Industry News, Trends and Technology, and Standards Updates

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.

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 16^th 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.

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 jan.p.driessen@nxp.com.

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.♦

The long-awaited set of EDA informational videos is now available on the Cimetrix website. They can be found at the http://www.cimetrix.com/EDA-In-Depth 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!♦

The focus of the most recent SYSTEMA Expert Day, held during a snowy week in Dresden in late January 2016 in conjunction with the 13^th 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"

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.♦

In my October 27th blog, I wrote about the Equipment Automation topic shown in the figure below and stressed the importance of developing good equipment purchasing specifications from the outset to ensure the company’s manufacturing objectives can be met. Given the number of EDA pilot and production projects currently active across the industry, it’s likewise important to consider what kind of Factory System Infrastructure will be necessary to support a full-scale EDA deployment… so the purpose of this posting is to highlight this topic for the semiconductor manufacturing IT professionals who may face these challenges soon.

However, before diving into a detailed design process for an EDA factory system, you must decide what overall system architecture will govern that design. A number of factors go into this decision, including 1) the functional requirements that distinguish EDA-based data collection from other more traditional approaches, 2) technology constraints of the existing factory systems, 3) budget limitations, 4) schedule requirements, and especially 5) the non-functional requirements (scalability, performance, reliability, ease-of-use, etc.) that often make the difference between success and failure of a given system.

Each of these factors deserves a thorough treatment of its own, but since we were invited to address this topic at a recent seminar sponsored by SEMI Taiwan, we’ve assembled an overview presentation entitled “Factory Systems Architectures for EDA” that you can use as a starting point. It not only covers in more depth the requirements above which drive key architectural decisions, but also suggests what some of the major architectural components of a production system would need to be, based on the experience Cimetrix has gained working with the earliest adopters of EDA across the semiconductor device maker and equipment supplier communities. These include provisions for handling the scores of equipment metadata models that will exist in a production facility, for creating and managing the thousands of data collection plans that are resident at the equipment instances themselves, for monitoring and maintaining the overall performance of a system with such inherent flexibility, and for a number of other examples. Finally, the presentation describes some high-level examples of architectural “styles” that have been implemented in the industry thus far.  

We sincerely hope you will download this presentation and its companion “The Power of E164: EDA Common Metadata” that was also presented at the SEMI Taiwan event, and contact us when you want to know more about any of these topics.♦

The past few months have been full of news regarding adoption of the SEMI EDA (Equipment Data Acquisition, or Interface A) suite of standards by a number of major semiconductor manufacturers, which has quickly rippled throughout the supplier community as equipment makers and software providers alike consider the implications for their product road maps and support teams.

The range of EDA-related projects now underway covers the full spectrum from basic experimentation with this new data collection paradigm on a couple of tools to integration of these data sources with factory-level predictive analytics platforms to full factory pilot deployments. And although the specific requirements for the equipment suppliers involved in these projects may seem to be very different at the outset, eventually the fab purchasing departments will set a target for EDA interface functionality and performance that is high enough to support their automation requirements for the next 5-7 years.

What this means in practice is that even though the current specifications may be fairly loose regarding which “Freeze” level (I or II) is called for and what the equipment metadata model structure and content should be, eventually the factory customers will fully understand the benefits they can achieve by requiring the latest versions of the entire EDA suite (especially the E164 Common Equipment Metadata standard), and will revise their specifications accordingly. For this reason, it makes perfect sense for equipment suppliers to plan for this level of implementation now rather than waiting for it to be required across the board. This is especially true for suppliers who are just beginning their EDA implementations, as it is no more difficult to build a Freeze II-/E164-compliant interface using today’s EDA development software products than a less capable interface.

From the factory customer usage perspective, it is important to consider the distinguishing features of the EDA standards when developing new purchasing specifications. In particular, unlike SECS/GEM, much of the EDA interface capability is dictated by the structure and content of the equipment metadata model, so this must be carefully designed to ensure that the automation requirements and performance expectations are clearly communicated. This may in turn require more direct involvement of the process, equipment, and industrial engineering teams than ever before.

