US20060259177A1 - Method and system for aggregating and combining manufacturing data for analysis - Google Patents

Abstract

A method and system for aggregating and combining manufacturing data for analysis for the purposes of increasing manufacturing efficiency and reducing manufacturing downtime due to abnormal conditions. An embodiment provides for a method of dividing an entire manufacturing process into parts and further into subparts for the purposes of tracking the path that a workpiece takes during the entire manufacturing process. Data is measured specific to the path and assigned to a data set stored on a data processing device for analysis of the conditions for the workpiece being examined. An embodiment provides for quicker data analysis which may result in less manufacturing product being discarded due to lengthy delays between abnormal conditions and the response to those conditions. An embodiment provides for users to be alerted when abnormal conditions are present. In one example, a data processing device non-manually halts production when abnormal conditions are present.

Description

CROSS REFERENCE TO RELATED APPLICATIONS
• This application is a Continuation of U.S. application Ser. No. 10/789,895, filed Feb. 27, 2004, which is incorporated herein be reference.
• This patent application is related to U.S. patent application Ser. Number 10/786,678, filed Feb. 25, 2004, entitled METHOD AND SYSTEM FOR CORRELATING AND COMBINING PRODUCTION AND NON-PRODUCTION DATA FOR ANALYSIS, assigned to Micron Technology, Inc., and incorporated herein by reference.
TECHNICAL FIELD
• The disclosed embodiments generally relate to integrated circuit manufacturing, including methods for reducing integrated circuit manufacturing abnormalities, and for aggregating and combining data from integrated circuit manufacturing processes for data analysis.
BACKGROUND
• Defects in semiconductor manufacturing processes result in lost production and fabrication downtime. The cause of these defective products is hard to determine as today's manufacturing processes involve not only a single manufacturing operation but multiple instances of the same operation performed by different machines and a multitude of other processes and machines. Identifying the cause of the error is in itself time-consuming and the large amounts of data to examine makes quick and accurate analysis difficult.
• During the semiconductor manufacturing process the unprocessed wafer proceeds through several distinct manufacturing processes. Measurements are taken during this process and analyzed. If the analysis shows that a problem is occurring the process is stopped and the condition resolved before production is allowed to resume. These processes can become very complicated and data analysis may not be able to be completed and studied until well after the time that the particular wafer that had been processed has been completely processed by the faulty process.
• Further compounding the lag time between the measurement and the analysis and then the subsequent reaction is that for a single process, a number of machines may perform that same exact process. The number of measurements taken during this manufacturing process expands linearly and data analysis is no longer cumbersome, it is impossible. Anomalies in the processes are not identified quickly enough and entire lots of end-product may be defective as a result.
• The problem is that there is no easy way to combine the data and reduce the data processing time so meaningful data analysis can take place and reaction to current conditions can take place quicker. What is needed is a way to aggregate processes and resulting data for the purposes of analysis.
BRIEF DESCRIPTION OF THE DRAWINGS
• FIG. 1 is a pictorial representation of an exemplary fabrication facility with fabricating processes contained therein.
• FIG. 2 is a pictorial representation of a scenario of an embodiment of the present invention.
• FIG. 3 is a flowchart illustrating generally a method according to an embodiment of the present invention.
• FIG. 4 is a pictorial representation of a scenario of an embodiment of the present invention.
• FIG. 5 is a flowchart illustrating generally a method according to an embodiment of the present invention.
• FIG. 6 is a flowchart illustrating generally a method according to an embodiment of the present invention.
• FIG. 7 is a block diagram illustrating generally, among other things, one example of portions of a data analysis system, and an environment with which it is used, for processing and analyzing the data acquired from the manufacturing process.
• FIG. 8 is a pictorial representation of an exemplary fabrication facility with fabricating processes contained therein.
DETAILED DESCRIPTION OF THE INVENTION
• In the following detailed description of exemplary embodiments of the invention, reference is made to the accompanying drawings (where like numbers designate like elements), which form a part hereof, and in which is shown by way of illustration specific exemplary embodiments in which the invention may be practices. Those embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that other embodiments may be utilized and logical, mechanical, electrical and other changes may be made without departing from the scope of the present invention. In the description, numerous specific details are set forth to provide a thorough understanding of the invention. However, it is understood that the invention may be practiced without those specific details. In other instances, well-known circuits, structures and techniques have not been shown in detail in order not to obscure the invention.
• Parts of the description may be presented in terms of operations performed through the execution of programming instructions. As well understood by those skilled in the art, those operations may take the form of electrical, magnetic, or optical signals capable of being stored, transferred, combined, and otherwise manipulated through, for example, electrical components.
• The term substrate is understood to include semiconductor wafers. The term substrate is also used to refer to semiconductor structures during processing, and may include other layers that have been fabricated thereupon. Both wafer and substrate include doped and undoped semiconductors, epitaxial semiconductor layers supported by a base semiconductor or insulator, as well as other semiconductor structures well known to one skilled in the art. The term conductor is understood to include semiconductors, and the term insulator or dielectric is defined to include any material that is less electrically conductive than the materials referred to as conductors. In embodiments of the present invention, the term workpiece includes substrate or wafer or integrated circuits or other electronic devices.
• The term lot is understood to include a subset of the entirety of fabricated workpieces. A lot may further be considered as that quantity of product produced under similar conditions, at a similar establishment, over some period of time.
