Industry News, Trends and Technology, and Standards Updates

The Cimetrix Resource Center is a great way to familiarize yourself with standards within the industry as well as find out about new and exciting technologies. 

Our resource center features information about equipment connectivity and control, data gathering, GEM (SECS/GEM), EDA/Interface A, and more. These standards are among the key enabling technologies for the Smart Manufacturing and Industry 4.0 global initiatives that are having a major impact on the electronics assembly, semiconductor, SMT and other industries. Manufacturers and their equipment suppliers must be able to connect equipment and other data sources, gather and analyze data in real time, and optimize production through a wide variety of applications.

The many presentations featured in our resource center provide in-depth coverage from Cimetrix expert's presentations at many different conferences and expos around the world. Some of our most popular presentations are below.

• Getting the Most from the GEM Standard
• Addressing Connectivity Challenges of Disparate Data Sources in Smart Manufacturing
• Standards-based Multi-Source Data Collection in the Smart Manufacturing Era
• Smarter Manufacturing through Equipment Data-Driven Application Design

Be sure to stop by our Resource Center any time or download the presentations directly from the links in this posting.

In the EDA Best Practices blog series, we have discussed choosing a commercial software platform, using that package to differentiate your data collection capabilities and how to choose what types of data to publish. In this post we will review why you should choose to provide an E164-compliant equipment model.

What is E164?

Equipment Data Acquisition (EDA) - also referred to as Interface A - offers semiconductor manufacturers the ability to collect a significant amount of data that is crucial to the manufacturing process. This data is represented on the equipment as a model, which is communicated to EDA clients as metadata sets. The metadata, based upon the SEMI E125 Specification for Equipment Self-Description, includes the equipment components, events, and exceptions, along with all the available data parameters.

Since the advent of the SEMI EDA standards, developers and fabs have recognized that equipment models, and the resulting metadata sets, can vary greatly. It is possible to create vastly different models for similar pieces of equipment and have both models be compliant with the EDA standards. This makes it difficult for the factories to know where to find the data they are interested in from one type of equipment to another.

Recognizing this issue, the early adopters of the EDA standards launched an initiative in to make the transition to EDA easier and ensure consistency of equipment models and metadata from equipment to equipment. This effort resulted in the E164 EDA Common Metadata standard, approved in July 2012. Another part of this initiative was the development of the Metadata Conformance Analyzer (MCA), which is a utility that tests conformance to this standard. With this specification, equipment modeling is more clearly defined and provides more consistent models between equipment suppliers. This makes it easier for EDA/Interface A users to navigate models and find the data they need.

Power of E164

The E164 standard requires strict name enforcement for events called out in the GEM300 SEMI standards. It also requires that all state machines contain all of the transitions and in the right order as those called out in the GEM300 standards. This includes state machines in E90 for substrate locations and in E157 for process management. The states and transition names in these state machines must match the names specified in the GEM300 standards.

These requirements may seem unnecessarily strict, but implementing the common metadata standard results in:

• Consistent implementations of GEM300
• Commonality across equipment types
• Automation of many data collection processes
• Less work to interpret collected data
• Ability for true “plug and play” applications
• Major increases in application software engineering efficiency

Knowing that a model is E164 compliant allows EDA client applications to easily and programmatically define data collection plans knowing that the compliant models must provide all of the specified data with the specified names. For example, the following application is able to track carrier arrival and slotmap information as well as movement of material through a piece of equipment and process data for that equipment.

This application will work for any GEM300 equipment that is E164 compliant. The client application developer can confidently create data collection plans for these state machines, knowing that an E164-compliant model must provide the needed state machines and data with the proscribed names.

Decide to be E164 compliant

A number of leading semiconductor manufacturers around the globe have seen the power of requiring their equipment suppliers to provide EDA/E164 on their equipment, and now require it in their purchase specifications.

If you are a semiconductor manufacturer, you should seriously consider doing the same because it will greatly simplify data collection from the equipment (and most of your candidate suppliers probably have an implementation available or underway.

