Semiconductor Industry News, Trends, and Technology, and SEMI Standards Updates


Alan Weber: Vice President, New Product Innovations

Alan Weber is currently the Vice President, New Product Innovations for Cimetrix Incorporated. Previously he served on the Board of Directors for eight years before joining the company as a full-time employee in 2011. Alan has been a part of the semiconductor and manufacturing automation industries for over 40 years. He holds bachelor’s and master’s degrees in Electrical Engineering from Rice University.
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Recent Posts

Models in Smart Manufacturing Series – Introduction

Posted by Alan Weber: Vice President, New Product Innovations

Mar 24, 2017 11:30:00 AM

As a child I was an avid model builder—airplane models, trains, engines, cars, ships, even monsters (anyone remember “The Visible V8” and “The Creature”?)—anything I could get my hands on. At the time I didn’t reflect on the source of this fascination, but with the benefit of hindsight, it is clear that these models provided an interactive, tangible way to visualize, explore, understand, and enjoy the topics that were interesting to me. It was a way to enrich an otherwise intellectual activity.


In fact, when Hurricane Carla ravaged the Texas coast and cut our electricity for 3 days, one of our luckier neighbors snaked an extension cord over the fence, which provided just enough power to run the refrigerator, a small black-and-white TV, and… you guessed it… my electric train. 


More than four decades later, I still enjoy working with models, but in the high-tech manufacturing domain, they often operate in the reverse direction, providing a logical way to interact with and understand physical entities, like materials, fixtures, processes, devices, components, equipment, and entire systems. And as important as various model types have been throughout the relatively brief history of the semiconductor industry, they are increasingly an integral part of the “Smart Manufacturing” initiative that is sweeping a wide range of industries worldwide. 

The focus of my next few blog posts will be the specific models that are inherent in the communications interface definitions for manufacturing equipment, subsystems, and other devices that are expected to cooperate over the [Industrial] Internet of Things. Our first post in this domain almost a year ago introduced the notion that the metadata models called for in the latest generation of SEMI Equipment Data Acquisition (EDA) standards were already directly aligned with the Industry 4.0/Smart Manufacturing vision. This series goes into much more detail, showing how specific sections of the equipment models in the GEM and EDA standards directly support many of the factory monitoring, analysis and control applications that are essential for running a Smart Manufacturing enterprise (see Substrate Management example below).


Moreover, to the extent that the structure and content of these models can truly be standardized, their associated applications can be process- and supplier-independent, greatly reducing the development and support costs for the factory IT departments while providing useful capabilities for the production engineering and operations stakeholders.

To get a feel for the overall direction of this series, download the presentation "The Role of Models in Semiconductor Smart Manufacturing",  along with the transcript,  from the APC Conference held last October in Phoenix. Then watch for subsequent postings that address specific applications, from productivity (OEE) monitoring, material tracking, product traceability, process execution monitoring, and beyond.

We look forward to your feedback and to sharing the Smart Manufacturing journey with you.

Topics: Equipment Models, Industry 4.0, Smart Manufacturing

EDA Testing – How is this accomplished today??

Posted by Alan Weber: Vice President, New Product Innovations

Feb 7, 2017 1:30:00 PM

Over the past several months, we have posted a number of blogs dealing with the testing of SEMI’s Equipment Data Acquisition (EDA / aka Interface A) standards suite. The first of these posts connected the importance of this topic to the increased adoption of the EDA standards across the industry, and broke the overall problem domain into its three major components. 

Subsequent postings provided additional detail in each of these areas:EDA_Icon.png

To bring this series to a close, this post addresses the “as-is” state of EDA testing as it is practiced today by the advanced semiconductor manufacturers who are requiring EDA interfaces on new equipment purchases and the suppliers who provide that equipment. 

For compliance testing, the three options in general use include: 

  1. ECCE Plus product- this software tool was originally developed under contract with the International Sematech Manufacturing Initiative (ISMI) to validate the fidelity, usability, and interoperability of early versions of the standard; it can used to manually execute a set of procedures documented in the “ISMI Equipment Data Acquisition (EDA) Evaluation Method for the July 2010 Standards Freeze Level: Version 1.0” document (see title page below) to exercise most of the capabilities called for in the standard; note that this is the only commercially available solution among the three.