Moreover, when communicating these requirements to the OEM community, it is useful to explain what strategic manufacturing objectives are being addressed and what the information will be used for; this context helps the suppliers understand why the specs may include a high level of detailed information, and can shift a potentially adversarial negotiation process more towards a teaming relationship based on shared objectives.

Fortunately, Cimetrix has many years of experience not only working in the EDA standards development process, but also providing products and services to the early fab adopters of this technology and most of their equipment suppliers. As a result, we have developed a pro forma set of EDA implementation guidelines which can easily be adapted to a specific factory’s purchasing requirements, and are happy to work with you to evaluate your needs and apply this process.  

For more information about developing good EDA purchasing specifications, check out the video we’ve prepared at the link below – and then let us how we can help!

Cimetrix participated in the recent Advanced Process Control (APC) Conference in Austin, Texas, along with more than 120 control professionals across the semiconductor manufacturing industry. This conference, now in its 27^th year, is one of only a few global events dedicated to the domain of semiconductor process control and directly supporting technologies, so it was encouraging to see its attendance and energy level rebound from its low water mark a few years ago. The calendar may have indicated it was fall, but nobody told the weather forecasters… Austin set temperature records that week, even hitting 99°F one day!

Given the importance of high quality equipment data for all types of equipment- and factory-level process control applications, it is vital that Cimetrix and its customers understand the current requirements and future direction of this industry segment. Many presentations addressed these topics indirectly, but perhaps the newest insights in this regard came not from the wafer fabrication processes, but rather from the Back End, OSAT (Outsourced Assembly and Test), and advanced packaging segments.

As evidence, a number of presenters mentioned the growing need for equipment data collection in these areas, and cited the following reasons: 1) increasing demands by the consumer product manufacturing customers of these facilities (especially smart phone providers, but others as well) for equipment data to support their product quality and supply chain optimization initiatives; 2) emphasis on the Overall Equipment Effectiveness (OEE) productivity metrics, and the event/status data needed to support their automated calculation; 3) broader deployment of multi-variate Fault Detection and Classification (FDC) applications, which require more equipment trace data parameters than have typically been collected from back end equipment; and finally, 4) actual feedback control based on back end metrology – the best example of this presented last week was an application on dicing equipment that showed how kerf data collection and analysis can be used to adjust saw process parameters

The takeaway for Cimetrix in all this is that the back end equipment suppliers will need to anticipate this demand and may need to upgrade their interface capabilities substantially.

Since some of Cimetrix’ customers have pioneered the application of the latest generation of SEMI EDA (Equipment Data Acquisition) / Interface A standards in plumbing data from external “add-on” sensors to fault detection applications, I presented a generalization of this approach during one of technical sessions. This presentation, “Data Fusion at the Source: Standards and Technologies for Seamless Sensor Integration” is available on the Cimetrix website for those who want to learn more about how this is done.

Check back next week to learn more about creating good EDA/Interface A purchasing specifications.

As mentioned briefly in a previous posting, the adoption momentum for the SEMI EDA (Equipment Data Acquisition) suite of standards has picked up noticeably over the past 6 months, and a number of pilot projects are now underway at leading chip makers across the industry, especially in Asia. As these projects bear fruit, we expect to see explicit requirements for EDA interface capability in the purchase specifications of many more fabs in the coming months. But that’s just a start.

The early adopters of these standards who have now accumulated years of production experience clearly understand that the key to realizing the full manufacturing benefit of this technology lies in the structure and content of the equipment metadata models, which to date have been largely determined by the equipment suppliers themselves. The resulting diversity of EDA implementations is reminiscent of the situation that existed in the days before GEM, when every chip maker required their own particular “dialect” of SECS-II, and the equipment suppliers had to support a custom interface for each customer… not a pretty picture.