If you are an equipment supplier and your factory customers have not required that your EDA models be E164 compliant, you should still seriously consider providing this capability anyway as a way to differentiate your equipment. Moveover, E164-compliant models are fully compliant with all other EDA standards. Finally, it is much easier and more cost effective to create E164-compliant models from the outset than it is to create non-compliant models and then convert to E164 when the factory requires it.

Conclusion

The purpose of the E164 specification is to encourage companies developing EDA/Interface A connections to implement a more common representation of equipment metadata. By following the E164 standard, equipment suppliers and factories can establish greater consistency from equipment to equipment and from factory to factory. That consistency will make it easier and faster for equipment suppliers to provide a consistent EDA interface, and for factories to develop EDA client applications.

The Cimetrix Resource Center is a great way to familiarize yourself with standards within the industry as well as find out about new and exciting technologies.

Our resource center features information about equipment connectivity and control, data gathering, GEM (SECS/GEM), EDA/Interface A, and more. These standards are among the key enabling technologies for the Smart Manufacturing and Industry 4.0 global initiatives that are having a major impact on the electronics assembly, semiconductor, SMT and other industries. Manufacturers and their equipment suppliers must be able to connect equipment and other data sources, gather and analyze data in real time, and optimize production through a wide variety of applications. The videos and video series featured in our resource center provide in-depth coverage of some of these concepts.  Some of our featured videos are below.

• Smart Manufacturing Series by Ranjan Chatterjee
• What Happens in a Gigafactory Minute
• Cimetrix at IPC Apex 2019
• The Big Picture: EDA Overview and Mechanics

Be sure to stop by our Resource Center any time or watch the videos directly from the links in this posting.

The old adage “You get what you pay for” doesn’t fully apply to equipment automation interfaces… more accurately, you get what you require, and then what you pay for!

This is especially true when considering the range of capability that may be provided with an equipment supplier’s implementation of the EDA (Equipment Data Acquisition, also known as Interface A) standards. Not only is it possible to be fully compliant with the standard while delivering an equipment metadata model that contains very little useful information, the standards themselves are also silent on the topics of Performance and Data Quality.  So you must take extra care to state these requirements and expectations in your purchase specifications if you expect the resulting interface to support the demands of your factory’s data analysis and control applications. Moreover, to the extent these requirements can be tested, you should describe the test methods and tools that you will use in the acceptance process to minimize the chance of ugly surprises when the equipment is delivered.

We have covered the importance of and process for creating robust purchase specifications in a previous posting. This post will focus specifically on aspects of Performance and Data Quality within that context.

Scope of Performance and Data Quality Requirements

From a scope standpoint, Performance and Data Quality requirements are found in a number of sections in an automation specification. The list below is just a starting point suitable for any advanced wafer fab – your needs may extend and exceed these significantly.

Here are some sample requirements that pertain to the computing platform for the EDA interface software:

• The interface computer should have the capability of a 4-core Intel i5 or i7 or better, with processing speed of 2+ GHz, 8 GB of RAM, and 500 GB of persistent storage with at least 50% available at all times.
• The equipment must monitor key performance parameters of the EDA computing platform such as CPU utilization (%), memory utilization (GB, %), disk utilization (GB, %) and access rate, etc. using system utilities such as Perfmon (for Windows systems) and store this history either in a log file or in some part of the equipment metadata model.
• The network interface card must support 1 GB per second (or faster) communications.

In the area of equipment model content, the following requirements are directly related to interface performance and data quality:

• The equipment should make the EDA computing platform performance parameters available as parameters of an E120 logical element that represents the EDA interface software itself.
• The supplier must provide a written description of the update rates, recommended sampling intervals, normal operating ranges and behaviors, and high/low/rate-of-change limits for all key process parameters. These will be used to design data quality filters in the data path between the equipment and the consuming applications/users.
• Equipment parameters provided through the EDA interface must exhibit a number of data quality characteristics, including, but not limited to: an internal sampling/update rate sufficient to represent the underlying signal accurately; timing of trace reports that is consistent with the sampling interval within +/- 1.0%; values in adjacent trace reports must contain then-current values at the specified sampling interval; and rejection of obvious outliers.