  1. Company-specific test suites – one major chip manufacturer (and early adopter of EDA) maintains its own partially-automated set of compliance tests, and provides this system to its equipment suppliers as a pre-shipment test vehicle. This set of tests is then used in the fab as part of the tool acceptance process; however, this system also includes a number of company-specific automation scenarios, which are not available for outside use. This highlights the need to support custom extensions in an industry-validated tester if it is to be commercially viable.

  2. In-house custom test clients – this is a variation of #2 that some of the major OEMs have chosen as their economies of scale dictate; the problems with this approach are that a) the test clients must be kept current with the EDA standards, which are themselves a moving target, and b) unless thoroughly validated by the eventual customers of the equipment, there is no guarantee that passing these tests will satisfy the final acceptance criteria for a given factory. 

For performance and stability testing, there are no automated solutions currently available. The ISMI EDA Evaluation Method does describe some rudimentary performance evaluation procedures, but these no longer reflect the expectations of the customers with many years of accumulated EDA production experience. Clearly a better solution is needed.

Finally, for metadata model conformance testing, the only available solution is the Metadata Conformance Analyzer (MCA) that was commissioned by Sematech and implemented by NIST (National Institute of Standards and Technology). It has not been updated in almost five years, and exhibits a number of known issues when applied to a SEMI E164-compliant equipment model (E164 = Specification for EDA Common Metadata), so it will be increasingly insufficient as more companies require full Freeze II / E164 specification compliance. 

The good news in all this is that Cimetrix has recognized and anticipated this emerging need, and is actively addressing it on our product roadmap. If you want to know more about EDA testing and/or discuss your specific needs, please contact Cimetrix for a demonstration of this exciting new capability!

Topics: Interface A, EDA, EDAConnect, ECCE, Data

EDA Compliance Testing – Scope and Approach

Posted by Alan Weber: Vice President, New Product Innovations

Oct 19, 2016 11:30:00 AM

In a recent blog posting we introduced the topic of EDA (Equipment Data Acquisition) standards testing and sub-divided the domain into three parts:

  • Compliance testing – does the equipment adhere to the specifications described in the SEMI Standards?

  • Performance and stability testing – does the equipment meet the end users’ performance and availability specifications?

  • Equipment metadata model conformance testing – does the equipment model delivered with the interface represent the tool structure and content anticipated by the end customer?

Today’s post deals with the first of these parts in greater detail.

To begin, we should point out that standards compliance testing is not a new idea – it has been an integral part of the acceptance testing process for automated manufacturing equipment for decades. As each new generation of SEMI’s communications standards (SECS-II, GEM, GEM300, and now EDA / Interface A) reached critical mass, the compliance testing process naturally evolved from an ad hoc, manually driven set of procedures to a more thorough, formal process supported by automated testing software. Moreover, the use of this kind of software and the reliance of leading chip makers on its results has greatly contributed to the efficiency of the overall new fab startup and initial yield ramp process, so its importance to the industry cannot be overstated.

So where does the industry turn for information about how to test for EDA standards compliance?Although the Sematech manufacturing consortium’s R&D program no longer includes SEMI Standards definition, validation, and promotion support, the work that its ISMI subsidiary (International Sematech Manufacturing Initiative) did in the formative years of the EDA standards is still directly applicable. In particular, the “ISMI Equipment Data Acquisition (EDA) Evaluation Method for the July 2010 Standards Freeze Level: Version 1.0” is the globally accepted approach for checking compliance of an equipment’s EDA interface.


This document takes an automation test engineer through the entire set of steps for connecting a tool to a known-compliant test client (in this case, the Cimetrix Equipment Client Connection Emulator, or ECCE Plus product), adding entries to the interface’s Access Control List (ACL), uploading and inspecting the equipment metadata model, managing Data Collection Plans (DCPs), and invoking all the other services defined by the SEMI EDA Standards suite (E120, E125, E132, E134, E164, etc.). Its appendices not only define the required procedures in detail, they also describe the expected results and suggest a format for reporting these to interested stakeholders.

