Industry News, Trends and Technology, and Standards Updates

A little over 3 years ago, the Elsevier publishing company decided that the topic of Smart Manufacturing had achieved enough global breadth and momentum to warrant in-depth treatment in an edited set of articles. As they researched the domain, they realized that it split neatly into two categories of material: a general set of concepts that are applicable in any industry, and example implementations that are specific to a small set of related industries.

The editors then consulted with a number of industrial and academic subject matter experts to subdivide each category into a set of topics, and the Table of Contents for each of the two volumes was born. The results were first published in August 2020, and are available from Elsevier:

• Volume 1: Concepts and Methods (426 pages) 
  
  “Research efforts in the past ten years have led to considerable advances in the concepts and methods of smart manufacturing. Smart Manufacturing: Concepts and Methods puts these advances in perspective, showing how process industries can benefit from these new techniques. The book consolidates results developed by leading academic and industrial groups in the area, providing a systematic, comprehensive coverage of conceptual and methodological advances made to date.”
  
  
• Volume 2: Applications and Case Studies (528 pages) 
  
  “Research efforts in the past decade have led to considerable advances in the concepts and methods of smart manufacturing. Smart Manufacturing: Applications and Case Studies includes information about the key applications of these new methods, as well as practitioners’ accounts of real-life applications and case studies.”

One of the editors was Dr. Thomas F. Edgar, a long-time professor in the University of Texas Chemical Engineering department who specialized in process modeling, control, and optimization. He is also credited by many as being the “father of semiconductor APC" (advanced process control), since a number of his graduates ended up in the automation/process engineering department at AMD/Austin and transformed this domain from spreadsheets and “sneakernet” to a fab-wide, model-based process control system.

Needless to say, I was honored when he called to say that Elsevier wanted a case study on the semiconductor industry’s use of smart manufacturing, and to ask if I would write that section. I readily agreed, and provided the chapter titled “Smart Manufacturing in the Semiconductor Industry: An Evolving Nexus of Business Drivers, Technologies, and Standards.” The abstract for that chapter follows:

“The semiconductor industry embarked on its own “Smart Manufacturing” journey well over 30 years ago, long before the term was coined. The continuous productivity improvements that we now take for granted are essential for creating and building the devices that fuel our electronics-based global economy and maintaining commercial viability in a hypercompetitive industry. However, what we have learned in the process is that like many scientific endeavors, it is a journey without a destination. As new market opportunities are met with new device and system technologies in an ever-changing business environment, the list of manufacturing challenges is never complete.

This is where the global Smart Manufacturing initiative enters the picture. Although its key tenets are not specific to the semiconductor industry, the attention it drew to this topic triggered the formation of the SEMI Smart Manufacturing Community, which now provides a forum for thought leaders across the semiconductor manufacturing value chain to focus on these important challenges. To put this initiative in its proper perspective, this chapter explores the past, present, and future of Smart Manufacturing in the semiconductor industry.”

Regarding the Smart Manufacturing Initiative (now with chapters in several geographic regions), SEMI is the perfect host for such a group. Since its charter includes both trade and standardization activities for the industry worldwide, and its members develop the diverse technologies that comprise the electronics industry, SEMI forms an ideal backdrop for describing the evolution of semiconductor Smart Manufacturing. This is illustrated in the figure below.

Thinking back over my almost 50 years in the industry, I think the factor that contributed the most to the industry’s current success was the evolution of its collaboration culture. Whether driven by the need for industry efficiency, the fear of extinction, or the recognition of mutual interdependence, today’s global semiconductor industry enjoys a collaborative culture that is unequaled in other for-profit industries.

If the topic of Smart Manufacturing piques your interest, I invite you to visit the Elsevier websites shown above for more information. I have also distilled the semiconductor case study chapter into a set of slides that I will be happy to review/discuss with you.

We wish you the best on your company’s Smart Manufacturing journey – let us know how we can help!