Advanced users of the EDA standards are now raising their expectations for the equipment to provide self-monitoring and diagnosis capability in the form of built-in data collection plans (DCPs), as expressed in some of the following requirements:

• The supplier must provide built-in DCPs to support common equipment performance monitoring, diagnostic, and maintenance processes that are well known to the supplier. Documentation for these DCPs must define their purpose, activation conditions, interface bandwidth consumed, and the types of analysis the collected data enables.
• The supplier must describe the operating conditions that can lead to a PerformanceWarning situation for the EDA interface.
• The supplier must describe the algorithms used to deactivate DCPs under PerformanceWarning conditions. These might include LIFO (i.e., the last DCP activated is the first to be deactivated), decreasing order of bandwidth consumed or “size” (in terms of total # of parameters and # of trace/event requests), etc.

Because of the power and complexity of the DCP structure defined in the EDA standards, it is not sufficient to specify the raw communications performance requirement as a small number of isolated criteria, such as total bandwidth (in parameters per second) or minimum sampling interval. Rather, since the EDA interface must support a variety of data collection client demands for a wide range of production equipment, these requirements should be expressed as combinations of sampling interval, # parameters per DCP, # of simultaneously active DCPs, group size, buffering interval, response time for ad hoc “one-shot” DCPs, maximum latency of event generation after the related equipment condition occurred, consistency of timestamps in trace reports with the specified sampling interval, and perhaps others.

Moreover, some equipment types may have more stringent performance requirements than others, depending on the criticality of timely data for the consuming applications… so there may be process-specific performance requirements as well.

Measurement and Testing

Methods for measuring and testing the above requirements should also be described in the purchase specifications so the equipment suppliers can know they are being successfully addressed during the development process and can demonstrate compliance before and after shipping the equipment. Clarity at this phase saves time and expense later on.

Examples of such requirements include:

• The supplier must test the EDA interface across the full range of performance criteria specified above and provide reports documenting the results.
• An earlier requirement states that the EDA interface must be capable of reporting at least 2000 parameters at a sampling interval of 0.1 seconds (10Hz) with a group size of 1, for a total data collection capacity (bandwidth) of 20,000 parameters per second. In addition to this overall bandwidth capability, the supplier must demonstrate that this performance is possible over a range of specific data collection deployment strategies, meaning different #s and sizes of DCPs, different sampling intervals, group sizes, etc. without causing the EDA interface to reach one of its “Performance Warning” states or overstress its computing platform. To this end, all combinations of the following data collection configuration settings must be run for at least 15 seconds each; assuming the equipment has n processing modules:
  • Trace intervals (in seconds): 1, 0.5, 0.2, 0.1 (and 0.05 if possible)
  • # of parameters per DCP: 10, 50, 100, 250, 500, 1000 (and 2000 if possible)
  • # of DCPs: 1, 2, 3, … to n
  • Group size: 10, 5, 2, 1
• The test client should be run on a separate computing platform with sufficient computing power to “stay ahead” of the EDA interface computer; in other words, the EDA interface should never have to wait on the client system.
• Test reports should indicate the start and stop time of each iteration (i.e., one combination of the above settings), and verify that the timestamps of the data collection reports sent by the EDA interface are within +/- 1% of the value expected if the samples were collected exactly at the specified trace interval.

Performance parameters of the EDA interface platform should also be monitored during the tests and included in the report. These parameters should include memory usage, CPU processing load, and disk access rate (and perhaps others) for all processes that constitute the EDA interface software.

This approach is shown in tabular form for a 2-chamber tool (see below); since Group Size does not (or should not) impact the effective parameters per second rate, it is not shown in the table.

• A summary report for all performance tests that show acceptable message generation and transmission timing across the full range of data collection test criteria must be available.
• Detailed SOAP logs for specific performance tests must be available on request.

In Conclusion

We hope you now have some appreciation for the importance of solid requirements in this area, and can accurately assess how well your current purchase specifications express your actual needs. If you want to know more about a well-defined process for improving your specifications, or have any other questions regarding the status and outlook of the EDA standards, and how they can be implemented, please contact us.

