Mapping yield information of semiconductor dice

In one exemplary embodiment, yield information of semiconductor dice is mapped by obtaining yield information of a first die that was formed on a first location on a first wafer. Yield information is obtained of a second die that was formed on a second location on a second wafer. A portion of the first location corresponds to a portion of the second location such that the portion of the first location would overlap with the portion of the second location if the first location was on the second wafer. A plurality of pixel elements is defined. Each pixel element corresponds to a different location on a wafer, and at least one of the plurality of pixel elements corresponds to the portion of the first location that corresponds to the portion of the second location. An average yield is determined for the at least one of the plurality of pixel elements based on the yield information of the first die and the second die. A deviation is determined for the at least one of the plurality of pixel elements based on the average yield of the at least one of the plurality of pixel elements and the yield information of the first die and the second die.

BACKGROUND

The present application relates to semiconductor dice manufacturing, and more particularly to mapping yield information of semiconductor dice.

2. Related Art

Semiconductor devices are typically manufactured by fabricating the devices on a semiconductor wafer. An individual device is formed as a die on the wafer using known semiconductor fabrication processes. Depending on the size of the die, a single wafer can contain hundreds of dice. The dice are generally arranged in a pattern (i.e., a die placement) on the wafer to maximize the number of dice on the wafer.

After the dice are fabricated on the wafer, the dice are electrically tested. Dice that pass the electrical testing are sorted from the dice that fail the electrical testing. While semiconductor manufacturers collect yield information of the semiconductor dice, such as the number of dice that pass and fail the electrical testing, they often make limited use of the collected yield information.

SUMMARY

In one exemplary embodiment, yield information of semiconductor dice is mapped by obtaining yield information of a first die that was formed on a first location on a first wafer. Yield information is obtained of a second die that was formed on a second location on a second wafer. A portion of the first location corresponds to a portion of the second location such that the portion of the first location would overlap with the portion of the second location if the first location was on the second wafer. A plurality of pixel elements is defined. Each pixel element corresponds to a different location on a wafer, and at least one of the plurality of pixel elements corresponds to the portion of the first location that corresponds to the portion of the second location. An average yield is determined for the at least one of the plurality of pixel elements based on the yield information of the first die and the second die. A deviation is determined for the at least one of the plurality of pixel elements based on the average yield of the at least one of the plurality of pixel elements and yield information of the first die and the second die.

DETAILED DESCRIPTION

The following description sets forth numerous specific configurations, parameters, and the like. It should be recognized, however, that such description is not intended as a limitation on the scope of the present invention, but is instead provided as a description of exemplary embodiments.

With reference toFIG. 1, a plurality of semiconductor wafers102can be processed using a processing tool104to form integrated circuits as dice on wafers102. After the dice are formed on wafers102, a tester106performs one or more tests on the dice. In general, dice that fail the one or more tests are marked and discarded. Yield information, such as the number of dice that pass or fail the one or more tests, can be used to monitor, adjust, and optimize processing tool104. It should be recognized that wafers102can be processed using any number of processing tools, and tested using any number of testers.

With reference toFIG. 2, an exemplary process200is depicted of mapping yield information of dice. In step202, yield information of a first die that was formed on a first location on a first wafer is obtained. For example, with reference toFIG. 3-A, assume a plurality of dice302are formed on a wafer306. As depicted inFIG. 3-A, dice302can be arranged on locations on wafer306in accordance with a die placement304. Thus, in this example, in accordance with step202(FIG. 2), after a die is formed on wafer306, the die is tested, and yield information, such as whether the die passed the test, is obtained.

With reference toFIG. 2, in step204, yield information of a second die that was formed on a second location on a second wafer is obtained. For example, with reference toFIG. 3-B, assume a plurality of dice308are formed on a wafer312. As depicted inFIG. 3-B, dice308can be arranged on locations on wafer312in accordance with a die placement310. Thus, in this example, in accordance with step204(FIG. 2), after a die is formed on wafer312, the die is tested, and yield information, such as whether the die passed the test, is obtained.

