Industry News, Trends and Technology, and Standards Updates

Read now in Korean or below in English.

저희 씨메트릭스는 Forward As One이라는 테마로 코엑스에서 2월 9일부터 11일까지 개최되는 세미콘 코리아 2022행사에서 여러분을 반갑게 맞을 준비를 하고 있습니다. 부스는 C홀에서 D홀로 넘어온 통로 앞에 위치한 DS37입니다.

잠시 들리셔서 귀사에서 활용할 수 있는 최신 기술도 파악하시고 업계동향도 듣고 가시기를 바랍니다.
씨메트릭스는 이번 세미콘 코리아에서 다음의 주제를 가지고 여러분을 기다리도록 하겠습니다.

• AI, 빅데이터, 인더스트리 4.0기술을 활용한 분석 및 솔루션 개발
• 후공정 차세대 장비소프트웨어를 위한 플랫폼
• 전공정 장비소프트웨어의 고도화
• SEMI 표준 (SECS/GEM, GEM300, EDA/Interface A)
• 납기 단축을 위한 테스터 & 문서화
  
  

특히, 국내뿐 아니라 해외 진출을 목표로 하는 고객들에게 최고의 파트너로서 최선을 다할 것을 약속드립니다.

아무쪼록 이번 행사에서 저희 부스를 방문해주시기를 부탁드리며, 아래 링크에 접속하시면 사전 미팅예약을 할 수도 있습니다.

After being canceled in 2020 at the beginning of the pandemic, SEMICON Korea is now back, with a hybrid event where the exhibition is live and in-person! Our Korea team will be exhibiting at the COEX in Seoul from February 9-11 at booth DS37. We look forward to the show and hope to see many of you there!

The theme for this year’s show is Forward as One and the exhibition will showcase the latest semiconductor materials, equipment, and related technologies. This is a great opportunity to meet with current clients, potential clients and see both the present and future of the global semiconductor industry.

If you want to find out more about EDA/Interface A, Equipment connectivity, and control or learn about our Smart Factory Platform, be sure to stop by our booth at DS37 to speak with our standards and product experts. We look forward to meeting backend OEMs as well as frontend OEMs as both are looking for the next-generation technology but in different ways.

We hope to see you at our booth, or you can request a meeting any time by clicking the button below.

SEMICON West 2021 wrapped up last month and despite being a hybrid event with limited attendance, there was nevertheless a lot of excellent technical information available through the various SEMI-organized programs. One such program was the Smart Manufacturing Pavilion which featured a series of “Meet the Experts” presentations on Tuesday afternoon, December 7.

Our own Alan Weber (VP of New Product Innovations) was privileged to be included, and his talk was titled “Accelerating Advanced Backend Automation through Smart Application of Frontend GEM 300 Standards.” The presentation was co-authored by Michael Kollex, Swee Shian Yap, and Olaf Herzog of Infineon Technologies, who also developed and contributed much of the technology that was discussed. But we’re getting ahead of ourselves…

Background

Automating assembly, packaging and test facilities has always faced challenges not seen by their upstream wafer fab counterparts. These include (but are not limited to):

• Multiple material transformations (and associated carrier types)
• Linear flow shop manufacturing operations (vs. cyclical)
• High product variety and velocity
• Significant manual intervention
• Complex unit product traceability requirements
• Low (relative to wafer fab) equipment cost and automation budget
• Equipment supplier un-familiarity with SEMI Standards
• Handling multiple data source types/protocols

In mid-2019, a new SEMI Standards task force—the Advanced Backend Factory Integration Task Force (ABFI TF)—was created to address these challenges.

An important operating principle of this task force is to ensure that any new standards identified for the assembly and packaging segments are not only technically consistent with the existing body of connectivity and control standards but also directly leverage as much of the current SEMI automation standards as possible. This is especially important for the capabilities covered by the GEM 300 (Generic Equipment Model) standards since GEM is already well adopted by many of the equipment suppliers to the backend assembly, packaging, and test market.

However, this still leaves a gap that must be filled by the automation requirements for each factory customer, which is precisely what the work described in this presentation accomplishes in a way that serves the entire industry.

Solution Approach

The approach to this problem features several key innovations.