The Cimetrix Resource Center is a great tool for anyone who wants to learn more about industry standards including Equipment Connectivity and Control, data gathering, GEM (SECS/GEM), EDA/Interface A, and more. These standards are among the key enabling technologies for the Smart Manufacturing and Industry 4.0 global initiatives that are having a major impact on many industries. Manufacturers and their equipment suppliers must be able to connect equipment and other data sources, gather and analyze data in real time, and optimize production through a wide variety of applications. The free eBooks listed below provide in-depth coverage of the some of these concepts.  They have been written by technical experts who have participated in and led the standards development processes for more than two decades.

• Features and Benefits of GEM
• Implementing Equipment Control Applications using CCF
• EDA Applications and Benefits for Smart Manufacturing
• Models in Smart Manufacturing

Be sure to stop by our Resource Center any time or download the white papers directly from the links in this posting.

The Cimetrix Resource Center is a great tool for anyone who wants to learn more about industry standards including GEM (SECS/GEM), GEM300, EDA/Interface A, and more. These standards are among the key enabling technologies for the Smart Manufacturing and Industry 4.0 global initiatives that are having a major impact on many industries. Manufacturers and their equipment suppliers must be able to connect equipment and other data sources, gather and analyze data in real time, and optimize production through a wide variety of applications. The free white papers listed below provide in-depth coverage of the most broadly used equipment connectivity standards. They have been written by technical experts who have participated in and led the standards development process for more than two decades.

• Introduction to the Standards: GEM (SECS/GEM) – SEMI E5, E30, E37
• Introduction to the Standards: GEM300 – E40, E87, E90, E94, E116, E148, E157
• Introduction to the Standards: EDA/Interface A – E120, E125, E132, E134, E164 (and others)
• Introduction to the Standards: PV2 (PVECI)
• Introduction to the Standards: Implementing GEM and PV2
• SECS Messaging Primer

Be sure to stop by our Resource Center any time or download the white papers directly from the links in this posting.

Previous blog posts have discussed the merits of choosing a commercial software platform for implementing the equipment side of EDA (Equipment Data Acquisition) and how you would use that package to differentiate your equipment data collection capabilities from your competitors.

In this post, we discuss how to design the equipment model to contain enough information to make it useful without publishing so much data that it becomes cumbersome for your factory customers to find the data that is most important to them.

Data to Publish

• E164 (EDA Common Metadata) Values

The automation requirements for the most advanced fabs call for the latest versions (Freeze II) of all the standards in the EDA suite, including the EDA Common Metadata (SEMI E164) standard. In addition to providing an excellent foundation for a new equipment model, E164 enables consistent implementation of GEM300, commonality across equipment types, automation of many data collection processes, less work to interpret collected data, and true plug-and-play client applications—all of which contribute to major increases in engineering efficiency. These capabilities benefit both the equipment suppliers and their factory customers alike. Therefore, equipment models should make all E164-compliant data available.

To summarize, those who remember the complexity of implementing SECS-II before GEM came along (pre-1992) will understand this analogy: E164 is to EDA what GEM was to SECS-II.

• Fab-specified Data

The second blog post made the following statement:

“In effect, the metadata model IS the data collection 'contract' between the equipment supplier and the fab customer."

“This is why the most advanced fabs have been far more explicit in their automation purchase specifications with respect to equipment model content, going so far as to specify the level of detailed information they want to collect about process performance, equipment behavior, internal control parameters, setpoints and real-time response of common mechanisms.”

You only have to read the latest requirements specs for these fabs to get more specifics. Pick the one from your customer base that sets the bar highest and let that be your target.

Data to Avoid in the Model

It is easy to fall into the mindset that if publishing some data through the EDA interface is desirable, the more data we can publish, the better. This is not always the case. In his fascinating book, The Paradox of Choice, Barry Schwartz makes the case that freedom is defined by one’s ability to choose, but more choice doesn’t mean more freedom. In fact, too many choices actually cripple one’s ability to choose. The same can be said of data published in an EDA interface. Making too much data available actually hinders the creation of EDA client applications.

We were recently working with a fab to perform a proof-of-concept where we connected an EDA client to a piece of equipment with an EDA interface. We were able to connect to the equipment in a matter of minutes, but finding suitable data to collect for our proof-of-concept took almost an hour because there was so much superfluous data published from the equipment.