We’re drowning in data but still thirsting for wisdom… This is a paraphrase of what one of the virology doctors interviewed at the beginning of the COVID-19 pandemic said about the challenges they faced in charting a course of action using the myriad volumes of data being collected around the world. The sentiment reminded me that in an entirely separate but no less complex domain: electronics manufacturers face similar challenges using data from the thousands of disparate sources in a factory to make the decisions that ultimately determine the viability of their enterprises.

To the extent that these two domains have a similar problem statement, they also share a set of solution technologies. Among them is Machine Learning (ML), which is a promising subset of Artificial Intelligence (AI). The essence of Machine Learning is using data collected from past events to develop and train a model that can make reliable predictions about the outcome of future, as-yet-unseen events.

Machine Learning technology is already more ubiquitous than you might imagine. For instance, if you’ve ever 1) ordered one of Amazon’s product recommendations based on your previous purchases and search history, 2) seen the names on the faces of people in your photo library (and updated the ones it got wrong), 3) rated your satisfaction with a Netflix movie you’ve just watched, or 4) interacted with one of the language translation apps on your smart phone, congratulations—you are now an integral part of the Machine Learning ecosystem of the suppliers of those everyday products.

Beyond these familiar examples, two factors have contributed to the rapid ascendance of ML as a viable production technology: the availability of massive amounts of data and affordable storage and processing power to analyze it. As a result, Machine Learning is now seen as a key enabling technology for Smart Manufacturing in multiple industries and at multiple positions along the value chain.

Documented use cases in semiconductor front-end factories include production scheduling and dispatching, process analysis and excursion prediction, equipment FDC (fault detection and classification), preventive maintenance, failure prediction, trace data analysis, and so on. Equipment suppliers are likewise looking for ways to add value to their products by offering ML-based capabilities as an option. Semiconductor back-end and SMT factories and their respective equipment suppliers are also actively evaluating ML technologies. These instances all have one thing in common: the need for quality equipment data… and lots of it.

Two of the most prevalent approaches to Machine Learning are 1) supervised learning, which uses labelled data samples to develop a model that predicts the labels for future samples; and 2) unsupervised learning, which can use unlabeled data samples to develop a model of expected behavior that can predict deviations from that behavior for future samples.

Both categories have direct applications in manufacturing and include a range of specific algorithms that may be suited to various problem types. The trick is knowing which technique(s) to apply in which situations, and once chosen, how to tune a particular technique for a given problem.

One category of public-domain algorithms that has shown significant promise for commercial applications has been labelled “Deep Learning,” and it refers to a variety of “Artificial Neural Net” approaches. These algorithms fall into the supervised learning category but can nevertheless be applied in situations where the labels are not known a priori (e.g., analyzing system log files to detect anomalous behavior).

A frequently-cited candidate for a supervised learning algorithm is predictive maintenance, which uses as input a high-dimensional (i.e., lots of parameters) trace vector of equipment parameters with a binary label (“good” or “failed”). With enough production history this data can be used to train and validate a failure prediction model that would improve on the “run to fail” or “just in case” maintenance strategies used today.

Likewise, a good candidate for unsupervised learning is anomaly detection using equipment log file data (could also include trace data) as input. Many equipment suppliers save this kind of data for “after the fact” analysis of problems that occur in manufacturing without a clear sense of what might be useful. Deep Learning algorithms are particularly adept at using high dimensional data to create complex mathematical models that represent “normal” behavior and can be used to flag “abnormal” behavior as it happens.

From an implementation standpoint, there is no “one size fits all” Machine Learning package or library that can address the full range of potential opportunities. Moreover, since Machine Learning is only a component (albeit a vital one) of an overall solution system, it is important to understand how all the pieces fit together when considering these technologies for a specific problem. Subsequent posts in this series will explore these topics in greater detail, so stay tuned for additional information about how to map these new technologies into your product and problem space. However, if your need is more urgent, click the button below to see how we can explore this exciting new area together.

