Industry News, Trends and Technology, and 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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All You Could Ever Want to Know about Smart Manufacturing – in a Two-Volume Treatise

Posted by Alan Weber: Vice President, New Product Innovations on Aug 26, 2021 1:15:00 PM

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.

Smart-manufacturing-two-volume-image

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!

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Topics: Industry Highlights, Smart Manufacturing/Industry 4.0, Machine Learning

Machine Learning in Smart Manufacturing: Technologies You Should Be Tracking Now

Posted by Alan Weber: Vice President, New Product Innovations on Jun 3, 2021 11:45:00 AM

shutterstock_510171997-1We’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.
machine-learning1.1

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.

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Topics: Industry Highlights, Smart Manufacturing/Industry 4.0, Machine Learning

18th Innovation Forum for Automation: Successful Transition of an Important Tradition

Posted by Alan Weber: Vice President, New Product Innovations on Mar 17, 2021 1:45:00 PM

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

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

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

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

18-innovations-forum-pic1The second event in the series was hosted by Fabmatics on February 25, 2021, with the enticing theme of Forever Young: Automation Makeover Rejuvenates Golden Age Fabs. It featured speakers from Bosch, Nexperia, Cohu, and SYSTEMA.

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

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

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Topics: Industry Highlights, Events, Smart Manufacturing/Industry 4.0

Thinking Ahead: Why would I want to buy EDA client libraries for my equipment?

Posted by Alan Weber: Vice President, New Product Innovations on Nov 11, 2020 11:30:00 AM

Background and Audience

Over the past several years, I have written numerous blog postings heralding the benefits of the SEMI Equipment Data Acquisition (EDA, also known as Interface A) standards, promoting their adoption by 300mm wafer fabs around the world, explaining how to develop robust purchase specs to ensure the interfaces delivered by the equipment suppliers meet the fab customers’ expectations, describing how the various components of the standards work together and the importance of the embedded equipment model, and finally explaining how to run compliance and performance tests on an EDA interface to validate its fitness for production use. The target audience for most of these postings has been the factory users, for they are the ones who increasingly depend on detailed equipment and process data to profitably run their enterprises.

By contrast, this posting is aimed at the equipment suppliers who are looking to increase the value of their product families by augmenting their hardware offerings with software capabilities that only they are uniquely qualified to provide.

This is not a new idea. Several major equipment suppliers have offered so-called “Equipment Engineering Systems (EES)” products as companions for their equipment over the years, providing applications like Fault Detection and Classification (FDC), production monitoring, maintenance management, local repositories for diagnostics and field support, and other capabilities that leveraged deep domain knowledge of the equipment. However, these systems necessarily relied on private interfaces to the equipment for their data, such as an additional network connection, direct access to the file system, or other mechanisms. And from the fab’s perspective, these constituted yet another piece of infrastructure to maintain.

Now there’s EDA: a key enabler for value-added equipment applications

Since the SEMI EDA standards are inherently multi-client, a single EDA interface can support not only the factory information and control systems that depend on equipment data, it can also provide whatever information a supplier-specific application may need as long this data is represented in the equipment metadata model. Since that model is designed by the equipment suppliers as a fundamental component of the EDA interface, they can choose to put as much information in these model as they want, possibly well beyond that required by the fab customers’ purchase specifications. In fact, these models could be used to implement the diagnostic logging capability that suppliers usually build into their equipment for their own use, but without requiring custom software to read and interpret that information. See the figure below for an example of such a configuration.

EDA-Equipment-1The EDA standards also include a provision for “built-in DCPs” (DCP = Data Collection Plan) which can be shipped with the equipment and protected from accidental deletion at the factory site. These DCPs could be crafted by the equipment supplier to directly feed whatever value-added applications the supplier chose to develop, whether these resided on a computer local to the equipment in the fab, on portable computers used by field service engineers to diagnose problems, or on remote cloud-based systems allowed to connect via secure EDA-defined URLs. This flexibility opens up a wide range of application types, from those that embed equipment-specific algorithms to generic Machine Learning frameworks… the possibilities are endless.

What all these approaches have in common is a standard EDA client capability that can establish a session with the equipment, activate Data Collection Plans, and receive the ensuing Data Reports. The Cimetrix EDAConnect product provides all these features and more in a lightweight set of .NET libraries which can be deployed wherever they are needed to consume EDA data.