Publishing everything including the kitchen sink reduces the ability to create an efficient EDA client application.

Some examples of data to avoid publishing in the model include:

• Parameters that have no value – If a parameter is available in the model, but the value is not published by the equipment control application, that parameter is just extra noise in the interface. Consider not adding it to the model.
• Parameters with values that do not change – If a parameter value does not change during the life of the application, it does not make sense to collect that parameter’s data. For example, if an application uses an equipment constant, it may not be necessary to publish that constant through the EDA model.
• Irrelevant data – If a parameter contains data that is irrelevant to data publication, it should not be added to the model. For example, having parameters in the model that contain the IP address or port number for connection are not very useful in the equipment model. This information is necessary in connecting with an EDA client, but is not relevant for data collection in the model.

The takeaway: Publish data required by E164 and additional fab-specified data, but carefully evaluate other data to be published to make sure it is relevant and useful for data collection.

If you have questions about Equipment Data Acquisition or would like a demo of the functionality described above, please contact Cimetrix to schedule a discussion. 

You can download an introduction to EDA White Paper any time.

In the first blog of this series, Clare Liu of Cimetrix China made the compelling case for choosing a commercial software platform for implementing the equipment side of the EDA (Equipment Data Acquisition) standards interface rather than developing the entire solution in-house. 

Whenever this “make vs. buy” decision is discussed, however, the following question inevitably arises: “If we choose a standard product for this, how can we differentiate the capabilities of our equipment and its data collection capability from our competitors?” It’s a great question which deserves a well-reasoned answer.

Platform Choice and System Architecture

Most advanced fabs use EDA to feed their on-line FDC (Fault Detection and Classification) applications, which are now considered “mission-critical.” This means if the FDC application is down for any reason, the equipment is considered down as well. It is therefore important to choose a computing platform for the EDA interface that is highly reliable and has enough processing “headroom” to support the high bandwidth requirements of these demanding, on-line production applications. Moreover, this platform should not be shared by other equipment communications, control, or support functions, since these may adversely impact the processing power available for the EDA interface. 

Surprisingly, this approach is not universally adopted, and has been a source of problems for some suppliers, so it is an area of potential differentiation. 

Adherence to Latest Standards 

The automation requirements for the most advanced fabs call for the latest versions (Freeze II) of all the standards in the EDA suite, including the EDA Common Metadata (E164) standard. Dealing with older versions of the standard in the factory systems creates unnecessary work and complexity for the fab’s automation staff, so it is best to implement the latest versions from the outset. The Cimetrix CIMPortal Plus product makes this a straightforward process using the model development and configuration tools in its SDK (Software Development Kit), so there is absolutely no cost penalty for providing the latest generation of standards in your interface.

It takes time and effort for equipment suppliers with older versions of the standards to upgrade their existing implementations, so this, too, is an opportunity for differentiation.

Equipment Metadata Model Content

This is probably the area with the largest potential for competitive differentiation, because it dictates what a factory customer will ultimately be able to do with the interface. If an equipment component, parameter, event, or exception condition is not represented in the equipment model as implemented in the E120 (Common Equipment Model) and E125 (Equipment Self-Description), and E164 (EDA Common Metadata) standards, the data related to that element cannot be collected. In effect, the metadata model IS the data collection “contract” between the equipment supplier and the fab customer.

This is why the most advanced fabs have been far more explicit in their automation purchase specifications with respect to equipment model content, going so far as to specify the level of detailed information they want to collect about process performance, equipment behavior, internal control parameters, setpoints and real-time response of common mechanisms like material handling, vacuum system performance, power generation, consumables usage, and the like. This level of visibility into equipment operation is becoming increasingly important to achieve the required yield and productivity KPIs (Key Performance Indicators) for fab at all technology nodes.

The argument about “who owns this level of information about equipment behavior” notwithstanding, providing the detailed information the fabs want in a structure that makes it easy to find and access is a true source of differentiation.