The first key innovation is the definition of a detailed “Target Equipment Integration Sequence” applicable to all equipment types that supports full automation of assembly and packaging operations while eliminating the ambiguity that raises implementation costs for equipment suppliers and factory engineers alike. The scope of this sequence for a given unit of equipment covers its entire operation, from loading material carriers and verifying their content; mounting, usage tracking, and unmounting of consumables and durables that are directly associated with the products being manufactured (a key traceability requirement); creating the process/control jobs appropriate for that material and retrieving the associated process recipes; tracking execution of those jobs and accumulating the data items required for single device traceability; storing that data in the substrate map data structures; and passing that information back to the factory systems.

The specific expression of this integration sequence is a ladder diagram of the system communication partners, which include an operator/robot, the equipment, an Equipment Automation Framework (data collection server), and the Factory Information and Control System(s). This is shown in the figure below.

The second key innovation is basing the messages that constitute this sequence on existing, mature SEMI GEM 300 standards, thereby reducing (and eventually eliminating) the need for custom implementation software. Amazingly, except for the need to support “nested carriers,” realizing the integration sequence requires almost no modifications to the existing GEM 300 standards.

The third key innovation is realizing this integration sequence in a sample application suite to bring the specification to life and provide a reference implementation to accelerate the development process. This includes 1) an equipment simulator that faithfully implements all the capabilities called for in the integration sequence, 2) a sample factory host application that serves as the principal communications partner for the equipment during the implementation of the integration sequence by a specific equipment supplier, and finally 3) a set of automated tester “plug-in” modules for validating that the equipment-side implementation has in fact met the requirements. Together these software modules greatly reduce the time it takes to understand, implement, and test these important new specifications. This software suite is depicted in the figure below.

The fourth innovation is more procedural than technical: by openly sharing this design with the industry standards community, we believe it will be enhanced and further generalized by other assembly and packaging thought leaders, increasing the level and sophistication of overall automation capability while lowering integration and operation costs across the industry.

What’s Next?

Next steps include promoting this design to potential stakeholders through webinars and regional training events, gathering and incorporating feedback into the key artifacts, and validating its applicability in multiple manufacturing sites.

Where Can I Get the Presentation?

A fully narrated version of the presentation is available here; we hope you find it useful, and please contact us with any questions. We wish you the very best on your company’s backend automation – let us know how we can help!

SEMICON Japan 2021 is back and our team in Japan will be there! You can read about it now in Japanese or below in English.

セミコンジャパン2021が東京ビッグサイトで12月15日から17日の3日間開催されます。　「共に前進しよう」のスローガンの下、弊社Cimetrixも出展致します。

Cimetrix独自のブースを以て出展する2回目のセミコンジャパンとなります。今年はブース番号＃1608にて皆さまのご来場をお待ちしております。また共同出展を行って頂ける、ローツェ株式会社様（＃5411）、株式会社明電舎様（＃4941）にても、Cimetrixのご案内を差し上げております。

半導体産業における製造技術、装置、材料をはじめ、車やIoT機器などのSMARTアプリケーションまでをカバーするエレクトロニクス製造サプライチェーン唯一の国際展示会。東京ビッグサイトでのリアル展示会に加えて、リアルとオンラインセミナーも提供し、今年は「SEMICON Japan 2021 Hybrid」として開催されます。

Cimetrixは私共が誇る世界標準としてご利用いただいておりますGEM通信、装置制御ソリューション、またEDAなどのソフトウェア製品をご紹介させて頂きます。

また生産現場でご利用いただけるハイパフォーマンスデータ収集のプラットフォームであるサピエンスもご紹介しております。　CimetrixのSapienceはクラウドネイティブのデータ解析やマシーンラーニング等にご活用いただける装置データ収集のパイプラインプラットフォームシステムです。

では皆さまのご来場を楽しみにしております。

SEMICON Japan 2021, with the theme “Move Forward Together” will be held at Tokyo Big Sight from 15 December – 17 December, and Cimetrix Incorporated will be there!

We will exhibit for the second year at booth #1608 and welcome you to come and join us. You can also find information about Cimetrix at our partner Rorze Corporation (booth #5411).