With reference toFIGS. 3-Aand3-B, in one exemplary embodiment, a portion of the first location of the first die (such as one of the plurality of dice302formed on wafer306) corresponds to a portion of the second location of the second die (such as one of the plurality of dice308formed on wafer312) such that the portion of the first location would overlap with the portion of the second location if the first location was on the second wafer. For example, with reference toFIG. 3-C, die placement304is depicted superimposed with die placement310. As depicted inFIG. 3-C, the locations of dice302overlap with the locations of dice308. More particularly, with reference toFIG. 3-D, one die302from die placement304(FIG. 3-C) is depicted with one die308from die placement310(FIG. 3-C). As depicted inFIG. 3-D, a portion of the location of die302overlaps with a portion of the location of die308, which is depicted as location314.

In the present exemplary embodiment, as depicted inFIGS. 3-Ato3-D, the first die and the second die can have different sizes. As also depicted inFIGS. 3-Ato3-D, the first die and the second die can have different placements (e.g., die placement304is different than die placement310). Additionally, the first die and the second die can be different products produced by the same process (e.g., wafer306and wafer312can be processed using the same processing tool104(FIG. 1)).

With reference toFIG. 2, in step206, a plurality of pixel elements are defined. Each pixel element corresponds to a different location on a wafer, and at least one of the pixel elements corresponds to the portion of the first location of the first die that corresponds to the portion of the second location of the second die. For example, with reference toFIG. 4-A, a plurality of pixel elements402are depicted. As depicted inFIG. 4-A, each pixel element402corresponds to a different location on a wafer. With reference toFIG. 4-B, plurality of pixel elements402is depicted superimposed with die placement304(FIG. 3-A) and die placement310(FIG. 3-B). With reference toFIG. 4-C, a portion of plurality of pixel elements402are depicted superimposed on one die302from die placement304(FIG. 3-A) and one die308from die placement310(FIG. 3-B). As depicted inFIG. 4-C, at least one pixel element402corresponds to the portion of the location of die302that overlaps with the portion of the location of die308.

With reference toFIG. 2, in step208, an average yield for the at least one pixel element from step206is determined based on the yield information of the first die and the second die obtained in steps202and204. For example, with reference toFIG. 4-C, assume that the yield of die302is 20 percent and the yield of die308is 10 percent, then the average yield of pixel element402in the location of die302that overlaps with the location of die308is 15 percent. It should be recognized that yield information can be obtained in various forms. For example, yield information can be obtained from a semiconductor manufacturer in a publishable form, such as normalized values. For a more detailed description of transforming yield information, see U.S. patent application Ser. No. 10/803,787, titled TRANSFORMING YIELD INFORMATION OF A SEMICONDUCTOR FABRICATION PROCESS, filed Mar. 17, 2004, which is incorporated herein by reference in its entirety.

It should also be recognized that the yield information of the first die and the second die obtained in steps202and204can be transformed before determining the average yield in step208. For example, if the yield information obtained in steps202and204are not normalized, a normalization step can be included prior to determining the average yield in step208. The yield information of the first die and the second die can be normalized using a linear or nonlinear function that takes into account average yield.

In some circumstances, determining the average yield in step208based on normalized yields rather than actual yields can more accurately characterize the performance of the fabrication process. For example, assume that the yield information obtained in steps202and204for the first die and the second die are the actual yields of the first die and the second die. As a numerical example, assume that the first die is formed on 100 wafers and tested, and only 10 of the 100 dice passed, which corresponds to an actual yield of 10 percent. Assume that the second die is also formed on 100 wafers and tested, and 90 of the 100 dice passed, which corresponds to an actual yield of 90 percent. Thus, the average yield determined based on the actual yields of the first die and the second is 50 percent. Now assume that 100 of the first die are fabricated on each of the 100 wafers, and 1,100 of the 10,000 dice passed, which corresponds to an average yield for the first die of 11 percent. Thus, the normalized yield of the first die is about 91 percent. Assume that 100 of the second die are fabricated on each of the 100 wafers, and 9,200 of the 10,000 dice passed, which corresponds to an average yield for the second die of 92 percent. Thus, the normalized yield of the second die is about 98 percent. The average yield based on the normalized yields is 94.5 percent. Now assume that the first die and the second die are different products produced by the same fabrication process. If the low actual yield of the first die and the high actual yield of the second die are primarily related to the difference in pixel element402in the location of die302that overlaps with the location of die308is 15 percent. It should be recognized that yield information can be obtained in various forms. For example, yield information can be obtained from a semiconductor manufacturer in a publishable form, such as normalized values. For a more detailed description of transforming yield information, see U.S. patent application Ser. No. 10/803,787, titled TRANSFORMING YIELD INFORMATION OF A SEMICONDUCTOR FABRICATION PROCESS, filed Mar. 17, 2004, which is incorporated herein by reference in its entirety.