• The terms operation, machine, process, and chamber are used in the present application to describe various abstractions of the fabrication process. In an embodiment, an operation is a top level abstraction and may include subdivisions, such as, machines, processes, chambers, etc. It is to be understood that an operation may also be subdivided into further operations, according to an embodiment of the present operation for other abstracted analysis of data. In a further embodiment of the present invention, top-level operations may be further aggregated, up and including, considering an entire manufacturing facility as a single operation. In an embodiment, a machine is a mid-level abstraction and may include subdivisions, such as, processes, chambers, etc. In an embodiment, the term machine is not to be taken in a limiting sense as a single physical machine, but in a more broader sense as a collection of processes, chambers, other machines, etc. In an embodiment, a machine may denote the aggregation of multiple machines, including the subdivisions, if any, of such machines. In an embodiment, a process is a low-level abstraction and may include subdivisions, such as, chambers. In an embodiment, a process is a mid-level abstraction and may include subdivisions, such as, machines, chambers, etc. In an embodiment, a process may denote the aggregation of multiple processes, including the subdivisions, if any, of such processes. In an embodiment, a chamber is a low-level abstraction and typically will not include further subdivisions. In an embodiment, a chamber may denote the aggregation of multiple chambers and may include further subdivisions. Though the terms operation, machine, process and chamber are used to represent a hierarchical linking between manufacturing steps, this is not limiting. It is to be understood that an operation may be a single machine, an aggregation of machines, a single process, an aggregation of processes, a single chamber, an aggregation of chambers, or any combination. Further, any of the other steps, such as a process, may be performed by any combination of the other steps. For example, an operation may include wafer handling unit chambers which perform a process on a wafer. It will be understood by those skilled in the art, that any subdivision of manufacturing steps can be used by embodiments of the present invention, and that mere use of other terms to denote manufacturing steps does not depart from the scope of the present invention
• FIG. 1 depicts a pictorial representation of a simplified manufacturing process for workpieces. Workpieces, in an embodiment, include integrated circuits. Workpieces forprocessing105 enter the manufacturing process and are processed, in this simple example, by threeseparate operations110,120, and130. It is recognized that though the term operation is used, the term operation may include any number of machines, chambers, processes, etc. Further, the operation may include a single machine, chamber, process, etc. Conditions in the operations performing the processing are very important to the end quality of thefinished product106. In addition, the conditions of themanufacturing facility100 may also impact the quality of theend product106. In an embodiment, a manufacturing facility includes an integrated circuit fabrication building, wafer handling unit, vertical furnace machines, computer component assembly facilities, etc. It is recognized that each of these manufacturing operations may further be subdivided. These subdivisions represent the technique of using multiple machines, chambers, processes, etc. to perform the same operation. In110, this is exemplified by the four individual machines,Machine A₁111,Machine A₂112,Machine A₃113, andMachine A₄114. Each of these machines performs a substantially similar operation to respective workpiece being processed in the respective machine. However, data relating to onemachine111,112,113 or114 is not relevant to the other machines. For example, data in regards toMachine A₃113 for example, would be of no relevance to a workpiece that was processed byMachine A₁111. Capturing data forMachine A₃113 and including that data in an analysis of conditions for workpieces processed byMachine A₁111 carried out by adata processor140 would represent a computational cost that prohibits the real time analysis of data relating tomachine A₃113 and further prohibits accurate, real time analysis of theentire operation110. This results in workpiece lots progressing further down the operational chain and when the data finally yields results that an operator or system can react to, resources have been wasted on the lots in further processing. In an extreme case, this may lead to scrapping entire lots of workpieces. Though the operation at110 is subdivided into machines, it is recognized that there are other subdivisions possible, including subdividing an operation into other operations, chambers, machines, processes, etc.
• Data may also be measured on the operations at120 and130. These operations may further be subdivided. In an embodiment, this subdivision is similar to the subdivision presented inOperation A110, as described above. In an embodiment, this subdivision is performed by some other logical arrangement, such as, consecutive non-similar machines being subdivided by the possible route that a workpiece may take through the operation. In such an arrangement, data is acquired for workpieces depending on the route the workpieces took, and stored in a data set relevant to the workpiece being examined, for further analysis by adata processor140. Data concerningOperation C130 is acquired for workpieces being processed according to the operation subdivisions contemplated forOperation C130. Data acquisition for a particular production lot of workpieces is only performed from sources that relate to the processing that the particular lot of workpieces underwent. This reduces the amount of extraneous data being collected and stored and allows for quicker data analysis. This has the added benefit of providing for quicker response to conditions present in the fabrication operations.