SEMICON Japan will be a hybrid show this year that offers both an in-person exhibition as well as programs and seminars online. It is a must-attend event that connects technologies in the digital transformation era, bringing together the entire semiconductor manufacturing supply chain as well as the “SMART” applications powered by semiconductor technology such as IoT.

Cimetrix will be showing all our world-class GEM equipment connectivity and control software solutions, as well as our EDA/Interface A products. These factory automation software products all leverage open-architecture designs and industry standards.

Cimetrix Sapience will also be available for a demo at our booth. Sapience is a scalable, distributed platform for managing high-performance data pipelines. This cloud-native platform is ideal for streaming analytics, machine learning, and artificial intelligence applications requiring massive amounts of data directly from the production equipment.

We encourage you to stop by booth #1608 and speak with an expert for your Smart Equipment and Smart Factory software needs. You can also book a meeting with us in advance by clicking the button below. We hope to see you soon!

After missing the live and in-person event last year due to the pandemic, SEMICON West is once again opening its doors to connect the entire electronics supply chain, and Cimetrix Incorporated will be exhibiting. Our booth is #345 and we hope you'll stop by and see us!

SEMICON West will be held December 5-7 in the Moscone Center in San Francisco, CA, USA – the theme for this hybrid event is "Explore, Network, Interact." After almost two years of restricted travel and virtual events, SEMICON West is a great place to reconnect with contacts, customers, and partners to drive your business forward. We couldn’t be more excited about finally meeting in person for the first time since 2019!

Cimetrix expert Alan Weber will participate in the "Optimizing Your Manufacturing—Connecting the Smart Way" track in the Smart Manufacturing Pavilion on Tuesday, December 7 at 2:45 PST. His presentation is titled “Accelerating Advanced Backend Automation Through Smart Application of Frontend 300mm Standards" and we invite you to join us at the Smart Manufacturing Pavilion to learn about this important topic firsthand.

Stop by our booth any time to visit with our team of experts and discuss our standards, connectivity, and control solutions. We will also feature our Smart Manufacturing cloud-native connectivity and application platform Cimetrix Sapience®. You can visit with us in our booth any time during the show or make an appointment ahead of time by clicking the link below.

Background

Cimetrix CIMConnect^TM customers enjoy many benefits to maintaining an active support contract, and today we are announcing yet another one: access to a set of product-specific training videos.

A few years ago, the Solutions Engineering team at the Cimetrix Connectivity Group posted product training material including the full set of CIMConnect training PowerPoint presentations to facilitate self-training for those unable to attend a formal session. We update this repository periodically as the training material is revised and improved. The material is available online through the Customer Portal. After logging in, you can find the presentations here:

Training Videos

To complement the presentation material shown above, the Solutions Engineering team is now creating video training material. As of mid-June, 2021, the first set of training videos for CIMConnect is also available via the customer portal (see below).

By clicking on the “CIMConnect Video Library”, you can see full set of available training videos and access them via this table:

The material is organized by topic, such as Collection Events or Status Variables. Each topic is subdivided into one or more instruction parts. When there is a lab, the implementation of the lab is covered twice. First, the implementation of the lab is reviewed and demonstrated in CIMConnect’s “Getting Started” sample application. Second, the lab is implemented step by step from scratch in a new application.

A few of the training PowerPoint presentations are not yet complete but should be available soon. This includes topics like Remote Commands, Equipment Constants, Factory Setup, and Operator Interface. Solutions Engineering plans to expand the training to other products as well.

Also, note that other videos are also available that go beyond the scope of the training material. These are found on the same “CIMConnect Video Library” page at the bottom.

Customers are welcome to purchase CIMConnect training and/or consulting services at any time. The training material described above is not a substitute for working directly with a product and standards expert, where a customer can discuss specific equipment hardware, software architecture, and unique customer requirements. Nevertheless, this material should help our customers when they need a refresher course and especially when new employees are assigned to work with CIMConnect after its initial development.

Next week, the world will once again gather at the industry’s leading trade fair for electronics development and production. The 2021 productronica and SEMICON Europa shows are open for in-person attendance and Cimetrix Incorporated will definitely be there. We hope to see many of you there as well!