For the first time since the Fall of 2019, the SEMI North America Information & Control Committee (I&CC) was finally able to meet and conduct business online. Throughout all of 2020, the I&CC was not able to meet because SEMI regulations did not at that time allow voting in online meetings. Instead, only the task forces have been meeting. As a result, any passing ballots, unless super clean, had to wait for adjudication in the North America I&CC.

This year, prior to the I&CC meeting on April 1 and 2, all of the associated task forces also met as usual, including the GEM 300, Diagnostic Data Acquisition (DDA), and Advanced Backend Factory Integration (ABFI) task forces. Moreover, the I&CC was able to conduct all the unresolved business that had accumulated over the last year. During the committee meeting, the I&CC successfully used the SEMI Virtual Meeting (SVM) software which runs in an internet browser, allows each committee member to log in, and allows for official voting to take place during the meeting. The North America I&CC will meet again during the summer.

GEM 300 Task Force

In the GEM 300 task force, the primary activity was to officially redefine its charter and scope to match what it has already been doing for the last 20 years. Each SEMI task force defines a “Task Force Organization Force” document (aka TFOF) to establish its charter and scope. Somehow, the GEM 300 task force charter and scope were severely out of date.

In addition to this update, some changes to the E5 standard finally passed voting, pending some final approval. The E5 changes include several new messages and establish definitions for commonly used data collection terminology. The new messages complement the existing set of messages by allowing the host to query information about the current data collection setup. Currently, it is common for a host program to reset and redefine all data collection after first connecting to an equipment because there has been no way to query this information. With these new messages, the host will be able to query the setup and confirm that no data collection has changed while disconnected. Finally, it will be easier to test GEM interfaces with these new messages.

The task force already approved tasks to consider some major work to the GEM standard. The task force is also considering changes to the E116 standard, but there is some resistance to these changes. Here is a summary table of the GEM-related standards activity from across the globe.

DDA Task Force

In the Diagnostic Data Acquisition (DDA) task force (responsible for the EDA standards, aka Interface A), freeze 3 development is moving forward. All of the ballots still failed as expected. The number of remaining technical issues nevertheless has dwindled to just a handful. E132, E125, and especially E164 need the most work.

Following is a summary of the previously completed work.

And here is a summary of the work in progress.

All of the failed ballots will be reworked and resubmitted for voting. For many of these ballots, it will be the sixth time to go through the SEMI ballot procedure. Consensus is very nearly achieved, and the defects in the ballots have been identified and corrected. Additionally, there are plans to modify SEMI E179, the standard that defines how gRPC will be utilized. While testing EDA freeze 3, Cimetrix has identified two simple ways to modify the E179 protocol buffer files in order to reduce overhead. These and a few other changes will be proposed in a new ballot.

One of the last changes to the freeze 3 standards will be the introduction of passwords. In the current freeze 1 and freeze 2 versions, there are no passwords. Any client that knows a valid, unused Access Control List entry (ACL, the equivalent of a user name) can connect; therefore, there really isn’t any authentication unless using the SSL protocol with certificates. Passwords will enhance EDA security and facilitate EDA interface setup by allowing client applications to use the same ACL entry while defining a unique password to block other clients from using the same entry. The final E132 ballot will finalize the password feature.

The task force leaders are asking the voting members to raise any final issues before these ballots are submitted to SEMI to the next voting cycle so that we can approve these standards, give implementers a chance to experiment with EDA freeze 3, raise any serious issues that impede the implementation, and then propose the final changes which incorporate that feedback. Until a version of these standards is formally approved, it will be difficult to get concrete and widespread feedback on the new technology, which is a necessary precursor to its adoption and use.

ABFI Task Force

The Advanced Factory Integration task force passed more changes in E142 without controversy. The task force plans to create E142.4, another GEM implementation of E142, designed for larger wafer maps to allow for increased traceability possibilities. Additionally, the task force continues to make plans to develop an adoption matrix as a new standard to describe when GEM and GEM 300 standards should be adopted in backend equipment based on equipment features.