Self-Monitoring Capability

If you really want to set your equipment apart from your competitors, consider going well beyond simply providing access to the level of information needed to monitor equipment and process behavior and include “built-in” Data Collection Plans (DCPs) that save your customers the effort of figuring out what data should be collected and analyzed to accomplish this. Your product and reliability engineering teams probably already know what the most prevalent failure mechanisms are and how to catch them before they cause a problem… why not provide this knowledge in a form that makes it easy to deploy?

A few visionary suppliers are starting to talk about “self-diagnosing” and “self-healing" equipment… but it will be a small and exclusive group for a while – join them.

Readiness for Factory Acceptance

Before the fab’s automation team can fully integrate a new piece of equipment, it must follow a rigorous acceptance process that includes a comprehensive set of interface tests for standards compliance, performance, and reliability. This process is vital because solid data collection capability is fundamental for rapid process qualification and yield ramp that shorten a new factory’s “time to money.” If you know what acceptance tests and related software tools the fab will use (which is now explicit in the latest EDA purchase specifications), you can purchase the same software tools, perform and document the results of these same tests before shipping the equipment. 

This will undoubtedly speed up the acceptance process, and your customers will thank you for the effort you took to put yourself in their shoes. Incidentally, this usually means the final invoice for the equipment will be paid sooner, which is always a good thing.

In Conclusion

In this posting, we have only scratched the surface regarding the sources of competitive differentiation. As you can see, choosing a commercial platform enables this far more readily than the in-house alternative, because it allows your development team to focus on the topics above rather than worrying about compliance to the standards. If you’d like to know more, please give us a call or click below to talk schedule a meeting. 

Clare Liu, a Cimetrix Solutions Engineer, goes over the pros and cons of choosing a commercial product for EDA/Interface A vs. building a solution from scratch. Read it now in Chinese, or below in English. 

本文的焦点是当许多半导体设备制造商面对他们那些最先进的客户提出的自动化需求时，如何在购买支持EDA（Interface-A）标准的软件产品，或者自主开发之间做出决策。

鉴于我本人在今年初加入Cimtrix之前，曾经在一家半导体装备公司里担任EDA标准实现项目的主要开发人员，我想解释说明一下选择商业解决方案的利与弊。

• 利

1. 经验

在半导体制造设备上实现EDA要求软件开发人员具有半导体行业标准（SEMI）和半导体设备的经验。这对大多数设备供应商来说是非常困难的。即使他们已经拥有良好的软件开发人员,经验丰富的工厂自动化工程师和一个完整的硬件设计团队,他们还是需要有效的共同协作,找出如何设计一个结构良好的设备模型(SEMIE120 CEM 通用设备模型规范)并将设备所有的变量、时间和报警映射到设备模型的各个节点上（SEMI E125 EqSD设备自我描述规范）。 一个商业的EDA解决方案能够同时为OEM提供这些知识，并且可以基于该设备，提供EDA开发过程的指导方针。

2. 验收

简单地实现EDA接口功能和正确有效地实现的结果是不一样的。我从中得到的教训之一是，我们花了几乎整整一年的时间来实现EDA Freeze I的各种功能，并为测试的需要开发了客户端软件。然而，当我们将我们的EDA解决方案发布给客户工厂时，他们使用权威的第三方测试软件产品对所有设备的EDA解决方案进行了验证。我们的实现最初没有通过验收，因为我们对EDA标准的理解与客户的理解有些差异。为此我们花了很长时间来逐一解决验收中遇到的问题。商业的EDA解决方案通常已经在许多工厂得到了验证，因此更加标准化。

3. 时机

一个商业的EDA解决方案可以帮助OEM在短时间内开发出合格的EDA接口。自主开发EDA会给本已紧张的交付进度增加时间压力，如果需求来自一个新客户，第一个支持EDA标准的设备供应商通常会更有优势。在业务方面，EDA功能很有可能是获得订单的关键。在技术方面，第一个EDA的使用会成为整个Fab的范例，可以被用来制定其他设备在生产环境中必须满足的操作要求。

4．服务

使用商业EDA解决方案通常包括来自软件供应商的良好的技术支持，这些技术支持可能包含在最初的许可证费用中，或者是单独的技术支持合同。这意味着OEM公司不需要维持一个专门的软件团队来维护和解决遇到的软件问题。相反，他们可以依靠更专业的支持团队，而不用担心任何内部开发人员离开公司所带来的影响。