Of course, those familiar with the use of this method and the associated software tools know that it can take 2-3 days to execute this process manually, which is an inefficient way to check compliance for the incoming tool set of an entire fab. Fortunately, there IS a better approach. Cimetrix has automated these evaluation procedures in a way that ensures the target equipment meets the automation software purchasing requirements to the satisfaction of both the equipment supplier and the semiconductor manufacturer, while leaving the door open for factory-specific requirements that represent unique competitive advantage.

Note that ISMI and its member companies also recognized that much of the potential value of the EDA standards would be derived from (and limited by!) the content of the equipment metadata model, so they funded the development of another software tool to check these aspects of a supplier’s implementation. But that is a topic for an upcoming blog – watch for it.

So… if you want to know more about EDA testing and/or discuss your specific needs, contact Cimetrix for a demonstration of this exciting new capability!

Alan Weber
VP, New Product Innovations
Cimetrix Incorporated

Topics: Interface A, EDA

EDA Testing – What Does the Problem Look Like for the Industry?

Posted by Alan Weber: Vice President, New Product Innovations

Oct 4, 2016 11:15:00 AM

Anticipating and promoting the increased adoption of SEMI’s Equipment Data Acquisition (EDA / aka Interface A) standards, we’ve posted a number of blogs over the past 12 months to address questions that potential stakeholders have repeatedly asked across the value chain. These postings have dealt with everything from the factory applications enabled by EDA to the best practices for OEM implementation of these standards to the development of robust equipment purchasing specifications.

Since the adoption process has now clearly reached critical mass, we must seriously address the question “How are we going to test the equipment and systems that incorporate these standards?” in a way that supports the entire industry. It’s an excellent question, and one that has a multi-part answer.


Given the structure and expected use of the EDA standards, the acceptance testing process for a unit of semiconductor manufacturing equipment will include at least three components, each of which addresses a different aspect of the standards. Note that we’re explaining this from the perspective of the end customer in a semiconductor factory, since this is the most common use case, but most of the same principles apply when testing EDA client infrastructure/application components as well.

  • Compliance testing – does the equipment adhere to the specifications described in the SEMI Standards, and were these specifications interpreted correctly? Will it cleanly connect to the EDA client infrastructure without modification or extensive configuration?

  • Performance and stability testing – does the equipment meet the end users’ performance and availability specifications in terms of data sampling intervals, overall data volume transmitted, size and number of DCPs (data collection plans) supported, demands on the computing/network resources, up-time, etc.? Will it support the range of application clients expected in a production environment?

  • Equipment metadata model conformance testing – does the equipment model delivered with the interface represent the tool structure and content anticipated by the end customer? If the customer has requested that SEMI E164 (EDA Common Metadata) be fully supported, does the metadata model meet these specifications?

Of course, in addition to the requirements dictated by the standards themselves, most advanced semiconductor manufacturers will have a number of factory-specific requirements that must also be supported by the EDA interface. These may include special events and data for particular automation schemes, vectors of process parameters to support fault detection applications or other feature extraction algorithms, synchronization signals for external sensor integration, and the like. To address these requirements efficiently, an EDA test system should be extensible by its users.

You can see how interesting and vital this topic becomes when you consider the range of requirements outlined above. We’ll explore each of these in more detail in the next few postings, so stay tuned!


Topics: Interface A, EDA

Realizing Industry 4.0 with SEMI Standards: Right Here and Now

Posted by Alan Weber: Vice President, New Product Innovations

May 6, 2016 1:00:00 PM


Since the concept was first articulated in 2011 by a German government-supported program promoting deeper integration of manufacturing software and hardware across the production value chain, the term “Industry 4.0” has gained recognition and momentum as the rallying cry for the 4th industrial revolution (see left). Wikipedia  summarizes it like this: “Industry 4.0 facilitates the vision and execution of a ‘Smart Factory.’ Within the modular structured Smart Factories of Industry 4.0, cyber-physical systems monitor physical processes, create a virtual copy of the physical world, and make decentralized decisions. Over the Internet of Things, cyber-physical systems communicate and cooperate with each other and with humans in real-time…”

This definition may lead you to ask “What aspects of Industry 4.0 are truly revolutionary, and what technologies and tools are available today that would enable me to start building “Smart[er] Factories?” In this blog, I offer some potential answers to these questions that put the vision of Industry 4.0 within reach for automation practitioners familiar with the latest generation of SEMI Standards.  