Luckily, the standards community recognized this problem early on, and addressed it via the Specification for EDA Common Metadata (SEMI E164). This standard effectively unifies the equipment models across the fab, regardless of process type or supplier, enabling the factory software developers to create generic manufacturing applications that “plug and play” with the equipment to address the problems that are common to all (status and productivity monitoring, material flow, resource utilization, etc.). 

As a result, the next wave of factory implementations can directly leverage these lessons learned by requiring compliance to “Freeze 2, E164” level of the EDA standards suite, and focus their energies on new application development rather than supplier-specific custom integration software. Given the years of experience Cimetrix has dedicated both to the development of the EDA standards in the SEMI community and in providing product-based implementations on “both ends of the wire” (in other words, equipment and client/host side), we can support customers wherever they are in the implementation life cycle, from building awareness to initial purchase specification development to system architecture and application design to conformance and acceptance testing.

For more information about how we can help align your activities with this accelerating adoption process, please contact us… and stay tuned for more specifics on all the above!

For an introduction to EDA, download the presentation Interface A Overview: Characteristics, Benefits, and Applications.

The 2015 SEMICON Taiwan was held at the Taipei Nangang Exhibition Center in the Nangang District of Taipei City, Taiwan September 2-4.  The event drew over 30,000 visitors from all over the world for the three-day event with nearly 1,500 exhibits from a diverse array of companies and organizations across the semiconductor industry.

Cimetrix exhibited at SEMICON Taiwan trade show for the first time, in conjunction with its newest Asian partner, Flagship International.

This turned out to be the 20^th Anniversary of SEMICON Taiwan, so the mood was especially buoyant and the attendance brisk. SEMI also commemorated the event with a Gala Dinner at the Grand Hyatt on Wednesday night, and we were privileged to attend courtesy of our Flagship colleagues.

The president of Taiwan, Ma Ying-jeou, even made a guest appearance to thank the semiconductor industry for its contribution to Taiwan’s economic health. 

Shortly after the exhibition opened on Wednesday (Sept. 2), a couple of visitors from UMC showed up, and shared the latest work they’ve been doing with the Wait Time Waste (WTW) concepts that we had presented two years earlier. C.Y. Tiao and James Lin of UMC even made two presentations at the eMDC conference on this topic. 

From a standards perspective, this has now taken the form of SEMI E168, Specification for Product Time Measurement (PTM), and has been defined at the “time element” and supporting GEM/SECS message level for process equipment, automated material handling systems (AMHS), and material control systems (MCS). With the advent of SEMI E164 (EDA Common Metadata), these concepts are especially easy to implement, because all the events necessary to calculate the full suite of time elements are required by standard… but more on this is a later blog!

Since much of the world’s foundry capacity is in Taiwan, the equipment industry was well represented at the show, which included many of Cimetrix’ current customers as well as a few local prospects. As a result, Dave Faulkner and Kerry Iwamoto had a chance to visit a number of these firsthand.

Another highlight of the week for Cimetrix was participation in the eMDC (e-Manufacturing & Design Collaboration Symposium), where I made a presentation entitled “Data Fusion at the Source: Standards and Technologies for Seamless Sensor Integration” that addresses the challenges faced by process engineers in effectively using data from an increasing number of sources to analyze process behavior.

The basic idea is to handle the association of necessary context information (lot, substrate, product, layer, process, recipe, step, chamber, etc.) with the raw collected data as close to the source as possible, using a single, integrated model of the equipment and all related data sources. The equipment model that forms the foundation of the EDA/Interface A standards serves this purpose perfectly.

On a final related note, in visiting and talking with a number of the leading chip makers during the week, it seems that the adoption momentum for EDA is now building steadily, so we look forward to supporting this initiative across the value chain. Stay tuned for a late September post that will shed more light on this process!