5. 知识更新

由于很多改进得到认同，还有很多新的技术在关键产品中的使用变得可行，半导体行业的EDA标准每年都在发生变化，在写这篇文章的时候，一个新的EDA标准冻结版本Freeze III正在投票中。商业EDA解决方案通常会紧紧追随标准的发展，同时会不断根据其他工厂用户的请求增加新的功能。这使得OEM能够快速、可靠地响应客户的最新需求。

• 弊

1.成本

OEM必须为商业软件的许可证，以及可能的、每年的技术支持支付费用。

2.知识产权(IP)

一些OEM公司为了对EDA功能的源代码有完全的控制权和所有权，他们选择自主开发并拥有这些软件，其原因是大多数商业软件包通常不会为基本许可证的使用者提供源代码。

3.修复错误的时间

如果在商业软件包中发现错误，设备工程师甚至工厂客户可能需要帮助软件供应商找到根本原因。他们还必须等待供应商修复并发布新版本的软件。这对于使用者来说非常不方便。

如果您的公司正面临这样的决定，请联系我们——我们很乐意分享我们的专业知识和市场知识，并协助您做出明智的决定。

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The focus of this blog posting is the decision that many semiconductor manufacturing equipment suppliers face when deciding how to address the automation requirements of their most advanced customers, namely, whether or not to buy a commercial software package that supports the SEMI Equipment Data Acquisition (EDA / Interface A) Standards, or to develop this capability in-house.

I am especially qualified to explain the pros and cons of choosing a commercial solution, having worked as the EDA standards implementation lead developer in an equipment supplier before joining the Cimetrix team earlier this year.

• Pros

In this final article of the “EDA Application and Benefits” series we discuss an application that is one of the most basic and intuitive, but also provides the foundation for the many of the emerging capabilities in the machine learning and artificial intelligence (AI) domain—trace data analysis. Moreover, of all the applications we’ve introduced over the past 6 months, trace data analysis is the one that most directly leverages the capabilities of the SEMI Equipment Data Acquisition (EDA) standards. 

Problem Statement

When we ask fab process engineers and their supporting automation teams why they are now requiring the latest SEMI EDA/Interface A standards on their new equipment, the answer we hear most often is “To better understand equipment and process behavior.” And when asked why this cannot be achieved using the SECS/GEM interfaces, the answers are equally consistent: “The detailed information we need is either unavailable or cannot be collected at the frequencies we need to accurately see and characterize the behaviors we are interested in. And even if this were possible, we don’t have the operational freedom to change our data collection systems as quickly as our needs change, so we must have a more flexible approach.” 

What these engineers are looking for as a starting point is a way to easily specify a list of potentially related equipment parameters and collect their values at a rate that is fast enough to see how they are changing in relationship to one another. Human beings are wonderful at pattern recognition, and simply being able to juxtapose a set of signals on a “strip chart” display (see first figure below) can yield important insights into the underlying process. Of course, this capability is most useful when the engineer can precisely specify the timeframe of interest for this visual analysis. This is sometimes called data “framing” and can be accomplished by using equipment events to bracket the period of interest (see second figure below).

While humans may be good at pattern recognition, they quickly get overwhelmed when the number of parameters to view grows and/or the timespan to consider expands… which is where trace data analysis software enters the picture.

Solution Components

In addition to very flexible time-series data visualization tools, trace data analysis software packages must be able to “slice and dice” subsets of large data sets to compare every imaginable combination of equipment instance, process chamber, product, layer, recipe, fixture, consumable batch, shift, operator, … (you get the picture) to look for correlations between important factory metrics and the behavior of the equipment involved. Moreover, they must be able to identify and flag “abnormal” (which must be flexibly defined) situations for further analysis, since these may hold clues about incipient failures that traditional multivariate FDC (fault detection and classification) applications may not catch.

In fact, there is an emerging school of thought for fault detection that states “most of the time, the equipment is making good wafers, so unless there’s something very different about the tool behavior between the most recent lots and the current lot (as determined through trace data analysis), it’s very likely that the current lot is good as well.” This simplified approach has also been called “model-less FDC” because it mostly compares trace data signals rather than passing tool parameters into highly context-specific multivariate statistics-based models.