• Measurements may be taken on theworkpiece105 as well as conditions of theactual manufacturing operations110,120,130. These measurements can be called production data. The production data is from sources that are directly related to the manufacturing process being performed. These sources include, but are not limited to, test probe data, parametric data, film thickness data, and critical dimension data. In an embodiment, a particular production data sample is gathered once per lot, i.e., production lot data. In an embodiment, a particular production data sample is gathered multiple times per lot. In an embodiment, a particular data sample is applied across multiple production lots. Though this detailed description uses the term production data to refer to these data measurements, this is not to be taken in a limiting sense, as any data that relates directly to the manufacturing process being performed is considered to be production data, regardless of what it is actually called. Further, production data may be further defined as being either online or offline. Online data may be data which is measured directly on the workpiece being manufactured and may be things such as the temperature of the manufactured workpiece, or its thickness. Online data may also be data measured from the manufacturing process in question while the workpiece is being processed. Offline data is that data that, though directly related to the manufacturing process, is not measured on the actual manufactured workpiece or during the actual manufacturing step, such as the operating temperature of the machine, the operating pressure, critical dimensions data on a finished workpiece, or some other measurement.
• The pictorial element labeled100 represents the entire facility in which the manufacturing process resides. Measurements may be conducted on the entire facility, as well. These measurements can be called non-production data or alternatively, facility data. The non-production data is from sources not directly related to the manufacturing process. These sources include, but are not limited to, atmospheric conditions, water conditions, gas conditions, chemical conditions, exhaust pressure, and electrical conditions. In an embodiment, a particular sample is gathered from one location by one sensor. In an embodiment, a particular sample is gathered from multiple locations by multiple sensors. Alternatively, these measurements may be called facility data as they generally, but without limitation, relate to the facility in which the production takes place. Though this detailed description uses the term non-production data, or facility data, to refer to these data measurements, this is not to be taken in a limiting sense, as any data that does not relate directly to the workpiece can be considered to be non-production data, or facility data, regardless of what it is actually called. This data is input into adata processor140 for further analysis.
• FIG. 2 is a pictorial representation of an exemplary combination of multiple chambers in asingle fabrication machine200 according to an embodiment of the present invention. A workpiece for processing proceeds left to right in this example, being first processed byProcess A210, thenProcess B220, and finally Process C,230. Each of the constituent process may have multiple chambers for performing the similar process. In this example,Process A210 is accomplished by one of three chambers,Chamber A₁211,Chamber A₂212 orChamber A₃213. In this example,Process B220 is accomplished by one chamber,Chamber B221. In this example,Process C230 is accomplished by one of two chambers,Chamber C₁231 orChamber C₂232. The number of possible routes, or groups, throughMachine Y200 can be given by the equation:
  G_n=N_a*N_b*N_c
• where, G_nis the number of possible routes, or groups, throughMachine Y200, N_ais the number of chambers performingProcess A210, N_bis the number of chambers performingProcess B220, and N_cis the number of chambers performingProcess C230. In the example given byFIG. 2, the number of possible routes, or groups, is 6. These groups are as follows:
• • Group Y₁: A₁-B-C₁
  • Group Y₂: A₁-B-C₂
  • Group Y₃: A₂-B-C₁
  • Group Y₄: A₂-B-C₂
  • Group Y₅: A₃-B-C₁
  • Group Y₆: A₃-B-C₂
• In an embodiment,Process B220 has two chambers performing the same process. The number of possible routes, or groups, throughMachine Y200 can be given by the equation:
  G_n=N_a*N_b*N_c
  where, G_nis the number of possible routes, or groups, throughMachine Y200, N_ais the number of chambers performingProcess A210, N_bis the number of chambers performingProcess B220, in thiscase 2, and N_cis the number of chambers performingProcess C230. In this embodiment the number of possible routes, or groups, throughMachine Y200 is 12.
• Providing for the general case, a multi-chambered machine, Machine Z, has several chambers doing a specific process (process-1, process-2, . . . , process-n). Some of those chambers accomplish the same process. Consider the machine, Machine Z, that does m different processes in a lot of different possible routes:$\mathrm{Process}\text{-}1\text{:}\mathrm{Machine}Z\mathrm{has}{\mathrm{<figure-callout\; label="Z"\; filenames="US20060259177A1-20061116-D00000.png,US20060259177A1-20061116-D00004.png"\; state="\{\{state\}\}">Z</figure-callout>}}_1\mathrm{chambers}$$\mathrm{Process}\text{-}2\text{:}\mathrm{Machine}Z\mathrm{has}{\mathrm{<figure-callout\; label="Z"\; filenames="US20060259177A1-20061116-D00000.png,US20060259177A1-20061116-D00001.png"\; state="\{\{state\}\}">Z</figure-callout>}}_2\mathrm{chambers}$$⋮$$\mathrm{Process}\text{-}m\text{:}\mathrm{Machine}Z\mathrm{has}{Z}_n\mathrm{chambers}$
  Thus, the number of possible combinations, or groups, for the whole process pattern is (Z₁*Z₂* . . . * Z_n) in this machine.
• FIG. 3 presents, at a high level, a flowchart of the method, according to an embodiment of the present invention for the handling of data from a machine, e.g.,Machine Y200 as depicted inFIG. 2.Process A210, has three data sources, data fromChamber A₁311, data fromChamber A₂312, and data fromChamber A₃313.Process B220, has one data source, data fromChamber B321.Process C230, has two data sources, data fromChamber C₁331, and data fromChamber C₂332. These data sources include, but are not limited to, test probe data, parametric data, film thickness data, critical dimension data, DC tester data, inline measurement tool data, surface characteristics data, etc. Moreover, data related to any chamber is measured after a chamber in the operation. In an embodiment, data is measured relative to a chamber after a workpiece leaves the chamber and prior to subsequent process. Data from Chambers A₁-A₃, Chamber B, and Chambers C₁-C₂are grouped at340 according to the possible routes a workpiece may take through the entire machine,Machine Y200. According to which one of these routes a particular lot of workpieces undertook, the proper data grouping is assigned to that lot at350. Based on the proper data grouping, specific measured data can be assigned to the data set corresponding to the particular lot of workpieces being examined. In an embodiment this measured data can include, but not be limited to, test probe data, parametric data, film thickness data, critical dimension data, DC tester data, inline measurement tool data, surface characteristics data, etc. Following proper grouping of the data and assigning that data grouping to the lot of workpieces at350, the data is analyzed360. In an embodiment, theanalysis360 is a statistical analysis of the data. In an embodiment, theanalysis360 is a trend analysis. In an embodiment, thisanalysis360 can be compared to expected conditions. Any current manufacturing conditions that depart from the expected conditions can trigger a warning. In an embodiment, the warning can be an audible warning. In an embodiment, the warning can be a message sent to communications devices. In an embodiment, the warning can be a message sent over a Wide Area Network to a user or computer system. In an embodiment, a departure from expected conditions can trigger a non-manual shut down of all fabrication machines in the groups being currently examined. It is to be recognized that only with quicker, accurate and morefocused data analysis360 can timely and appropriate reactions to current conditions be made.