The Cimetrix Connectivity Group of PDF Solutions is happy to announce that Cimetrix CIMControlFramework^TM (CCF) version 6.0 is now available for download.

CCF is a software development kit (SDK) that enables users to design and implement a high-quality equipment control solution using provided components for supervisory control, material handling, operator interface, platform and process control, and automation requirements. CCF is built on the reliable Cimetrix connectivity products which provide GEM/GEM300/EDA interface functionality.

We have previously done a series of blog posts on the functionality of CCF. The same great functionality users have come to expect with CCF is still available, but in a cleaner, slicker, easier to use package.

Reorganized directory structure

In versions before CCF 6.0, core CCF packages (packages provided by CCF) were contained in the same directory as sample code and runtime files. This made it more difficult for CCF users to understand what code was required to be customized and what code was basic to CCF. (Note: you can still customize the basic CCF functionality, but it is not required.) In this release, we modified the directory structure to identify more clearly what is core CCF and what is sample or custom code. This is closer to the structure followed by CCF applications. The following diagram shows the new structure:

In addition to clarifying CCF components, the new structure allows us to easily develop samples for additional equipment types. New samples will be added in future versions of CCF.

New WPF framework

Since CCF started providing a Windows Presentation Foundation (WPF) framework, we have received feedback on WPF features user would like added to the framework. Also, our engineers have continued to improve their WPF expertise, which has led to other improvements. CCF 6.0 includes the requested changes and best practice improvements in the new WPF framework. Some of these changes include:

• Simplified hierarchy which makes it easier to understand which objects to inherit from.

• Centralized style elements to allow users to change the look and feel (skin) of the operator interface to meet their needs.
• Enhanced controls library that provides common controls for use in creating equipment control.
• Increased E95 compliance, available with a configurable control panel.
• Accelerated screen creation is possible with the change in hierarchy organization and the enhanced control library.
• Richer set of native WPF screens. In earlier versions, CCF had several native WPF screens, but also had many screens created with WinForms and hosted in WPF. CCF 6.0 has all native WPF screens in the WPF sample operator interface. These screens can be reused, customized, or replaced. (Note: WinForms screens are also still available in CCF 6.0.)

The following image shows the main screen of the WPF operator interface for the CCF atmospheric equipment sample application. Most of the controls on the screen are available for use and customization by CCF developers.

Updated samples

CCF has contained fully functional atmospheric and vacuum sample applications for many years. Over the years, we have improved the functionality for scheduling, simulation, and device interface interaction. However, the samples had always remained the same. With CCF 6.0, the atmospheric and vacuum sample applications were updated to take advantage of the other changes that have been made in CCF. These changes to the samples make them more useful in illustrating the proper use of CCF and providing a better starting point for creating custom applications.

Spring cleaning

CCF was originally released the summer of 2011 making it 10 years-old. Over the years, CCF has had several methods, objects and devices become obsolete. They were not removed from the product for backward compatibility reasons, but they were marked as obsolete. Because CCF 6.0 is a major release, we took the opportunity to do some spring cleaning and remove the obsolete items. CCF is now cleaner and tighter, and using it is much clearer.

Training material and upgrade guide

All the PowerPoint slides, lab documents, and corresponding solutions used for training developers on CCF have been updated for CCF 6.0. We have already successfully used the new training materials with a few customers to help them get started with their equipment control application development.

As part of CCF 6.0, we provide a CCF 5.10 to CCF 6.0 Upgrade Guide that contains detailed instructions on how to migrate applications created using previous versions of CCF to CCF 6.0.

Conclusion

We have been looking forward to the CCF 6.0 release for a long time and are excited for developers to get started using it. We are confident existing users will like the changes and that new users will have a good springboard in getting started with their equipment control application needs. We look forward to working with you and hearing from you.