Semiconductor manufacturers have been collecting and using data from the equipment in their factories for decades. Throughout this period, device sizes and process windows have shrunk continuously according to Moore’s Law, and the SEMI Standards have evolved by necessity to support the insatiable demand for data exhibited by the process analysis and control applications that keep a modern fab running profitably (see left). The newest of these standards, the Equipment Data Acquisition suite (EDA, also known as “Interface A”), provides the power and flexibility to support a wide range of critical manufacturing applications and human users with ever-changing requirements; moreover, these standards can be deployed in a variety of system architectures without disturbing the “command and control” capabilities of existing factory systems.

“What does all this have to do with Industry 4.0?” To understand this, let’s look at the foundation of a “Smart Factory,” the collection of the many thousands of devices that might need to communicate over the so-called “Internet of Things.” 

We already see evidence that the availability of low-cost, low-power, networkable computing hardware will likely result in an explosion of “smart sensors” and other intelligent devices on the factory floor. However, as social scientists have observed over the millennia, groups of smart individuals don’t necessarily exhibit smart behavior in the aggregate, so what additional attributes must these devices possess to be good citizens of a collaborative, Industry 4.0 environment? How will these devices communicate effectively with one another? And what oversight will be required to ensure this communication achieves the ultimate manufacturing objectives?

As a starting point, I propose that each device, or manufacturing “thing,” at a minimum should be discoverable, autonomous, model-based, self-aware, communicative, and well-behaved. Depending on the role the device must play, it might also be self-monitoring, capable of defending itself (secure), and a consumer of data from other devices/systems as well as a provider. So defined, these devices would need a minimum of external monitoring and supervision (read “management overhead”) to perform their basic functions, but would rely on higher-level systems to provide specific objectives, instructions, and constraints (read “configuration, recipes, and limits”) for their operation in a given context and timeframe.

I realize that’s a lot to absorb at once, but now imagine that each of these devices could implement a subset of the services called for in the EDA standards, especially those defined in E120/E125/E164 (equipment modeling and standard metadata modeling), E132 (session management), and E134 (data collection management). Consider the collaboration among independent devices and systems this would enable…and ask yourself, how much closer to the vision of Industry 4.0 can you possibly get?

I hope the ideas above were useful…or at least thought-provoking. We’ll be developing this theme further in the coming months, but I wanted to use this blog as a conversation starter. We’d love to hear your feedback, so give us a call, or feel free to reach out to us.

Topics: SEMI, EDA, IoT

OEM EDA Implementation Best Practices

Posted by Alan Weber: Vice President, New Product Innovations

Apr 26, 2016 1:00:00 PM


With all the recent news of increased EDA (also known as “Interface A”)adoption, especially in Asia, this is the perfect time to highlight the “Top 10 Best Practices” that semiconductor manufacturing equipment suppliers can follow in planning and executing their implementations of this important suite of standards. As we’ve said in previous blog postings and other EDA-related material, it is best to take a long-term view of your EDA interface design, independent of what a particular semiconductor manufacturer’s automation specifications may initially require. In so doing, you can be certain your implementation will satisfy all future EDA requirements, enabling your control system software team to focus on the features that truly differentiate your equipment from that of your competitors. The information in this posting can give you a running start on this process. It’s important to note that the “best practices” summarized below are the culmination of many years of EDA standards definition, related software product development, and manufacturing production experience among the early adopters. As such, most of them could support a dedicated blog posting, so watch for these in the coming months. In the meantime, if you’re interested in more specifics, please contact us.

1. Build a useful equipment model

First and foremost, since the content of the “equipment metadata model” is effectively the data collection “contract” between the equipment supplier and the factory users, your customer’s ultimate satisfaction with the EDA interface depends on the content and structure of this model. The role most affected by this model is the process engineer, so the equipment component, variable, event, and exception names should match the tool documentation, and the logical hierarchy should mirror the actual hardware structure.

