual tool upon the overall quality of the workpiece W. There are other tests made by parametric testers at various stages in the process of manufacture such as yield data, as distinguished from functional test data provided by the functional test apparatus 17 at the end of processing.
After completion of the first cycle of processing by the four stages 12-1 to 12-4, the workpiece W is recycled along line 14A-14Y to the input line 11 to stage 12-1 and is processed there by whichever tool is available in stage 12-1 and the sequential process is repeated at stages 12-2 to 12-4 as described above. The workpiece W is recycled many times through stages 12-1 to 12-4 repeatedly for manufacture of the metal layers until all of the metal layers including the metal layer N+1 have been manufactured in accordance with
As each lot and each workpiece in the lot are processed by a tool, that tool will send data including the Wafer_id, the Lot_id, and the Tool_id via cable 13-1, 13-2, 13-3 or 13-4 from stage 12-1, 12-212-3 or 12-4 respectively and cable 13 to the Data Collection Processor (DCP) computer system 15 which comprises a general purpose computer with a computer program which, among other things, stores the tool processing data for each lot and each workpiece W therein. In summary, the data sent to the DCP computer system 15 on cables 13 comprises the identity of each lot (e.g. semiconductor wafer workpieces W) processed by each of the tools at each of the stages on the factory floor 12 and each workpiece W and the processing step(s) performed by tools at each of the stages on the factory floor 12.
At the output from the factory floor 12, the workpieces W are transported along conveyor line 14Z to the conventional functional test apparatus 17 where the workpieces W are tested for defects or parameters. The output data from the functional test apparatus 17 is supplied on cable 18 to the DCP computer system 15 which comprises a general purpose computer with a computer program which also stores the functional test data for each workpiece W in each lot of workpieces which will include the Wafer_Id; and the Lot_id.
In accordance with current manufacturing technique, there may be several tools or chambers in a multi-chamber tool in stages 12-1 to 12-4 which provide data relating to processed lots and workpieces W.
There is a logistic database computer system 16 which contains the following data: Lot_id, Wafer_Id, Tool_Id, and Process Definition ID (PD_ID) which are supplied thereto by cable 22 from the DCP computer system 15. The logistic database computer system 16 also contains the Process Definition ID (PD_ID) data which is entered into the DCP computer system. The DCP computer system gets data from tool log files. The logistic data base computer system 16 supplies the following data: LOT ID, WAFER ID; TOOL ID and the Process Definition ID (PD_ID) data on cable 23 to the Data Mining Processor 26.
Output on cable from the functional test apparatus 17 is supplied via cable 21 to the Wafer Testing Data Base computer 20 which calculates the yield data for each workpiece and each lot. The yield data sent to the Wafer Testing Data Base computer 20 comprises results of the testing of workpieces W by the functional test apparatus 17. The Wafer Testing Database computer 20 supplies yield data on cable 25 to Data Mining Processor 26.
In step AC create a frequency table of data, e.g. Lot_id, Wafer_id & Tool_id shown in Table II below.
In step AD create an object yield database, e.g. Lot_id, Wafer_id; yield parameters (VAR1, VAR2, . . . ); HOL (Health Of Line) parameters.
In step AE join the frequency table and the object yield database by PRODUCT_ID into a new table shown by Table IV.
In step AF take “Frequency” as an independent variable & take “yield” as a dependent variable
In step AG a test is made which is to determine “Do tool Frequency and the tool Yield correlate?” IN step AG, a generalized linear model is used to evaluate the correlation between all good yield and Tool Frequency in accordance with the equation as follows:
Y=a+b*X. Where: a=intercept; b=slope of the line; Y=all good Yield, and X=tool Frequency for one tool.
We test whether b is significantly different from zero, then we check to see if b is positive or negative. If b is significantly different from zero, then there is a correlation between yield and tool frequency.
If b equals zero (horizontal line) there is no variation in yield as a function of frequency so tool does not affect yield.
In step AH select tool one selects a tool. If b is negative, this means the tool is a bad tool. If b is positive, this means the tool is a good tool.
In step Al display the tool frequency analysis plot shown in
In step AJ engineering action (stop tool.) If b is negative tool is bad, stop the tool.