• FIG. 4 is a pictorial representation of a two step process according to an embodiment of the present invention. In this example, a workpiece undergoes processing byOperation A410 and then undergoes processing byOperation B420. Operation A410 can be performed by three different machines,Machine A₁411,Machine A₂412, orMachine A₃413.Operation B420 can be performed by four different machines,Machine B₁421,Machine B₂422,Machine B₃423, orMachine B₄424. In an embodiment of the present invention a combination of these operations can be treated as a single operation such that, instead of analyzingOperation A410, thenOperation B420,Operation A-B400 can be analyzed singly. In order to treat a plurality of operations as a single operation or machine, the route of the workpiece undergoing processing must be provided. Specifically, the multiple routes are each assigned a group. As described herein, the number of groups in this example is 12, given by the equation:
  G_i=N_a*N_b
  where, G_nis the number of possible routes, or groups, throughOperation A-B400, N_ais the number of machines performingOperation A410, and N_bis the number of machines performingOperation B420. In this embodiment the number of possible routes, or groups, ofOperation A-B400 is 12. Here the possible groups are:$\mathrm{Group}1\text{:}{A}_1-{\mathrm{<figure-callout\; label="B"\; filenames="US20060259177A1-20061116-D00000.png,US20060259177A1-20061116-D00004.png"\; state="\{\{state\}\}">B</figure-callout>}}_1$$\mathrm{Group}2\text{:}{A}_1-{\mathrm{<figure-callout\; label="B"\; filenames="US20060259177A1-20061116-D00000.png,US20060259177A1-20061116-D00001.png"\; state="\{\{state\}\}">B</figure-callout>}}_2$$\mathrm{Group}3\text{:}{A}_1-{\mathrm{<figure-callout\; label="B"\; filenames="US20060259177A1-20061116-D00000.png,US20060259177A1-20061116-D00001.png"\; state="\{\{state\}\}">B</figure-callout>}}_3$$\mathrm{Group}4\text{:}{A}_2-{\mathrm{<figure-callout\; label="B"\; filenames="US20060259177A1-20061116-D00000.png,US20060259177A1-20061116-D00004.png"\; state="\{\{state\}\}">B</figure-callout>}}_1$$⋮$$\mathrm{Group}12\text{:}{A}_3-{\mathrm{<figure-callout\; label="B"\; filenames="US20060259177A1-20061116-D00005.png"\; state="\{\{state\}\}">B</figure-callout>}}_4$
  Further, in the case such as provided inFIG. 4 the number of possible routes is the mathematical product of the number of possible individual machines performing an operation and the number of possible machines performing the other operation. However, in the general case with more then two operations being performed by multiple machines, a simple mathematical product is not sufficient. Given a multiple number of operations with a number of machines performing such operation, Operation 1 (O₁) with N₁machines, Operation 2 (O₂) with N₂machines, Operation 3 (O₃) with N₃machines, through to Operation t (O_t) with N_tmachines. The number of possible groups through O₁and O₂, can be expressed as the product of N₁and N₂. The number of the combinations of two random operations from all possible operations, t operations can be expressed as:
  C={t*(t−1)}/2!
  where C is the number of combinations and t is the number of operations. The number of groups is given by the sum of the groups for all the combinations, as given by the equation:
  X=(G_{1 & 2}+G_{1 & 3}+ . . . +G_{1 & t})+(G_{2 & 3}+G_{2 & 4}+ . . . +G_{2 & t})+ . . . +(G(_{t−1)& t})
  and
  X=(N₁*N₂+N₁*N₃+ . . . +N₁*N_t)+(N₂*N₃+N₂*N₄+ . . . +N₂*N_t)+ . . . +(N_t−1*N_t)
  where G_(t−1)&tis a representation of the number of combinations of machines between operation (t−1) and operation t, N_tis the number of machines performing a particular operation t and X is the number of groups for all combinations.
• Generally, the number of combinations of random n operations is given by:
  Y=tC_n
  where Y is the total number of combinations, t is the number of operations and C_nis the number of individual machines performing a particular step. If the average of the number of possible routes among the operations is assumed to be G, generally, the sum of the number of possible routes in all t operations is given by:
  Y=Σ_n=1^ttC_n*G
  where Y is the total number of combinations, t is the number of operations, G is the number of possible routes and C_nis the number of individual machines performing a particular step in the process
• FIG. 5 presents, at a high level, a flowchart of the method, according to an embodiment of the present invention for the handling of data from a combined operation,Operation A-B400 as depicted inFIG. 4.Operation A410, has three data sources, data fromMachine A₁511, data fromMachine A₂512, and data fromMachine A₃513.Operation B420 has four data sources, data fromMachine B₁521, data fromMachine B₂522, data fromMachine B₃523, and data fromMachine B₄524. These data sources include, but are not limited to, test probe data, parametric data, film thickness data, critical dimension data, DC tester data, inline measurement tool data, surface characteristics data, etc. This data is grouped at540 according to the possible routes a workpiece may take through the combined operation. According to which one of these routes, or groups, a particular lot of workpieces undertook, a proper data grouping is assigned to that lot at550. Based on the proper data grouping, specific measured data can be assigned to the data set corresponding to the particular lot of workpieces being examined. In an embodiment this measured data can include, but not be limited to, test probe data, parametric data, film thickness data, critical dimension data, DC tester data, inline measurement tool data, surface characteristics data, etc. Following proper grouping of the data and assigning that data grouping to the lot of workpieces at550, the data is analyzed560. In an embodiment, the analysis is a statistical analysis of the data. In an embodiment, theanalysis560 is a trend analysis. In an embodiment, thisanalysis560 can be compared to expected conditions. Any current manufacturing conditions that depart from the expected conditions can trigger a warning. In an embodiment, the warning can be an audible warning. In an embodiment, the warning can be a message sent to communications devices. In an embodiment, the warning can be a message sent over a Wide Area Network to a user or computer system. In an embodiment, a departure from expected conditions can trigger a non-manual shut down of all fabrication machines in the groups being currently examined. It is to be recognized that only with quicker, accurate and morefocused data analysis560 can timely and appropriate reactions to current conditions be made.