Near the end of January for almost two decades, the companies that form the Automation Network Dresden (AND) have hosted a gathering of semiconductor automation professionals in the picturesque setting of Dresden, Germany. The chill of winter during this event has always been in stark contrast to the warmth of the reception the speakers and participants enjoy at this event. The big question this year was “how could the organizers possibly maintain this tradition amid the COVID-19 pandemic and its travel restrictions?"

Automation people being a creative bunch, they responded by offering a series of free, half-day “digital events” spread across the first four months of the year, and each hosted by one of the AND member companies: SYSTEMA, Fabmatics, XENON, and Kontron AIS. The first two sessions are now in the record books, and as a participant in both, I can attest that no momentum or value has been lost. The only exception is the lack of the forum’s famous evening event, which will undoubtedly return next year when the virus is behind us.

The first session of the year was hosted by SYSTEMA on January 28 with the theme of Datafication and Automation: The new normal for semiconductor manufacturing. I was privileged to among the invited speakers, and shared the agenda with Manfred Austen of SYSTEMA (always a hard act to follow!), Jean-Marc Philippe of ST/Micro Singapore, Axel Wogawa of SYSTEMA, and Klaus Kleilein of Fabmatics.

In this context, I chose the topic of semiconductor backend automation, and you can download my presentation “Wafer Fab Best Practices for Backend Automation”. Key takeaways from this presentation include the industry drivers for higher levels of backend automation, the unique challenges this poses when compared to wafer fab automation (see the figure highlighting multiple material transformations below), the role that industry standards in many categories will play in this process, and last but not least, the importance of defining explicit integration message sequences between all the equipment and the factory systems that bring them together into fully automated operations. This latter insight was one of the key enablers for the wafer fab transition from 200mm to 300mm, and the concept equally applies in the backend.

The second event in the series was hosted by Fabmatics on February 25, 2021, with the enticing theme of Forever Young: Automation Makeover Rejuvenates Golden Age Fabs. It featured speakers from Bosch, Nexperia, Cohu, and SYSTEMA.

The next event will be hosted by Xenon on March 25, 2021 with the theme Digitalization Meets Mechanical Engineering, and the final event hosted by Kontron AIS on April 29, 2021 (theme still TBD – stay tuned). For more information about this digital event series, visit the Automation Network Dresden website.

For help in crafting and executing your own backend automation strategy, or any other topic related to advanced manufacturing connectivity and control, please contact us by clicking the button below. 

Read the pre-show blog of the SEMICON China 2021 show today. Read it now in Chinese or below in English.

SEMICON China 2021将于2021年3月17日至19日在上海新国际博览中心举办。矽美科公司将一如既往的参加，希望在展会上和你们相遇！

如果您也在此展会，欢迎您光临我们位于N3-3157号展位。我们将展出GEM和EDA/Interface A产品以及我们的智能工厂平台。

SEMICON China将我们和世界上发展最快、最具活力的微电子市场联系在一起，并为参展商提供了一个为中国最专业的人士展示我们的产品和技术的平台。

在我们为展会做准备的过程中，我们欢迎任何会议邀约，无论是在线的还是具体地点面对面的，请单击下面的按钮预约。如果您也参与展会，我们随时恭候您光临我们的展位。希望我们很快能见面！

SEMICON China 2021, an in-person, is taking place March 17-19, 2021 at the Shanghai New International Expo Center. Cimetrix Incorporated will be exhibiting and we hope to see many of you there!

If you are able to be at the exhibition during this time, we encourage you to visit Cimetrix Incorporated at booth N3 3157. We will be featuring our GEM and EDA/Interface A products as well as our Smart Factory Platform.

SEMICON China connects people in the world’s fastest growing and most dynamic microelectronics market and gives exhibitors the platform to showcase our products and technologies in front of the most qualified audience of industry professionals in China.

As we prepare for the show, we welcome any meeting requests, both virtual or in-person depending on location, by clicking the button below. Or if you are able to be in Shanghai during SEMICON, we look forward to you dropping by our booth at any time. We hope to meet with you soon!

