Semiconductor device manufacturing: process – With measuring or testing
Reexamination Certificate
1998-11-03
2001-10-16
Niebling, John F. (Department: 2812)
Semiconductor device manufacturing: process
With measuring or testing
C700S110000, C700S121000, C702S035000
Reexamination Certificate
active
06303394
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates generally to a defect classification methodology in a semiconductor manufacturing system and more specifically, to a defect classification methodology in a semiconductor manufacturing system that determines if a cluster pattern exists on each layer and pre-classifies the cluster pattern if a cluster pattern is detected.
2. Discussion of the Related Art
In order to remain competitive, a semiconductor manufacturer must continuously increase the performance of the semiconductor integrated circuits being manufactured and at the same time, reduce the cost of the semiconductor integrated circuits. Part of the increase in performance and the reduction in cost of the semiconductor integrated circuits is accomplished by shrinking the device dimensions and by increasing the number of circuits per unit area on an integrated circuit chip. Another part of reducing the cost of a semiconductor chip is to increase the yield. As is known in the semiconductor manufacturing art, the yield of chips (also known as die) from each wafer is not 100% because of defects during the manufacturing process. The number of good chips obtained from a wafer determines the yield. As can be appreciated, chips that must be discarded because of a defect increases the cost of the remaining usable chips.
A single semiconductor chip can require numerous process steps such as oxidation, etching, metallization and wet chemical cleaning. Some of these process steps involve placing the wafer on which the semiconductor chips are being manufactured into different tolls during the manufacturing process. The optmiszation of each of these process steps requires an understanding of a variety of chemical reactions and physical processes in order to produce high performance, high yield circuits. The ability to view and characterize the surface and interface layers of a semiconductor chip in terms of their morhology, chemical composition and distribution is an inavaiable aid to those involved in research and development, process, problem solving, and failur analysis of integrated circuits. A major part of the analysis process is to determine if defects are caused by one of the process tools, and if so, which tool caused the defects.
As the wafer is placed into different tools during the manufacturing process, each of the tools can produce different types of particles that drop onto the wafer and cause defects that have the potential to decrease the yield. In order to develop high yield semiconductor processes and to improve existing ones, it is important to identify the sources of the various particles that cause defects and then to prevent the tools from dropping these particles onto the wafer while the wafers are in the tools.
In order to be able to quickly resolve process or equipment issues in the manufacture of semiconductor products, a great deal of time, effort is being expended by semiconductor manufacturers to capture and classify silicon based defects. Once a defect is caught and properly described and classified, work can begin to resolve the cause of the defect and to eliminate the cause of the defect.
The standard procedure to capture and classify defects is to place a semiconductor wafer into a scanning tool after a selected process is finished. The scanning tool scans the surface of the semiconductor wafer and maps initial defect information to a register. The initial defect information mapped to a register includes position information. As is known in the semiconductor art, the number of defects can be large and to attempt to analyze each one of the defects would be time consuming and, in some cases, counterproductive. In order to decrease the time required to analyze defects, methods were developed to classify defects so that only one of a class of defects needed to be analyzed in order to determine the cause of all of the defects in a certain classification. Systems known as Automatic Defect Classification (ADC) systems were developed.
One such system for automatically classifying defects consists of the following methodological sequence. Gather a defect image from a review station. View the defect image and assign values to elemental descriptor terms called predicates that are general descriptors such as roundness, color, hue, graininess, etc. Assign a classification code to the defect based upon the values of all of the predicates. A typical ADC system can have 40 or more quantifiable qualities and properties that can be predicates. Each predicate can have a specified range of values and a typical predicate can have value assigned to for example, typically a value between 1 and 256. A value of 1 indicates that none of the value is present and a value of 256 indicates that the quality represented by the predicate is ideal. For example, a straight line would have a value of 1 for the predicate indicating roundness, whereas a perfect circle would have a value of 256 for the same predicate.
The classification code for each defect is determined by the system from the combination of all the predicate values assigned to the defect. The goal of the ADC system is to be able to uniquely describe all the defect types in such a manner that a single classification code can be assigned to a defect, which has been differentiated from all other defect types. The can be accomplished, for example, by a system administrator who trains an artificial intelligence system to recognize various combinations and permutations of the 40 or more predicates to assign the same classification code to the same type of defect. This would result in a highly significant statistical confidence in the probability that the defect, and all other defects of the same type or class, will always be assigned the same classification code by the ADC system. This is done by performing a “best-fit” calculation against all assigned classification codes. If the fit does not meet pre-assigned standards, the system will assign an “unknown” code, which means that the system needs further training for that device/layer/defect.
As should be appreciated, if there are a large number of defects, the process of assigning a classification code to each defect is time consuming and requires scanning and evaluation tools to be utilized to a great extent. Although most defects tend to be random point occurrences easily captured in a high field of view, occasionally defects occur over a relatively large area, which are caused by the same source. These defects can easily extend over an area of several die or even large portions of the wafer. The scan tool can easily identify these as cluster type defects, where a cluster is defined as a group of defects in which every defect within the cluster is within one radius of at least one other defect in the cluster. These cluster type defects tend to fall within certain categories, including, for example, scratch, hot spot, comet tail, under etch patch, leak spray pattern, splattering, incomplete strip, and wafer edge exclusion (WEE) flaking.
Since the scan tool, or defect management system has all the information necessary to generate maps of defects, including these global patterns, it would be very beneficial to have the system pre-classify these clusters based upon an acquired knowledge base. For example, a long, narrow string of defects in a straight or slightly curved line might be called a scratch, a large round cluster of defects might be called a hot spot, and a large found cluster of defects, which tapers into a point might be called a comet tail. The advantages of such a system that pre-classifies defects as cluster defects would be to save personnel and equipment time in that not all defects would have to be analyzed because the presumption would be that all defects in a cluster would be caused by the same source and therefore not all of the defects would need to be analyzed.
Therefore, what is needed is a system that has the ability to analyze defect maps and pre-classify clusters of defects based on factors such as shape, appearance, location
Steffan Paul J.
Yu Allen S.
Advanced Micro Devices , Inc.
Nelson H. Donald
Niebling John F.
Pompey Ron
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