• In an embodiment of the present invention, the data from each sorted group can be graphed and reported automatically. In an embodiment of the present invention, specific specifications can be established for each report, including without limitation, graphs, charts, data. Further, the results and graphs can be automatically provided to the engineers responsible for the production of the workpieces being processed. With quicker data analysis by grouping or combining processes, engineers can quickly respond to conditions affecting the performance of the manufacturing process and reduce the amount of wasted material.
• FIG. 6 presents, at a high level, a flowchart of the method, according to an embodiment of the present invention for the collection and analysis of data on a particular lot of workpieces from production sources and non-production sources. Thedata sources300,500,605, represent an exemplary set of data sources to be analyzed. It is recognized that though three data sources are represented, any number of data sources may be present. These data sources may be from production sources and from non-production sources.Data source300 represents data from a group assigned to a particular lot of workpieces as contemplated byFIG. 2, and processed according to the method embodied inFIG. 3.Data source500 represents data from a group assigned to a particular lot of workpieces as contemplated byFIG. 4, and processed according to the method embodied inFIG. 5.Data source605 represents any of the other variety of data sources available in a manufacturing facility. In an embodiment of the present invention,data source605 represents a non-production data source. These sources include, but are not limited to, atmospheric conditions, water conditions, gas conditions, chemical conditions, exhaust pressure, and electrical conditions. In an embodiment, a particular sample is gathered from one location by one sensor. In an embodiment, a particular sample is gathered from multiple locations by multiple sensors. Alternatively, these measurements may be called facility data as they generally, but without limitation, relate to the facility in which the production takes place. Though this detailed description uses the term non-production data, or facility data, to refer to these data measurements, this is not to be taken in a limiting sense, as any data that does not relate directly to the manufacturing process can be considered to be non-production data, or facility data, regardless of what it is actually called. In the case that multiple data points are collected and assigned to a particular lot of workpieces, acalculation610 may be performed to further combine those data points into a single data point. In an embodiment, thiscalculation610 may be a weighted mean calculation, weighting the data for time, location, etc. The data can then be keyed611 to some unique value allowing for non-manual relation of the threedata sources650. Following the relation of the data at650, the data can be quickly analyzed at660. In an embodiment, thisanalysis660 is a statistical analysis. In an embodiment, thisanalysis660 is a trend analysis. In an embodiment of the present invention, thisanalysis660 is very focused on the particular lot of workpieces being considered. By not including data from operations, machines, processes, chambers and the like that did not affect the lot of workpieces being considered, the data set associated with that lot of workpieces being considered can be smaller, representing a computational cost saving, and allowing forquicker data analysis660. Aquicker data analysis660 further provides for quicker reaction to current manufacturing conditions.
• FIG. 7 depicts a block diagram of a system for implementing an embodiment of the invention analogous to thedata processor140 shown inFIG. 1. Illustrated are aserver701 connected to acomputer702 via anetwork710. Although oneserver701, onecomputer702, and onenetwork710 are shown, in other embodiments any number or combination of them may be present. Although theserver701 and thenetwork710 are shown, in another embodiment they may not be present.
• Thecomputer702 may include aprocessor730, astorage device740, aninput device760, and anoutput device750, all connected via abus770.
• Theprocessor730 may represent a central processing unit of any type of architecture, such as a CISC (Complex Instruction Set Computing), RISC (Reduced Instruction Set Computing), VLIW (Very Long Instruction Word), or a hybrid architecture, although any appropriate processor may be used. Theprocessor730 may execute instructions and may include that portion of thecomputer702 that controls the operation of the entire computer. Although not depicted inFIG. 7, theprocessor730 typically includes a control unit that organizes data and program storage in memory and transfers data and other information between the various parts of thecomputer702. Theprocessor730 may receive data from theinput device760, may read and store code and data in thestorage device740, may send data to theoutput device750, and may send and receive code and/or data to/from thenetwork710.
• Although thecomputer702 is shown to contain only asingle processor730 and asingle bus770, the present invention applies equally to computers that may have multiple processors and to computers that may have multiple buses with some or all performing different functions in different ways.
• Thestorage device740 represents one or more mechanisms for storing data. For example, thestorage device740 may include read only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, and/or other machine-readable media. In other embodiments, any appropriate type of storage device may be used. Although only onestorage device740 is shown, multiple storage devices and multiple types of storage devices may be present. Further, although thecomputer702 is drawn to contain thestorage device740, it may be distributed across other computers, for example onserver701.
• Thestorage device740 includes acontroller745, which in an embodiment may include instructions capable of being executed on theprocessor730 to carry out the functions of the present invention. In another embodiment, some or all of the functions of the present invention may be carried out via hardware in lieu of a processor-based system. Although thecontroller745 is shown to be contained within thestorage device740 in thecomputer702, some or all of thecontroller745 may be distributed across other systems, for example on theserver701 and accessed via thenetwork710.