2. Consider non-functional requirements

System performance expectations change over time, and, as a result, the equipment automation requirements may not include sufficient or up-to-date detail in this area. Therefore you must document your assumptions about the performance of your interface in terms of maximum sampling rate, average number of parameters per data collection plan (DCP), total bandwidth required (e.g., 20,000 parameters per second), and other factors important to the customer. In addition to performance, these will include scalability, availability, flexibility, extensibility, and ease of use, among others.

3. Define robust system architecture

The architecture of an EDA interface is greatly affected by the non-functional requirements mentioned in number 2 above, in addition to the specific capabilities required by the SEMI standards. One way to ensure these requirements can be met is to separate the EDA interface software from the equipment controller. In other words, run it on a different computer dedicated to the interface. Moreover, stick with the services and protocols defined by the standard – don’t be tempted to implement custom extensions that will only apply to a specific customer or client application, as this just increases your future support costs.

4. Choose platform with extra “headroom”

Computer hardware is inexpensive compared to the cost of downtime and support, so choose a platform that has room to grow. Based on many years of production experience, Cimetrix can provide specific guidelines in terms of CPU speed, number of cores, memory, disk, and other system attributes. Note that you may also be expected to upgrade these platforms in the field as the standard and/or customer requirements evolve, so plan accordingly!

5. Implement E164 common metadata standards

The E164 “EDA Common Metadata” standards likewise incorporate equipment modeling best practices from many early EDA implementations, so you should consider these as a required baseline for your equipment model, whether or not the first EDA customer calls for them in the automation specs. It is actually easier to do this when developing a new EDA interface than it is to come up with a separate set of structural and naming conventions, but it can be very difficult to implement later. (Note that we have had a number of previous blog postings on this topic.)

6. Use equipment modeling tools

Since typically 75% of the interface development and maintenance time is spent dealing with the content and behavior of the equipment model, these tasks are a perfect candidates for [at least partial] automation via model creation/editing tools and associated “wizards.” These tools should be able to generate an E164-compliant baseline model to which process-specific information can be added naturally. Moreover, if possible, use the resulting tool configuration files to create models programmatically, which will greatly reduce support costs over time.

7. Provide complete visibility into equipment behavior

The principal motivation expressed for EDA adoption by the factory operations people across the industry is “better understanding of equipment/process behavior.” Therefore, to satisfy this need, equipment suppliers should provide as much information as possible about key process variables/events/exceptions, and all the underlying mechanisms (sensors, actuators, I/O, low-level fault conditions) that affect them. Also make sure the E157 “steps” (recipe step-level transition events) are visible and meaningful to enable the kind of fine-grained condition-based trace data collection required by leading-edge fault detection, run-to-run control, and predictive analytics applications. Apply the principle “when in doubt, include it” – your customers will thank you.

8. Build in “hooks” for field service support

An EDA interface can be valuable for your own field support team if the proper “hooks” are included in the model from the outset. These capabilities range from a simple “sniff test” (Is the interface up and running?) to complete recent history of the platform’s operating conditions and the EDA clients’ demands on the interface. An explicit logging strategy should also be defined and documented to enable the factory customers to do their part in getting you the information required for prompt, one-pass success in support situations.

9. Develop thorough test plans and use them

In addition to the range of test techniques expected for mission-critical software (unit, system, regression), EDA interfaces should be subjected to performance and stability testing as well. Most customers will also require standards compliance and other acceptance tests to be run, and results provided before and after delivery of the equipment. Where possible, industry accepted packages are preferable for this purpose.

10. Use proven commercial software


Last, but not least, you should heed the advice of race car drivers, test pilots, and stunt men who regularly caution their audiences “Don’t try this at home!” The related message for interface developers is that the EDA standards, while mature and well documented, are complex, moving targets that require significant expertise, time, and effort to understand and implement reliably. For most equipment suppliers, this resource is far better spent building features that differentiate the equipment, and relying on companies with proven track records to provide off-the-shelf interface software products that minimize both time-to-market and project risk.

We sincerely hope this material is useful to you, and feel free to contact us for more information.