The 2021 productronica show is co-located once again with SEMICON Europa in Munich, Germany from November 16-19 at the Messe München expo center. With safety protocols in place, Messe München is opening its doors to welcome the decision-makers and thought leaders from the industry to participate in showing new and innovative products and solutions spanning the entire value chain.

The Cimetrix booth will be in Productronica halls, where we will demonstrate our Smart Factory platform Cimetrix Sapience, to show how we can help with your smart manufacturing connectivity and application needs. Sapience, a cloud-native platform, features a distributed, scalable architecture for managing high-performance data pipelines from electronics equipment. As such, it is ideal for streaming analytics, machine learning, and artificial intelligence applications requiring data directly from the production equipment.

We are also highlighting an open position for a Munich-based Field Applications Engineer at the productronica career center and here on cimetrix.com.

We invite you to drop by our booth at productronica #437 in Hall A3, or you can make an appointment ahead of time by clicking the button below. We look forward to seeing you soon!

Cimetrix CIMControlFramework™ (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.

See previous series of blog posts on the functionality of CCF here.

While CCF does provide a built-in interface to handle GEM300 messages, CCF can be used just as effectively for building back-end and electronics equipment control applications handling the movement of chips and trays rather than wafers and carriers.

To demonstrate this ability, Cimetrix has added a continuous flow back-end sample as one of the fully working implementations provided with CCF. If you are already familiar with CCF, you will have seen the front-end Atmospheric and Vacuum cluster tool samples.

The continuous flow sample is different from these other samples as described below.

JEDEC input and output trays

For the Atmospheric and Vacuum samples, material is delivered as wafers in SEMI E87 carriers. For back-end and electronics markets, material is usually not in the form of a wafer and is not delivered in a carrier. For the Continuous Flow sample, the material is delivered on input trays and removed from the system on output trays. All trays used in the sample are similar to JEDEC trays, standard-defined trays for transporting, handling, and storing chips and other components. The trays have slots that can hold material in rows and columns. A JEDEC tray may appear as follows:

The Continuous Flow sample allows users to specify the number of rows and columns in a tray using configuration parameters. The sample has two input trays and two output trays.

Continuous Flow

As the name of the Continuous Flow sample indicates, material is continually processed until there is no more material or until the user tells it to stop. The sample does not use SEMI E40 Process Jobs or SEMI E94 Control Jobs to determine how material is processed. Rather the user selects a recipe to use during processing and presses the Start button. Material will continue to be processed until the Stop button is pressed.

By default, the Continuous Flow sample will process all material from the first input tray and then all of the material from the second input tray. When an input tray becomes empty, the empty tray will be removed and replaced with a full one. Similarly, when an output tray becomes full, it is automatically removed and replaced with an empty one. This allows the processing to run continuously until stopped.

Scheduler

The Continuous Flow sample scheduler is different from the schedulers in the Atmospheric and Vacuum samples in that it is not dependent on Process Jobs or Sequence Recipes to know how to move material through the system. It simply picks the next input material and places it in the first available process slot. It then picks the next completed material and places it in the first available output slot.

Visualization

A new visualization was created for the Continuous Flow sample. Rather than using round material, SEMI E87 carriers, load ports, and wafer handling robots, the new visualization draws rectangular material that looks like chips that may arrive in JEDEC trays. Rather than trying to render a robot, the visualization renders a circular end effector that moves material through the system. The following screenshot displays what the sample visualization looks like while processing.

In an upcoming version of CCF, the components of this visualization will be included in a visualization library that users can employ to customize their visualization more easily than has previously been possible in CCF.

Remote Commands

The Continuous Flow sample comes with three fully implemented remote commands that allow a host or host emulator to run the continuous flow sample. These commands are:

• PP_SELECT – Specify the recipe to be used for processing material.
• START – Start material processing using the selected recipe.
• STOP – Don’t introduce new material to be processed and stop after all processed material has been sent to output trays.

The following shows the S2F49 remote command body for selecting the recipe as sent from Cimetrix EquipmentTest.

Conclusion

We hope that the new Continuous Flow sample in CCF allows those who are creating semiconductor back-end or electronics equipment control solutions a great starting point for creating their applications. Please contact Cimetrix for additional information by clicking the button below.

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.