• Theinput device750 may be a keyboard, pointing device, mouse, trackball, touchpad, touchscreen, keypad, microphone, voice recognition device, or any other appropriate mechanism for the user to input data to thecomputer702. Although only oneinput device760 is shown, in another embodiment any number and type of input devices may be present.
• Theoutput device750 is that part of thecomputer702 that communicates output to the user. Theoutput device750 may be a cathode-ray tube (CRT) based video display well known in the art of computer hardware. But, in other embodiments theoutput device750 may be replaced with a liquid crystal display (LCD) based or gas, plasma-based, flat-panel display. In another embodiment, theoutput device750 may be a speaker. In still other embodiments, any appropriate output device suitable for presenting data may be used. Although only oneoutput device750 is shown, in other embodiments, any number of output devices of different types or of the same type may be present.
• Thebus770 may represent one or more busses, e.g., PCI, ISA (Industry Standard Architecture), X-Bus, EISA (Extended Industry Standard Architecture), or any other appropriate bus and/or bridge (also called a bus controller).
• Thecomputer702 may be implemented using any suitable hardware and/or software, such as a personal computer or other electronic computing device. Portable computers, laptop or notebook computers, PDAs (Personal Digital Assistants), two-way alphanumeric pagers, keypads, portable telephones, appliances with a computing unit, pocket computers, and mainframe computers are examples of other possible configurations of thecomputer702. The hardware and software depicted inFIG. 7 may vary for specific applications and may include more or fewer elements than those depicted. For example, other peripheral devices such as audio adapters, or chip programming devices, such as EPROM (Erasable Programmable Read-Only Memory) programming devices may be used in addition to or in place of the hardware already depicted.
• Thenetwork710 may be any suitable network and may support any appropriate protocol suitable for communication between theserver701 and thecomputer702. In an embodiment, thenetwork710 may support wireless communications. In another embodiment, thenetwork710 may support hard-wired communications, such as a telephone line or cable. In another embodiment, thenetwork710 may support the Ethernet IEEE (Institute of Electrical and Electronics Engineers) 802.3x specification. In another embodiment, thenetwork710 may be the Internet and may support IP (Internet Protocol). In another embodiment, thenetwork710 may be a local area network (LAN) or a wide area network (WAN). In another embodiment, thenetwork710 may be a hotspot service provider network. In another embodiment, thenetwork710 may be an intranet. In another embodiment, thenetwork710 may be a GPRS (General Packet Radio Service) network. In another embodiment, thenetwork710 may be any appropriate cellular data network or cell-based radio network technology. In another embodiment, thenetwork710 may be an IEEE 802.11x wireless network. In still another embodiment, thenetwork710 may be any suitable network or combination of networks. Although onenetwork710 is shown, in other embodiments any number of networks (of the same or different types) may be present.
• As was described in detail above, aspects of an embodiment pertain to specific apparatus and method elements implementable on a computer or other electronic device. In another embodiment, the invention may be implemented as a program product for use with an electronic device. The programs defining the functions of this embodiment may be delivered to an electronic device via a variety of signal-bearing media, which include, but are not limited to:
• (1) information permanently stored on a non-rewriteable storage medium, e.g., a read-only memory device attached to or within an electronic device, such as a CD-ROM readable by a CD-ROM drive;
• (2) alterable information stored on a rewriteable storage medium, e.g., a hard disk drive or diskette; or
• (3) information conveyed to an electronic device by a communications medium, such as through a computer or a telephone network, including wireless communications. Such as the information from theoperations110,120,130 ofFIG. 1 to thedata processor140 ofFIG. 1 via thenetwork710 ofFIG. 1, further depicted and described above and inFIG. 7.
• Such signal-bearing media, when carrying machine-readable instructions that direct the functions of the present invention, represent embodiments of the present invention.
• FIG. 8 depicts a pictorial representation of a simplified manufacturing process for workpieces, according to an embodiment of the present invention. A workpiece for processing105 is processed by theoperation710 and a resultingend product106 is produced. Data is measured on theoperation710 that is applicable to the workpiece being processed107. This data is transmitted over a network to a data processor for storage, analysis and examination. According to an embodiment of the present invention, theoperation710 can be the specific route that theworkpiece107 took through theentire manufacturing facility100. This has the benefit of only storing measured data that is applicable to the route that a workpiece took in a data set for that workpiece. It is to be recognized that a manufacturing facility represents hundreds of processes, fabrication machines, operations, manufacturing chambers and the like. Storing data from these various data sources and performing non-specific and non-focused data analysis meant to yield some information in regards to a particular lot of workpieces is an extremely large computational cost. This cost is manifested in large delays of time between measurement of the data and response to the conditions that are being measured. In extreme conditions this may result insubstandard product106 leaving themanufacturing facility100. In an embodiment, it could also result in workpieces proceeding too far along the manufacturing line from where the workpieces encountered out of specification conditions. This could mean that the workpieces have proceeded past a point that can not be recovered from, resulting in wholesale scrapping of workpieces. An analysis on a focused, specific data set corresponding to a particular lot of workpieces can reduce the likelihood of such a result, as operators can react quickly to out of specification conditions. In an embodiment, a system non-manually reacts to out of specification conditions.
• There are distinct advantages for this combination and aggregation as described herein. It allows for quicker more focused data analysis of conditions that directly affect the quality of the produced workpieces. It reduces the amount of extraneous data in a data set that do not directly relate to a particular production lot of workpieces or group. Quicker analysis also provides for quicker reaction, allowing operators and engineers to quickly respond to conditions, address the problems, and reduce the amount of product being affected by those conditions.