Conclusion

More and more semiconductor factories are requiring EDA interfaces with their new equipment purchases with highly prescribed equipment models and demanding performance criteria. From the equipment supplier’s perspective, these requirements have been viewed as a source of additional cost, with all the benefit accruing to the factory customers. But it doesn’t have to be that way…

Why not take advantage of this interface to offer additional value using a standards-based approach? This just might be an idea whose time has finally come. If you agree, give us a call – we can help you make it happen!

Topics: Industry Highlights, Semiconductor Industry, EDA/Interface A, Doing Business with Cimetrix, Standards

Semiconductor Backend Processes: Tracking Process Execution

Posted by Alan Weber: Vice President, New Product Innovations on Sep 30, 2020 11:45:00 AM

Background

semi-e157-pic1

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

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

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

SEMI E157 – Process Module Tracking

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

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

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

E157-pic1This model represents the state of that portion (or portions) of a unit of equipment that executes a recipe to transform whatever material is present in that part of the equipment. In front end equipment, the chambers are relatively distinct, and usually process a small number of substrates (often one) at a time. By contrast, backend processes cover a broad spectrum of material types, from single wafers to strips (or lead frames) of multiple die to individual packages. The material flow characteristics also vary, from discrete (i.e., single workpieces) to batch to continuous. Moreover, the production rates and material volumes for these processes range from perhaps 90 wafers per hour to thousands of packages per hour… With these challenges, it is no wonder that the pace of automation for these facilities has lagged that of the front end.

How is the E157 Standard Used?

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

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

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

How Might E157 be Adapted for Backend Equipment?

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

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

Which Backend Equipment Types Should Implement E157?

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

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

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

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

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

Conclusion

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

Topics: Industry Highlights, Semiconductor Industry, Smart Manufacturing/Industry 4.0, GEM300

Are you now required to work from home? Don’t let it cripple your EDA-related activities!

Posted by Alan Weber: Vice President, New Product Innovations on Mar 25, 2020 1:15:00 PM

WFHEDA1The COVID-19 pandemic is impacting businesses worldwide, and in many regions, working from home is now mandatory or at least strongly encouraged.

While this doesn’t pose a major disruption for many types of jobs, it can be problematic for people working with the automation features of advanced manufacturing equipment. The network connections to production equipment are normally part of a secure factory system infrastructure, which makes them almost impossible to reach from outside the company’s intranet. Luckily, for those responsible for testing and characterizing the SEMI EDA (Equipment Data Acquisition, also known as Interface A) interfaces on new 300mm equipment, this should only be a minor inconvenience. And why is that?

The choice of internet technologies (Web Services, SOAP/XML) as the foundation for the EDA standards makes it easy to connect to a piece of equipment over the internet as long as the user’s client computer can “reach” the connection URLs of the equipment (and vice versa). What this probably means in practice is setting up a VPN (Virtual Private Network) connection from your client computer (say, the laptop you normally use) to the company’s network. This is something that road warriors and remote employees must often do as a matter of course to access internal file systems, in-house applications, and other private information.

Once this is done, you can connect to the various service URLs for that equipment by including the remote computer name in the session connection strings. Note that you may have to modify the firewall settings of your client machine so the E134 NewData messages can find their way back to you. This is necessary because these are NOT request/reply messages like many of the EDA services; rather, they are initiated from the equipment, so your application has to be listening for them on the Consumer URL. This address is passed to the equipment when the connection session is first defined and established.

Using the Cimetrix ECCE Plus client product as an example, here is how I would set up a remote (from home!) session with an EDA-enabled 300mm equipment simulator running in our office on a machine named “edasimulator.” The first screenshot shows the choice of connections defined for my instance of the ECCE Plus; note that last one in the list that is highlighted.WFHEDA2png

Clicking on the “Edit Session Definition” button and then the “More >” checkbox yields the screen below. You can see that the equipment IP address is “edasimulator” (the remote computer name referenced above) and each of the Freeze II service URLs (E132 Location, E125 Location, and E134 Location) for the session are defined on that machine.WFHEDA3