It should also be recognized that the yield information of the first die and the second die obtained in steps202and204can be transformed before determining the average yield in step208. For example, if the yield information obtained in steps202and204are not normalized, a normalization step can be included prior to determining the average yield in step208. The yield information of the first die and the second die can be normalized using a linear or nonlinear function that takes into account average yield.

In some circumstances, determining the average yield in step208based on normalized yields rather than actual yields can more accurately characterize the performance of the fabrication process. For example, assume that the yield information obtained in steps202and204for the first die and the second die are the actual yields of the first die and the second die. As a numerical example, assume that the first die is formed on 100 wafers and tested, and only 10 of the 100 dice passed, which corresponds to an actual yield of 10 percent. Assume that the second die is also formed on 100 wafers and tested, and 90 of the 100 dice passed, which corresponds to an actual yield of 90 percent. Thus, the average yield determined based on the actual yields of the first die and the second is 50 percent. Now assume that 100 of the first die are fabricated on each of the 100 wafers, and 1,100 of the 10,000 dice passed, which corresponds to an average yield for the first die of 11 percent. Thus, the normalized yield of the first die is about 91 percent. Assume that 100 of the second die are fabricated on each of the 100 wafers, and 9,200 of the 10,000 dice passed, which corresponds to an average yield for the second die of 92 percent. Thus, the normalized yield of the second die is about 98 percent. The average yield based on the normalized yields is 94.5 percent. Now assume that the first die and the second die are different products produced by the same fabrication process. If the low actual yield of the first die and the high actual yield of the second die are primarily related to the difference in design of the first die and the second die (e.g., the features and acceptable tolerances of the first die may be smaller in comparison to the second die), the average yield determined based on the normalized yields (i.e., 94.5 percent) is a more accurate characterization of the performance of the fabrication process than the average yield determined based on the actual yields (i.e., 50 percent).

With reference toFIG. 2, in step210, a deviation for the at least one pixel element from step206is determined based on the average yield determined in step208and the yield information of the first die and the second die obtained in steps202and204. For example, with reference toFIG. 4-C, assuming that the average yield of pixel element402is 15 percent, the yield of302is 20 percent and the yield of die308is 10 percent, then the deviation is 5 percent. Although in this example a standard deviation was used as a measure of deviation, it should be recognized that various measures of deviation or variation can be used.

In one exemplary implementation, with reference toFIG. 2, step202can be repeated to obtain yield information of a first set of dice formed on locations on the first wafer, such as dice302of die placement304(FIG. 3-A). Step204can be repeated to obtain yield information of a second set of dice formed on locations on the second wafer, such as dice308of die placement310(FIG. 3-B). In the present exemplary embodiment, as depicted inFIG. 3-C, the locations on the first wafer can correspond with locations on the second wafer such that the locations on the first wafer would overlap with locations on the second wafer if the locations on the first wafer were on the second wafer. As depicted in4-B, the plurality of pixel elements402correspond to the overlapping locations.