Claims (42)

1. A system for analyzing an electronics fabrication process, comprising:

a processor; and

a storage device coupled to the processor that is configured to store a set of instructions for performing a method that is executed by the processor, the method further comprising:

determining fabrication routes for workpieces subjected to the fabrication process;

acquiring production data for the fabrication process;

identifying a route for a selected workpiece;

identifying production data corresponding to the selected workpiece;

subjecting the identified production data to an analytical process;

determining if the identified production data conforms to an expected condition; and

if the identified production data does not conform to the expected condition, generating a warning condition.

2. The system ofclaim 1, wherein determining fabrication routes for workpieces subjected to the fabrication process further comprises identifying one or more linked operations in the fabrication process.

3. The system ofclaim 2, wherein identifying one or more linked operations in the fabrication process further comprises identifying a physical location of the linked operation in the fabrication process.

4. The system ofclaim 2, wherein identifying one or more linked operations in the fabrication process further comprises identifying a machine performing the linked operation in the fabrication process.

5. The system ofclaim 2, wherein identifying one or more linked operations in the fabrication process further comprises identifying a processing time associated with the linked operation in the fabrication process.

6. The system ofclaim 1, wherein identifying a route for a selected workpiece further comprises identifying the route as a subset of operations in the fabrication process.

7. The system ofclaim 6, wherein identifying the route as a subset of operations in the fabrication process further comprises identifying a subset of operations having a similar end product.

8. The system ofclaim 1, wherein acquiring production data for the fabrication process further comprises measuring the production data using at least one sensor operably coupled to the processor and positioned in selected locations within the fabrication process.

9. The system ofclaim 1, wherein subjecting the identified production data to an analytical process further comprises subjecting the identified production data to one of a statistical analysis, and a trend analysis.

10. The system ofclaim 1, wherein generating a warning condition further comprises generating an audible alarm.

11. The system ofclaim 1, wherein generating a warning condition further comprises triggering a shut down of the fabrication process.

12. A system for analyzing an electronics fabrication process, comprising:

a processor; and

a storage device coupled to the processor that is configured to store a set of instructions for performing a method that is executed by the processor, the method further comprising:

determining fabrication routes for workpieces subjected to the fabrication process;

acquiring production data for the fabrication process;

for a selected workpiece, determining a fabrication route for the workpiece;

collecting production data corresponding to the selected workpiece;

processing the collected data in an analytical process to determine if the processed data conforms to an expected condition; and

if the identified production data does not conform to the expected condition, generating a warning condition.