Note that the client ID (From/Client Name), which is “MyHomeTestClient,” must also be defined in the equipment’s Access Control List (ACL). For me to be effective, this client must have sufficient privileges for the kinds of work I need to do, which may include using existing DCPs (Data Collection Plans), creating additional DCPs, viewing interface configuration parameters (e.g., Max Sessions) and ACL entries, browsing the metadata model, and looking at the SOAP logs. Results of some of these tasks using the ECCE Plus are shown below.WFHEDA4WFHEDA5pngWFHEDA6WFHEDA7png

This may sound like a lot of trouble, but with a little help from your company’s IT support team, you can follow the “shelter in place” guidelines and STILL work effectively on your EDA-related tasks. And when the current crisis has passed, you’ll know how to be even more effective when you’re on the road!

We hope the posting is useful for you, and most importantly, that you and your loved ones stay safe and calm.

Topics: Industry Highlights, EDA/Interface A, Customer Support, Partners, Doing Business with Cimetrix

Advanced Process Control Conference XXXI:  Retrospective and New Standards News

Posted by Alan Weber: Vice President, New Product Innovations on Nov 11, 2019 9:15:00 AM

APC2019-1The 31st annual APC Conference is now in the history books, and the diversity of topics, presenters, and local distractions made it well worth the visit to San Antonio! This year’s agenda featured half-day tutorials on the basics of APC and cyber-security, keynotes from chip makers and leading suppliers on automotive industry requirements, smart equipment, and smart manufacturing, and a series of packed technical sessions covering sensors and equipment control, fault detection and feedforward/feedback control, advanced analytics, and standards.

One of the presentations in the standards session provided detailed information about the new SEMI SMT-ELS (Surface Mount Technology Equipment Link Standards) M2M (machine-to-machine) communications standard. Alan Weber made the presentation titled “SEMI Standards to Support APC for Post-Fab Operations” to an interested audience, which triggered a number of discussions about the automation roadmap for the semiconductor assembly and test segment. This was especially relevant, since some of the leaders of the newly formed SEMI Advanced Backend Factory Integration Task Force (ABFI TF) were also present.

APC2019pic2The SMT-ELS standard has come a long way in a short time, and the ambitious, integrated demonstration created by 4 major SMT suppliers (Fuji, Juki, Panasonic, Yamaha) that was exhibited in June (Japan) and August (China) will again be shown in productronica (Munich, 13-15 November). The basic functions of SMT-ELS (officially designated at SEMI A1, A1.1, and A2) appear in the figure below.

APC2019pic3Cimetrix will likewise demonstrate this new standard at productronica, showing not only an equipment-level implementation of the M2M features but also the host-based configuration process and a plug-in for doing protocol validation tests.

Smart Manufacturing was a common theme this year, with an entire session dedicated to this global initiative. The Factory Integration section of the IRDS (International Roadmap for Devices and Systems) will be reorganized around the tenets of Smart Manufacturing, and a two-volume multi-industry book on this body of technology is scheduled for publication early next year. Another of Alan Weber’s presentations was dedicated to this topic, as he wrote the chapter chronicling the semiconductor industry’s development and use of these technologies.

APC2019pic4If you would like any further information, you can speak with a Cimetrix expert, or you can stop by our booth at productronica this week (Hall A3 booth 451). 

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Topics: Industry Highlights, Doing Business with Cimetrix, Events, Smart Manufacturing/Industry 4.0

Advanced Process Control Conference XXXI: Preview and Invitation

Posted by Alan Weber: Vice President, New Product Innovations on Oct 10, 2019 11:00:00 AM

The 31st annual APC Conference is coming up later this month (October 28-31), and will be held at the Embassy Suites Riverwalk in the scenic and historic setting of San Antonio, Texas.

sanantonioThis conference is one of the longest-running events specific to the semiconductor manufacturing industry, and always features speakers and topics that are germane to the industry’s leading practitioners of equipment/factory data collection, analysis, optimization, and control.  This year’s agenda promises more of the same – click here for a closer look at the details.

Specifically, as the automotive industry’s use of semiconductors continues to grow in anticipation of self-driving cars and their supporting infrastructure, the first keynote address from Steve Frezon of NXP Semiconductors (“Automotive Semiconductor ZERO DEFECT Enablement”) highlights the challenges that automotive customers place on the wafer fabs. A second keynote by Dr. Ben Rathsack of Tokyo Electron America (“SMART Tools: Intelligent Controls in Semiconductor Manufacturing”) focuses on the implications of the global Smart Manufacturing initiative for equipment suppliers, which has been a consistent theme of the conference under a variety of monikers since its earliest days.