In step BA, data from the tools A, B, C is transmitted via cables 13, 13A, 13B, and 13C and via cable 18 to the DCP computer system 15. As a result, data for process data from tools for each one of a plurality of individual processes for a processed object is stored in a data base in the DCP 15 in
In step BB, the DCP 15 transmits the data from step BA to the logistic database processor 16 which manipulates the data collected in step BA to correlate tool combination data by PRODUCT_ID, i.e. generating a tool combination or path for each PRODUCT_ID.
In step BC logistic database processor 16 creates a “Tool Path” Table for the Lot_id & Wafer_id; and the Tool Path (Combination) of the object through the various tools on the factory floor 12. Table V below shows a table of combinations of tool paths for the factory floor 12 of
In step BD a Product Yield Data Base is created comprising PRODUCT_ID (Lot_id & Wafer_id), Yield Parameters (VAR1, VAR2, . . . ), and HOL (Health Of Line) Parameters as shown in Table VI
In step BE Join the data from TOOL_PATH in Table V and Product Yield Database by PRODUCT_ID in Table VI into a new Composite Table seen in TABLE VII below.
In step BF of
Using Statistical Procedures is any Path significantly Different?
The statistical model is called ANOVA (Analysis Of Variance) which can be used to identify group difference. If p-value<0.1, we have found that there is tool combination difference.
In step BH select tool one selects a tool. Referring to
In step BI the Yield versus Frequency data for a tool determined to be affecting yield is displayed on a computer monitor for engineering judgment. The analysis has determined that there is a dependence between Yield and the number of times the tools was used. Therefore the tool requires human intervention. The tool needs either calibration, maintenance work or repairs. Depending on the severity of the problem and the availability of other tool the engineer has to decide if and when to stop tool, in step BJ.
In
In step CA store process data from tools for each one of a plurality of individual processes for a processed object in a process database.
Processing data, meaning tool information for each individual process for the processed object, is stored in a database as indicated above.
In step CB Yield numbers for each object (product or wafer) exiting a production line are stored in a yield database, as in step AD in
In step CC statistics are built (as number of times a tool was used, combinations of tools the processed objects have seen, combinations of tool-chambers processed objects have seen) for tool sets (also known as tools that perform same or similar operations, like plating, RIE, Metal depositions, etc) that are used more than once. In this step, the system develops shared statistics for all processes performed on similar tool units with the same capabilities associating a group of numbers with each of the similar tool units.
In step CD Yield numbers are generated for each group of similar tool units based upon the above statistics.
In step CE identify the bad tool units by using the above yield numbers.
In step DA of
In step DB, store tool information for each individual process for the processed object in a processing database.
In step DC, generate statistical numbers and associated yield numbers for each set of shared tools, for all tool combinations possible, without accounting for tool sequence.
In step DD, identify tool as bad when all combinations with that tool have a depressed yield.
In
In step EB, store tool yield numbers for each exiting product in a yield data database.
In step EC, store tool or chamber information for each individual process for the processed object, in a processing data database.
In step ED, generate statistical numbers and associated yield numbers for each tool/chamber frequency.
In step EE, for each frequency calculate a yield number for each frequency.
In step EF, identify a tool/chamber as bad when yield decreases monotonically with the tool usage.
The most common definition of Yield is the ratio of the number of functional working product divided by the total number of products produced. (2 good out of 5 produced: yield is ⅖ or 0.4 or 40%) Fabricators usually employ a couple of other definitions of Yield as described next.
Yield can be defined for parameters that have numerical values and are measured during (or at the end of) processing flow. For these parameters the fabricator has determined an upper and a lower permissible value called “SPEC.” Certain parameter have only a one sided specification (short-circuit, open-circuit, etc.)
Fabricators define Health of Line (HOL) as a simple multiplication of the Yields for a determined set of parameters.
Another aspect of the method of this invention is that it is effective not only when a tool is used for the exactly same process several times in one cycle but can also be used when a tool performs different processes in a product manufacturing cycle.
While this invention has been described in terms of the above specific embodiment(s), those skilled in the art will recognize that the invention can be practiced with modifications within the spirit and scope of the appended claims, i.e. that changes can be made in form and detail, without departing from the spirit and scope of the invention. Accordingly all such changes come within the purview of the present invention and the invention encompasses the subject matter of the following claims.