Of course, any trace data analysis application is only as good as the data that feeds it… which is where the EDA standards and the related equipment purchase specifications come into the picture.

EDA (Equipment Data Acquisition) Standards Leverage

Previous postings such as Episode 4 on Fault Detection and Classification and Precision Data Framing during Process Execution – Tricks of the Trade have highlighted the capabilities of the Freeze II EDA standards related to Data Collection Plans (DCPs) and the Trace Requests, Event Requests, and Exception Requests that comprise them. We have also highlighted the need for broad stakeholder involvement when creating the EDA section of an equipment purchase specification and described the process we’ve crafted to accomplish this.

However, to fully support a world-class trace data analysis application, it’s important to understand what to ask of the equipment suppliers. To this end, we’ve excerpted some key sample requirements from a typical purchase specification below.

• Equipment Model Content (SEMI E120, E125, E164)
  
  
  • The hierarchical depth of the metadata model should include at least the “field replaceable unit” (FRU) level, and one of two levels below this for complex sub-systems.
  • The metadata model must contain command and status information for all equipment components that affect material movement. This includes not only material transfer elements such as robot arms, but also devices that may inhibit/enable material movement, such as gate valves, interlocks, etc.
  • The metadata model must include control parameters for all significant operating mechanisms and subsystems in the equipment. The control parameters may include but are not limited to: process variable setpoints and status values; control variable status values; PID tuning parameters, control limits, and calibration constants.
    
  • The metadata model must include whatever additional usage counters, timers, and other parameters that may be useful in time-based, usage-based, and condition-based maintenance scheduling algorithms.
    
  • The metadata model must contain parameters the describe consumption rates and levels for key process resources such as electricity, process gases, and other consumables. These are used in some of the FDC models to detect potentially abnormal process conditions.
  • Suppliers must provide a written description of the update rates, recommended sampling intervals, normal operating ranges and behaviors, and high/low/rate-of-change limits for all key process parameters.
  • Etc.
    
    
• Data Collection Capability (SEMI E134)
  
  
  • Equipment must include built-in DCPs to support common equipment performance monitoring, diagnostic, and maintenance processes that are well known to the supplier. Documentation for these DCPs must define their purpose, activation conditions, interface bandwidth consumed, and the types of analysis the collected data enables.
  • Equipment parameters provided through the EDA interface must exhibit a number of data quality characteristics, including, but not limited to: an internal sampling/update rate sufficient to represent the underlying signal accurately; timing of trace reports that is consistent with the sampling interval within +/- 1.0%; values in adjacent trace reports must contain then-current values at the specified sampling interval; and rejection of obvious outliers.
    
    
• Performance Requirements
  
  
  • Performance requirements will be expressed as combinations of sampling interval, # parameters per DCP, # of simultaneously active DCPs, group size, buffering interval, response time for ad hoc “one-shot” DCPs, maximum latency of event generation after the related equipment condition occurred, consistency of timestamps in trace reports with the specified sampling interval, and perhaps others.
  • Example: The EDA interface must be capable of reporting at least 5000 parameters at a sampling interval of 0.1 seconds (10Hz) with a Group Size of 1, for a total data collection capacity (bandwidth) of 50,000 parameters per second. It must also support simultaneous data collection from at least 5 clients while still achieving a total bandwidth of 50,000 parameters per second; Group Sizes greater than 1 may be used to achieve this level of performance.
  • Some equipment types may have more stringent performance requirements than others, depending on the criticality of timely and high-density data for the consuming applications.
    

KPIs Affected

Trace data analysis will undoubtedly take its place among the other “mission-critical” applications in today’s fabs because of the increasing process complexity and the need to maintain the traditional “time to yield” production ramp. This is especially true for the industry pioneers now using the latest EUV scanners, as there will be much to learn about this new technology in the coming years.

Let Us Hear from You!

If you want to understand how the latest EDA standards and trace data analysis can support your future manufacturing objectives, or how to make this a reality in your Smart Manufacturing roadmap, please schedule a meeting!