13. The system ofclaim 12, wherein acquiring production data for the fabrication process further comprises measuring the production data using at least one sensor that is positioned in selected locations within the fabrication process.

14. The system ofclaim 12, wherein acquiring production data for the fabrication process further comprises collecting at least one of test probe data, parametric data, film thickness data and critical dimension data.

15. The system ofclaim 12, wherein determining a fabrication route for the workpiece further comprises determining one or more linked operations in the fabrication route.

16. The system ofclaim 15, wherein determining one or more linked operations in the fabrication route further comprises identifying at least one of a physical location of the operation, a machine performing the operation in the fabrication process, and a processing time associated with the operation in the fabrication process.

17. The system ofclaim 12, wherein determining a fabrication route for the workpiece

further comprises identifying the route as a subset of operations in the fabrication process.

18. The system ofclaim 17, wherein identifying the route as a subset of operations in the fabrication process further comprises identifying a subset of operations having a similar end product.

19. The system ofclaim 12, wherein processing the collected data in an analytical process to determine if the processed data conforms to an expected condition further comprises performing one of a statistical analysis and a trend analysis on the collected data.

20. The system ofclaim 12, wherein processing the collected data in an analytical process to determine if the processed data conforms to an expected condition further comprises forming a weighted mean value and comparing the weighted mean value to a selected key value.

21. The system ofclaim 12, further comprising at least one output device coupled to the processor, and wherein generating a warning condition further comprises transferring the warning condition to the at least one output device.

22. The system ofclaim 21, wherein the at least one output device comprises a loudspeaker, and wherein transferring the warning condition to the at least one output device further comprises transferring an audible alarm to the loudspeaker.

23. The system ofclaim 21, wherein the at least one output device comprises a visual display, and wherein transferring the warning condition to the at least one output device further comprises displaying a text message corresponding to the warning condition on the visual display.

24. A system for analyzing an electronics fabrication process, comprising:

a computer; and

a communications network coupled to the computer;

wherein the computer includes a processor that is configured to execute a set of instructions for performing a method, the method further comprising:

determining fabrication routes for workpieces subjected to the fabrication process;

acquiring production data for the fabrication process, and for a selected workpiece, determining a fabrication route for the workpiece;

collecting production data corresponding to the selected workpiece;

processing the collected data in an analytical process to determine if the processed data conforms to an expected condition; and

if the identified production data does not conform to the expected condition, generating a warning condition.

25. The system ofclaim 24, wherein the computer further comprises a storage device, and wherein the method further comprises storing the set of instructions on the storage device.

26. The system ofclaim 25, wherein the storage device further comprises at least one of a read only memory (ROM), a random access memory (RAM), a magnetic disk storage media, an optical storage media, and a flash memory device.

27. The system ofclaim 24, wherein the method further comprises communicating at least a portion of the set of instructions to the computer using the communications network.

28. The system ofclaim 24, wherein the method further comprises communicating at least a portion of the production data to the computer using the communications network.

29. The system ofclaim 24, further comprising at least one server coupled to the communications network that is operable to communicate at least a portion of the set of instructions to the computer using the communications network.

30. The system ofclaim 24, further comprising at least one server coupled to the communications network that is operable to communicate at least a portion of the production data to the computer using the communications network.

31. The system ofclaim 24, wherein acquiring production data for the fabrication process further comprises measuring the production data using at least one sensor positioned in selected locations within the fabrication process.

32. The system ofclaim 24, wherein acquiring production data for the fabrication process further comprises collecting at least one of test probe data, parametric data, film thickness data and critical dimension data.

33. The system ofclaim 24, wherein processing the collected data in an analytical process further comprises performing one of a statistical analysis and a trend analysis on the collected data.

34. The system ofclaim 24, wherein processing the collected data in an analytical process to determine if the processed data conforms to an expected condition further comprises forming a weighted mean value and comparing the weighted mean value to a selected key value.

35. The system ofclaim 24, wherein determining a fabrication route for the workpiece further comprises determining one or more linked operations in the fabrication route.

36. The system ofclaim 35, wherein identifying one or more linked operations in the fabrication route further comprises identifying at least one of a physical location of the operation, a machine performing the operation in the fabrication process, and a processing time associated with the operation in the fabrication process.

37. The system ofclaim 24, wherein determining a fabrication route for the workpiece further comprises identifying the route as a subset of operations in the fabrication process.

38. The system ofclaim 37, wherein identifying the route as a subset of operations in the fabrication process further comprises identifying a subset of operations having a similar end product.

39. The system ofclaim 24, further comprising at least one output device coupled to the communications network, and wherein generating a warning condition further comprises communicating the warning condition to the at least one output device.

40. The system ofclaim 39, wherein the at least one output device comprises a loudspeaker, and wherein communicating the warning condition to the at least one output device further comprises transferring an audible alarm to the loudspeaker.

41. The system ofclaim 39, wherein the at least one output device comprises a visual display, and wherein communicating the warning condition to the at least one output device further comprises displaying a text message corresponding to the warning condition on the visual display.

42. The system ofclaim 24, wherein generating a warning condition further comprises triggering a shut down of the fabrication process.

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