SEMI Europa is sponsoring a new event – Technologies Unite Global Summit – and we are excited to announce that Cimetrix Incorporated will have a virtual booth at this online event on 15-19 February, 2021. 

Technologies Unite Global Summit brings together the global microelectronics supply chain, manufacturers, and the end users for this digital experience that spotlights digital transformation and microelectronics industry innovation and growth. This summit will feature eight forums presenting the latest innovations, as well as seven pavilions from around the world where exhibitors and attendees can connect with the global SEMI community.

After a year of cancelled and postponed in-person events, we are happy to participate in these virtual events that gives all of a us a chance to re-connect with our global communities. Be sure to register for and attend this brand new event and be sure to stop by our booth!

Thomas Simon, the General Manager of our Europe office will be available during booth hours to host discussions about all of our products, and particularly our Cimetrix Sapience^® Smart Factory Platform.

We hope to see you there. You can contact our team any time to schedule a demo or make an appointment by clicking the button below.

SEMICON Japan 2020 is going virtual and we will be there! You can read about it now in Japanese or below in English.

今年のSEMICON JapanはCovid 19の世界的大流行の影響もあり、仮想環境での開催となりました。Cimetrix Incorporatedは12月14〜17日の4日間バーチャルブースに出展致しますので、皆様のご来訪を心よりお待ちしております。

日本では現在、半導体製造装置の3分の1と材料の半分以上を世界の半導体製造業界に供給しています。 世界がよりスマートになるにつれて、革新的なソリューションとテクノロジーがこのような大きな展示会で紹介され続けています。

Cimetrixは、GEM機器の接続と制御、およびEDA /interfaceA活用して装置データの収集、提供を行う事が出来ます。

Cimetrix Sapienceは、単一のイベント駆動型フレームワーク内でさまざまな工場設備をシームレスに接続するスマートファクトリープラットフォームです。 Sapienceプラットフォームにより、工場のITシステムは工場の機器に直接アクセスでき、その結果として生じる機器の通信、データ収集、およびプロセス制御により、インダストリー4.0、ビッグデータ、およびスマートファクトリーのイニシアチブの基盤が確立されます。

もし別途ご相談いただければ、デモンストレーションを行う事も出来ますので、お気軽にご連絡ください。

それでは2020年の最後のSEMICONショーにて皆様に会える事を心から楽しみにしています。

After Cancelling the show several months ago in Tokyo, due in part to the Covid 19 world-wide pandemic, SEMICON Japan reconsidered and decided to go forward with the Exposition in a virtual environment. Cimetrix Incorporated (now a part of PDF Solutions) is proud and excited to be participating with a virtual booth on the 14-17 of December and we hope to see many of you there!

Japan supplies one third of the equipment and more than half of all materials to the global semiconductor manufacturing industry. As the world gets smarter, innovative solutions and technologies continue to be introduced at big shows like this.

We will be able to schedule demos of our GEM equipment connectivity and control software solutions, as well as our EDA/Interface A products.

The Cimetrix Sapience platform will also be available for a demonstration by visiting our virtual booth to make an appointment with our team. Sapience is the Smart Factory Platform that seamlessly connects varying factory equipment within a single event-driven framework. The Sapience platform allows factory IT systems direct access to factory equipment, and the resulting equipment communication, data collection and process control establishes the foundation for Industry 4.0, Big Data and Smart Factory initiatives.

We encourage you to join the Cimetrix Japan team at our virtual booth at this last SEMICON show of 2020. We hope everyone is staying safe and healthy, and hope to see you soon!

To schedule a demo prior to the show, click the button below.

Today’s blog posting highlights the latest and most recent activity with the Cimetrix Book Club. Our employees constantly strive to develop their skills, share information, and keep up to date with the industry. Part of this effort includes an employee book club that involves many of our team members each month, and from time to time we cover some of their favorites here on our blog!