Topics: Interface A, EDA, Equipment Models

European Advanced Process Control and Manufacturing Conference XVI in Review

Posted by Alan Weber: Vice President, New Product Innovations

Apr 19, 2016 2:01:05 PM



Cimetrix participated in the recent European Advanced Process Control and Manufacturing (apc|m) Conference, along with more than 130 control professionals across the European and global semiconductor manufacturing industry. The conference was held in Reutlingen, Germany, a picturesque city of stone and half-timber buildings just south of Stuttgart.


This conference, now in its 16th year, is one of only a few global events dedicated to the domain of semiconductor process control and directly supporting technologies. The conference’s attendance this year was comparable in numbers and demographics to that of the previous two years, a clear indication that this area continues to hold keen interest for the European high-tech manufacturing community. Another highlight this year was the sponsorship of Bosch, a relative newcomer to the conference but a pillar of the German manufacturing industry. Reutlingen is home to Bosch’s automotive electronics division and its related semiconductor manufacturing facilities, so they were very well represented in the conference and excellent hosts!

Cimetrix was privileged to make two presentations at this year's conference. The first was entitled “Data Fusion at the Source: Standards and Technologies for Seamless Sensor Integration,” authored and delivered by myself. The external sensor integration and related data unification topics have enjoyed increasing interest over the past year, and even though the techniques outlined in the presentation leverage the latest versions of the Equipment Data Acquisition (EDA)/Interface A standards, they apply equally well for the 200mm manufacturing nodes prevalent in European wafer fabs and assembly/test factories. The solution architecture is shown in the slide below, but for the background and rationale behind this approach, feel free to download a copy of the entire presentation from our website by clicking on the link below.


  Download the Presentation


The second presentation, entitled “'Smart Manufacturing' solutions for high-mix manufacturing using Wait-Time-Waste improvement opportunities” was authored by Jan Driessen, a Principal Industrial Engineer with NXP Semiconductor in the Netherlands. It summarized the work of a project team from six companies and as many countries, and funded by the European Union's “integrate” program (cover page is on the left). Because of an unexpected work conflict during the conference, however, Jan was unable to attend, and, based on our companies’ shared interest in the Wait-Time-Waste technology and standards over the past several years, he thought that Cimetrix would be well qualified to give his presentation. I willingly agreed, worked with Jan to make sure I understood the latest material, and made the presentation. It essentially makes a compelling case for using equipment event data in a legacy 200mm fab to improve OEE, operational effectiveness, and factory capacity through a “chain of data operations” paradigm that he explains in some detail. The good news for 300mm fabs is that these same results can even more readily be achieved, because the availability and fidelity of the event data is much higher, especially if the fab has a full GEM300/EDA E164-compliant system infrastructure. For more information, request a copy of this presentation directly from Jan Driessen at

Other themes that were evident at the conference included 1) applications of APC and supporting metrology techniques for structures found in smart sensors, MEMS devices, LEDs, and other semiconductor products outside the traditional processor and memory segments; 2) increasing emphasis on equipment data collection in the back end to support productivity monitoring and control applications; 3) unit process control for a number of equipment types; and 4) an entire session devoted to industrial engineering topics.

As with other similar conferences around the globe, the takeaway for Cimetrix is that “Smart Manufacturing,” Industrie 4.0, the Industrial Internet of Things (IIoT), advanced process control and fault detection applications, “big data” analytics, and a host of other high-tech manufacturing technologies all depend on the ability to get the right data at the right time from the right sources on the factory floor, and then make it available wherever and whenever needed… For more information about how Cimetrix’s product families that directly address this “sweet spot,” please contact us.

Topics: SEMI Standards, Interface A, EDA, Events, Data

EDA Instructional Video Library Now On-Line!

Posted by Alan Weber: Vice President, New Product Innovations

Mar 22, 2016 1:00:00 PM



The long-awaited set of EDA informational videos is now available on the Cimetrix website. They can be found at the link found underneath the SEMI Standards tab on our home page.

The target audience for these videos includes anyone who is curious about the origins and vision behind the EDA standards; semiconductor factory operations people who want to know how these standards might provide real manufacturing benefit; automation/IT staff who need a refresher about the content of the standards themselves and alternatives for incorporating them in the factory’s data collection infrastructure; and, finally, purchasing people who must understand how to create robust requirements specifications for their equipment suppliers.