The rest of the Technical Sessions Agenda includes presentations, posters, and exhibits across the semiconductor value chain: sensor and subsystem providers, software suppliers, equipment manufacturers, universities, standards organizations, and semiconductor IDMs and foundries. Given the importance of equipment connectivity and control across the product and technology spectrum of these companies, Cimetrix will participate directly as it has for many years. Alan Weber, VP of New Product Innovations, will present a summary of a chapter (“Semiconductor Smart Manufacturing: An Evolving Nexus of Business Drivers, Technologies, and Standards”) in a soon-to-be-published 2-volume book on Smart Manufacturing. He will also present material jointly developed 1) with SK hynix on customer-driven automation requirements development, and 2) with SEMI Japan, Applied Materials, and others on new standards for flow shop style manufacturing, such as semiconductor back end and PCB assembly.

Central Texas can be a beautiful place to be in late October, so mark your calendars today and plan to join us in San Antonio!

Topics: Doing Business with Cimetrix, Events, Smart Manufacturing/Industry 4.0

EDA Best Practices Series: Specifying and Measuring Performance and Data Quality

Posted by Alan Weber: Vice President, New Product Innovations on Aug 1, 2019 12:14:00 PM

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

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

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

Scope of Performance and Data Quality Requirements

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

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

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

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

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

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

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

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

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

Measurement and Testing

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

Examples of such requirements include:

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

This approach is shown in tabular form for a 2-chamber tool (see below); since Group Size does not (or should not) impact the effective parameters per second rate, it is not shown in the table.edabest-measure-1
  • A summary report for all performance tests that show acceptable message generation and transmission timing across the full range of data collection test criteria must be available.
  • Detailed SOAP logs for specific performance tests must be available on request.

In Conclusion

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

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Topics: Industry Highlights, EDA/Interface A, Doing Business with Cimetrix, Smart Manufacturing/Industry 4.0, Cimetrix Products, EDA Best Practices

New SEMI Standards for Flow Manufacturing Automation Demonstrated at JISSO PROTEC!

Posted by Alan Weber: Vice President, New Product Innovations on Jun 26, 2019 10:59:00 AM

Jisso-ProtecCimetrix attended the recent JISSO PROTEC exhibition (June 5-7, 2019) at the Tokyo Big Sight International Exhibition Center to see the latest developments in SMT (Surface Mount Technology) manufacturing… and witnessed a truly compelling demonstration of the new SEMI Flow Manufacturing communications standards in action.

Jisso-1The new suite of standards is named SMT-ELS (Surface Mount Technology-Equipment Link Standards), and includes SEMI A1/1.1 as a lower-level messaging standard with SEMI A2 SMASH (Surface Mount Assembler Smart Hookup) defining the content of the messages required to configure an SMT manufacturing line and automate the material and information transfer among all equipment in that line. This is depicted in the figure below.

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The demonstration itself included placement equipment from 4 large equipment suppliers—Fuji, JUKI, Panasonic, and Yamaha—as well as load/unload stations and a bar code reader at the beginning of the line (see picture below). Each of these companies had implemented the “horizontal” (machine-to-machine) communications according to the SMT-ELS standards. The demonstration consisted of an operator scanning one of the stack of input boards with the barcode reader, placing it on the loader conveyor, and then watching as each piece of equipment automatically adjusted its internal conveyor to accept the board, run through its part placement recipe, and pass the board to the next equipment in the line, finally arriving at the unload station conveyor after a minute or so.

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Before a fully automated multi-vendor production SMT line can be implemented, more work on the standards is necessary, especially in the area of error handling and recovery. In addition, the suppliers of other (non-placement) equipment types must adopt this approach. However, given the factory benefit of mixing equipment from multiple suppliers to optimize line performance for a specific set of products, this is only a matter of time.

If you want to know more about the status and outlook of these standards, and how they can be implemented in your equipment or factory, please contact us.

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Topics: Industry Highlights, Events, Global Services, Smart Manufacturing/Industry 4.0, SMT/PCB/PCBA