Today's book is titled "The Art of Unit Testing" by Roy Osherove. The book review is by Westley Kirkham, a Quality Engineer based in Salt Lake City, UT, USA.

“The Art of Unit Testing” guides the reader step by step from writing the first simple tests to developing robust test sets that are trustworthy, maintainable and readable.

In the first section, Osherove explains what a unit test is, the properties of good unit tests, and why they are so important. The lion's share is dedicated to the nitty-gritty of writing and maintaining unit tests specifically, and testing suites generally. The first part of the section goes in depth to show how Mocks, Stubs and Isolation frameworks are used to test your code. The last section discusses how to deal with resistance to change from co-workers and management if you're trying to introduce Test-Driven Development or Agile methodologies, as well as how to deal with legacy code. Osherove also shares his insights on what tools he believes are the best aids in unit testing. ReSharper is one of his favorites, but he also reviews Nsubstitute, Moq, CodeRush and others.

One section that stood out to our team was Osherove's three pillars of a good unit test—trustworthiness, maintainability, and readability.

Trustworthy tests are up-to-date, simple and correct. There are no duplicate tests, and they do not test any old functionality or functionality that has been removed. The unit test only tests one item and doesn't conflict with other tests. The bugs the test finds are actual bugs in the code, and not bugs in the test.

Maintainable tests are flexible, and don't break with each minor change to the product. The tests are isolated. They are not over-specified and they are parameterized.

Readable tests are easy to understand and do not require the developer or tester who comes after you to spend extra time understanding what you've written. The test names are descriptive, and the asserts are meaningful. Any failures or issues caught will lead the developer in the right direction.

These three pillars should apply to all that we write, not just tests.

At Cimetrix, much of what Osherove teaches is already integrated into our engineering culture. As part of our implementation of Agile, developers write unit tests to verify that the functionality they have coded is correct. It is then reviewed by another developer and a member of the QE team to ensure that common use cases and important edge cases are covered and that the functionality is complete. All code must follow naming conventions and styles verified through ReSharper. For all of our products, unit tests are run on each build, and integration tests are run nightly.

Osherove's lessons on unit testing implementation, testing suite organization, and test-driven development integration are simple and practical. This book would benefit any team looking to improve the fidelity of its software products and the efficiency of its engineers.

Background

Previous blog posting in this series have discussed the rationale for using SEMI’s GEM, GEM 300, and related automation standards in semiconductor backend factories, and pointed out that the specific adaptations required for the various backend equipment types are one of the focus areas for the SEMI Advanced Backend Factory Integration (ABFI) Task Force. In this posting, I will deal specifically with the benefits that can be realized by using the E157 Process Module Tracking standard in a backend factory context.

Since none of the backend material transformations are implemented in what front end experts would consider a “process chamber,” this may seem like an unlikely fit. Moreover, the velocity of backend processes seems contrary with the typical front end recipe execution paradigm. Finally, the lack of distinct substrate locations for some of the processes makes it difficult to know precisely when the process begins and ends for the affected material in some cases.

Regardless of these challenges, the requirements for single device traceability that include knowing the exact process conditions that a device was exposed to at every moment in its manufacturing life cycle (including the backend) argue for use of this standard wherever possible.Since none of the backend material transformations are implemented in what front end experts would consider a “process chamber,” this may seem like an unlikely fit. Moreover, the velocity of backend processes seems contrary with the typical front end recipe execution paradigm. Finally, the lack of distinct substrate locations for some of the processes makes it difficult to know precisely when the process begins and ends for the affected material.

SEMI E157 – Process Module Tracking

The purpose of SEMI E157 is “to define a standard equipment capability to report process-related data to the factory system… the activities of a processing location (i.e., process module) that are related to the execution of a recipe.” The standard further states that “the collection of process data during recipe execution is important to today’s semiconductor factories to support various applications that help optimize equipment processes, finished product quality, yield, and overall factory performance.”