All the videos are roughly 6-8 minutes long, so we’ve tried to make it easy to address your individual interests. To this end, the first ten videos cover a wide range of technical and commercial topics grouped into the following three categories:

  1. What is EDA?

  2. Why is EDA important?

  3. How do I buy/build an EDA solution?

More will be added over time as the topics of interest to the semiconductor manufacturing automation community evolve, but we invite you to have a look today to see what’s there. And if English (or Texan!) is not your native language, or you want to review written versions of this material, the transcript for each video can be downloaded via the link below each video. Moreover, if your company infrastructure does not support direct viewing of video content, please contact us so we can make alternative arrangements to deliver this material.

Finally, if you have a suggested topic you would like to know more about, please let us know. We may have a presentation already available, or a video demonstration underway that can answer your question. And if not…we’ll make one!

Topics: Interface A, EDA

Equipment Data-Driven Continuous Improvement for 200mm Fabs

Posted by Alan Weber: Vice President, New Product Innovations

Feb 23, 2016 1:03:00 PM


The focus of the most recent SYSTEMA Expert Day, held during a snowy week in Dresden in late January 2016 in conjunction with the 13th annual innovationsforum, was “200mm Fab Enhancement” and featured a number of presentations from Systema GmbH customers and partner companies.

By way of background, there are a number of reasons for the emphasis on 200mm fab enhancement, most notably that many of these factories are enjoying a renaissance of business to meet the growing demands for IoT (Internet of Things) devices. Moreover, since the drivers for this market segment include cost, variety, and volume, the automation and operations people in these factories are faced with a new combination of challenges not seen in earlier markets.

Cimetrix’ contribution to the event was a presentation titled “Equipment Data-Driven Continuous Improvement for 200mm Fabs,” which outlined a model-based, ROI-driven approach for adding equipment data collection capabilities to existing factories. Our basic premise is that such an approach helps meet some of the automation challenges in an incremental, cost-effective way without requiring major redesign of the factory or equipment control systems.


Since the term “model” is used in many different contexts, we first clarified what this term means in the context of SEMI equipment communications standards, and how this evolved over the past three decades. This was accomplished using a natural language analogy, which is shown in the figure below. Note that the culmination of this process to date is the EDA (Equipment Data Acquisition) metadata model called for in the latest generation of standards, which is very prescriptive in terms of structure, content, and naming conventions for the elements of a semiconductor manufacturing equipment. And even thought the specifics of this model were designed with 300mm wafer fab equipment in mind, the principles well apply to all substrate sizes, and even to the types of material, processes, and equipment found in back end assembly and test factories.

After establishing the value of explicit models for representing equipment, sensors, and other key items in a manufacturing environment, we next introduced concept of an ROI-driven strategy for evaluating the relative benefit of various data collection projects. This strategy first identifies and ranks the key manufacturing objectives that must be addressed, then poses the questions that must be answered to meet those objectives. It then identifies the data sources for the information required to answer those questions, and the data collection techniques (including software) applicable to those sources. Finally, since the original objectives can change with time and additional knowledge, they should be re-examined periodically, giving the strategy an iterative aspect as well.

In order provide specific examples for the uses of equipment data in a continuous improvement program, the presentation listed a number of application use cases that have been successfully deployed in 200mm facilities. These included (in general increasing order of complexity) substrate tracking, process execution tracking, product time measurement (aka wait time waste analysis), external sensor integration, component fingerprinting, and product traceability.


A couple of these were then explained in more detail, showing how a basic tracking application could start by using a small subset of the equipment data, and then evolve over time to provide more advanced functions (and benefit!) as more detailed information was made available.

For those who want to understand this process in more depth, you are welcome to download the entire presentation using the link below, or call us to discuss how we can apply these ideas to your company!