In the present exemplary implementation, an average yield for each of the plurality of pixel elements402is determined based on the yield information of the first set of dice (dice302of die placement304(FIG. 3-A)) and the second set of dice (dice308of die placement304(FIG. 3-B)). A deviation for each of the plurality of pixel elements402is also determined based on the yield information of the first set of dice (dice302of die placement304(FIG. 3-A)) and the second set of dice (dice308of die placement304(FIG. 3-B)).

In one exemplary implementation, with reference toFIG. 2, steps202and204can be repeated to obtain yield information of a plurality of additional sets of dice formed on locations on a plurality of additional wafers, where the locations on the plurality of additional wafers overlap. The plurality of additional sets of dice can be of different products, which can include tens, hundreds, and thousands of different products, produced using the same fabrication process. The plurality of pixel points defined in step206correspond to the overlapping locations. The average yield and the deviation for each of the plurality of pixel elements are determined based on the yield information of the plurality of additional sets of dice.

In the present exemplary implementation, the average yield for a pixel element is determined as a sum of the yield information of all the dice in a location corresponding to the pixel element divided by the number of dice in the location. The deviation for the pixel is determined as a percentage of deviation of the yield information of all the dice in the location.

With reference toFIG. 5, in the present exemplary embodiment, after average yields and deviations are determined for the plurality of pixel elements402, a location of interest is identified. The location of interest can correspond to a location on a wafer that interacts with a processing equipment used to process the wafer, such as a clamp, wafer scribe, wafer identification, nitride injector, and the like. The location of interest can correspond to a location on a wafer specified by a user. The location of interest can also correspond to a pixel element having an average yield that is less than an established limit.

After a location of interest is identified, pixel elements around the identified location of interest are grouped based on the average yield and deviation of the pixel elements. For example, pixel elements can be grouped together that have: 1) the same average yields and deviations; 2) the same average yield and deviations within ranges of values; 3) average yields within ranges of values and the same deviations; or 4) average yields and deviations within ranges of values. The grouping of pixel elements around the location of interest can be ceased when the average yields or the deviations of the pixel elements are greater than an established limit.

For example, as depicted inFIG. 5, assume that a location of interest502is identified. Assume that pixel elements around location of interest502that have average yields within ranges of values and the same deviations are grouped together. For example, assume that a first grouping of pixel elements504includes pixel elements with yields between 0 and 10 percent and a deviation of 0 percent. Assume also that a second grouping of pixel elements506includes pixel elements with yields greater than 10 and less than 20 percent and a deviation of 10 percent. In this manner, the plurality of pixel elements402can be divided into different zones by grouping pixel elements402around locations of interest.

In the present exemplary embodiment, the pixel elements can be used in determining a placement of a die. In particular, the average yield and deviation associated with the pixel elements in the location in which the die is to be place can predict the likely yield of the die in that location. For example, with reference toFIG. 6, a die602is depicted as being placed in a selected location. The predicted yield of die602at the selected location can be determined based on the average yield and deviation associated with the pixel elements in the location.

As depicted inFIG. 6, in the present exemplary embodiment, if the pixel elements in the selected location have different average yields, then the average yield associated with the lowest deviation is selected. For example, die602is depicted as being placed in a selected location with two groupings of pixel elements—one grouping with yields between 0 and 10 percent and a deviation of 0 percent, and another grouping with yields greater than 10 and less than 20 percent and a deviation of 10 percent. Thus, in the present example, the average yield selected as predicting the yield of die602is between 0 and 10 percent.

Additionally, in the present exemplary embodiment, if the pixel elements in the selected location have different average yields and the deviations of the pixel elements are the same or within a determined range, the average yields of the pixel elements are averaged. For example, assume that one grouping of pixel elements inFIG. 6has a yield of 99 percent and a deviation of 0.56, and another grouping of pixel elements has a yield of 80 percent and a deviation of 0.57. Assume also that the determined range is 0.05. Thus, in the present example, the predicted yield of die602is 89.5 percent (the average of 99 percent and 80 percent).

Although exemplary embodiments have been described, various modifications can be made without departing from the spirit and/or scope of the present invention. Therefore, the present invention should not be construed as being limited to the specific forms shown in the drawings and described above.