These requirements are now every bit as important for backend factories as they are for the front end, so it is useful to understand how E157 can be effectively applied.

First of all, the E157 Module Process State Model is fairly simple, having only 4 states (three of which are “base states” with no sub-states) and 7 state transition events, shown in the diagram below.

This model represents the state of that portion (or portions) of a unit of equipment that executes a recipe to transform whatever material is present in that part of the equipment. In front end equipment, the chambers are relatively distinct, and usually process a small number of substrates (often one) at a time. By contrast, backend processes cover a broad spectrum of material types, from single wafers to strips (or lead frames) of multiple die to individual packages. The material flow characteristics also vary, from discrete (i.e., single workpieces) to batch to continuous. Moreover, the production rates and material volumes for these processes range from perhaps 90 wafers per hour to thousands of packages per hour… With these challenges, it is no wonder that the pace of automation for these facilities has lagged that of the front end.

How is the E157 Standard Used?

From the equipment’s perspective, every time the process module changes state according to the model above, the equipment sends the corresponding state transition event to the factory host computer. This is done using the SECS-II S6, F11 Event Report message with an event name exactly prescribed by the E157 standard.

The event report should also include whatever “context information” from the equipment that the factory applications need to analyze the equipment’s performance and behavior. For some backend processes, this might be lot ID, process job ID, recipe name, control settings, and current parameter values for important process variables. For others, it might be cumulative usage counts for fixtures with limited lifetimes, current levels of consumables used in the process, or configuration parameters for equipment with a range of setup possibilities. To further complicate matters, some of this information is common across most processes, some of it is process-specific, but some of it may actually be vendor-specific. It all depends on how the factory operates.

Finally, when used in conjunction with event timing information from other required standards (e.g., E90 Substrate Management), E157 data can help identify potential productivity issues, say, when there is an unexpected delay between material arrival (from E90) and recipe start (E157).

How Might E157 be Adapted for Backend Equipment?

As noted above, some equipment types process a stream of material continuously. In these situations, for a given lot, multiple substrates may be processed at the same time in a continuous flow (say, on a conveyor through an oven) until the lot is complete. For these types of equipment, E157 cannot be directly applied because it is chamber oriented, and you don’t get much useful information if you use the entire lot as the execution starting and completing events.

However, if you apply the same state model to the material (substrate, strip/lead frame, carrier, etc.) being processed rather than the equipment component, the collection events defined by E157 can be implemented when a unit of that material changes state. Specifically, the equipment can report the same collection events (ExecutionStarted, StepStarted, StepCompleted, ExecutionCompleted, StepFailed and ExecutionFailed) when execution on a substrate changes state, including when a step is started and completed. The meaning of a “step” would still be interpreted and designed by the equipment supplier. Associating these E157 collection events with a new “substrateID” data variable rather than a chamber enables the factory user to track the material state for each substrate going through the equipment.

Which Backend Equipment Types Should Implement E157?

Even though backend metrology, inspection, and test equipment may run recipes to perform their tasks, since no material transformation takes place, the state transition events and related context are far less important than the measurement and inspection results that these equipment types generate.
For the rest of the backend processes, the relative priorities for implementing E157 are the following:

High – die attach, wire bonding, dicing/sawing/singulation

Medium – backside grinding, polishing, plating, annealing molding, trim and form

Low – wafer mounting, die glue curing, deflashing, laser marking, tie bar cut, baking, burn-in

One category of equipment we have not mentioned is custom assembly equipment that can vary greatly by the end product form factor. The use of E157 in this equipment will depend entirely on the process complexity and sources of variability that must be tracked. However, it is safe to assume that for all but the simplest of processes, E157 will likely play a useful role.

Conclusion

E157 is a prime example of an exceptionally simple and well-written standard built on top of GEM technology that is easy to implement and provides a lot of end user value. The SEMI ABFI task force is now evaluating the specific adaptation of E157 for various backend equipment types and welcomes your contribution to that process.