“Equipment Data-Driven Continuous Improvement for 200mm Fabs"

Watch the Video

Topics: Semiconductor Industry, Market Trends, EDA, SYSTEMA GmbH, Data Collection

Manufacturing Applications for Leveraging a Factory-wide EDA Implementation

Posted by Alan Weber: Vice President, New Product Innovations

Dec 16, 2015 8:52:49 PM

In our November EDA-related blog, I covered highlights of the Factory System Infrastructure topic shown in the figure below, and emphasized the need to have a long-term architectural vision to guide the development of a scalable data collection and management environment. Today’s topic completes the picture by summarizing the kind of Manufacturing Applications that can leverage a factory-wide EDA implementation. Unlike infrastructure software alone, these applications are what really provide the ROI for the process engineers and other factory customers of the manufacturing IT department’s efforts, so it is important to understand the scope and requirements of these key applications early in the strategic planning process.


Even though Cimetrix is principally in the business of providing software products that enable equipment suppliers to provide data using EDA technology to the factory application developers that use the information in their production systems, we’ve been involved in this process for many years, and have a good idea of the dominant uses of this data to improve manufacturing Key Performance Indicators (KPIs). So in this blog, I’ll cover a little of the high-level picture of what applications fully leverage EDA data.

First and foremost, it is very easy to connect a basic EDA client to a piece of equipment, upload its metadata, and collect information about that tool’s behavior, so implementing a generic “quick-connect production monitor” independent from the fab-wide data collection system is a very common use for EDA. Moreover, if the model in the tool is compliant to the E164 (EDA Common Metadata) standard, you can make a lot of assumptions about the names of the modules, the wafers, the substrate locations, the process jobs, etc., since all of this information is standardized. As a result, you can quickly get an idea of what the equipment is doing, what recipes it is running, what wafers are being processed, and how well the tool is performing with no custom software whatsoever.

Once this is accomplished, the next step most process and equipment engineers take is to more fully characterize the tool’s behavior, so a very common use of EDA is simply improving equipment and process visibility. By inspecting the equipment model, you can see all the events and parameters that are available to be collected, plot them in Excel or on real-time strip charts, or pass them to other analysis applications.

After the equipment has been characterized, the first major production application most fabs will implement is multivariate fault detection (MVA FDC). This is actually the predominant application of EDA data in the industry to date, because in order to do well-architected fault detection applications, one must “frame” the trace data very carefully. High-speed data collection is usually only required in a small number of specific recipe steps after certain conditions have been established, so you can use EDA’s powerful event-based trace data collection to frame the precise data you want, and pass that on to the multivariate control and fault models.

Of course, once you understand a tool’s behavior and have good fault detection capability, you then start to use EDA data to compare tools across a fleet. You would normally want a set of similar equipment to behave in the same way, but perhaps you have one tool that performs exceptionally well, and you’re not quite sure why…In this case, you do what’s called a “golden run” analysis on that equipment, and compare the key trace variables in one with like variables in similar equipment to see where the differences are, and try to explain why those differences exist. Other names for this class of applications include chamber matching and tool matching.

Another key application that we’re starting to see significant interest in is external sensor integration. Factories are now starting to use EDA to present information collected from independent sensors alongside the information collected directly from the equipment. Sharing a common equipment model across these systems effectively “unifies” that data, so the downstream analysis applications believe the information was collected from a single, integrated source. The EDA metadata model offers an ideal way to accomplish this unification.  

Finally, in many advanced wafer fabs, it is important that substrates do not “sit around” after they’ve been processed. Minimizing inter-process wait times is especially important for some advanced processes, so knowing a priori—the precise moment that a lot is going to complete—is a critical capability so the material handling systems can be scheduled to pick up that material and take it to the next process. EDA provides an ideal way to make these predictions generically for multiple process types using the information that is required in the equipment model.

We’ll address these last two applications—external sensor integration and lot completion estimation—in more detail in later blog postings, but I wanted to get you thinking about these ideas early in the discussion of real EDA usage in semiconductor factories.

There are many more EDA application ideas and examples we could share at this point, from component fingerprinting to wait-time waste analysis to dynamic sampling for wafer-level feedback control to feature extraction for predictive maintenance…but these just scratch the surface of what factory customers will come up with once they experience firsthand the flexibility and power of EDA in their factories. More later as this creative process unfolds!

To schedule a time to discuss your EDA needs, click here to set-up a time to talk with one of our knowledgable experts.

Topics: SEMI Standards, Interface A, EDA, Doing Business with Cimetrix

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Interface A/EDA