Industry News, Trends and Technology, and Standards Updates

Cimetrix participated in the recent European Advanced Process Control and Manufacturing (apc|m) Conference, along with over 160 control systems professionals across the European and global semiconductor manufacturing industry. The conference was held in Dresden, a beautiful city in the Saxony state of Germany which was the site of the original European conference in 2000 and host to this annual event many times since.

This conference, now in its 18th year and organized by Silicon Saxony, is one of only a few global events dedicated to the domain of semiconductor process control and directly supporting technologies. The participants represented all links in the semiconductor manufacturing value chain, from universities and research institutes to component, subsystem, and equipment suppliers to software product and services providers to semiconductor IDMs and foundries across a wide spectrum of device types to industry trade organizations – something for everyone. 

As usual, the conference was very well organized, and featured a wide range of high-quality presentations, keynote addresses, and tutorial sessions. 

Highlights of the conference included the following:

• “FDC to the power of 2 – how it got us to the next level of manufacturing excellence“ by Jan Räbiger of GLOBALFOUNDRIES – one of a number of long-time thought leaders in the development and application of APC technology, Jan described the latest phase of FDC system evolution, which includes broad use of the EDA/Interface A standards to zero in on recipe step-specific anomalies that had previously escaped detection.

• “Applying the Tenets of Industrie 4.0 / Smart Manufacturing to Microelectronics Next Generation Analytics and Applications“ by James Moyne (University of Michigan / Applied Materials) – James presented a very nice decomposition of the domain into 6 topic areas (Big Data Environment, Advanced Analytics and Applications, Supply Chain Integration, CPS/IIoT, Cloud Computing, Digital Twin) and explained our industry’s relative status and recommended actions in each. one of the conclusions from his very disciplined treatment of the topic is that “Smart Manufacturing is essentially a connectivity problem” – and we couldn’t agree more!

• “Lithography Control is Data Hungry” by Tom Hoogenboom of ASML – his illustration of just how precise litho metrology has become was brilliant: controlling exposure and registration at the 5nm node on a 300mm substrate is like moving your chair in the conference meeting room by 1 mm and having an airborne observer of a 300km diameter region know it happened!

Finally, as in many prior years, Cimetrix was privileged to present at this conference, as Alan Weber delivered a talk entitled “EDA Applications and Benefits for Smarter Manufacturing.” This presentation described the potential use of SEMI EDA (Equipment Data Acquisition) standards to improve the performance and benefit of a range of manufacturing applications; it also included a specific ROI case study for the use of EDA in the all-important FDC (Fault Detection and Classification) application to reduce the false alarm rate and the severity of process excursions. If you want to know more, you can request to view a copy of the entire presentation.

However, it wasn’t all work and no play… The local sponsors, GLOBALFOUNDRIES, Infineon, and XFAB, hosted the conference banquet at the picturesque Adam’s Gasthof in the nearby city of Moritzburg.

In addition to all the food and libation one could possibly consume, the participants were feted with a torchlight walking tour of the town and its iconic Moritzburg Castle. All in all, German hospitality and history at its best.  

The insights gained from these and the other 30+ presentations are too numerous to list here, but in aggregate, they provided an excellent reminder of how relevant semiconductor technology has become for our comfort, sustenance, safety, and overall quality of life.

This (apc|m) conference and its sister conference in the US are excellent venues to understand what manufacturers do with all the data they collect, so if this topic piques your interest, be sure to put these events on your calendar in the future. In the meantime, if you have questions about any of the above, or want to know how equipment connectivity and control fit into the overall Smart Manufacturing landscape, please contact us!

In the third article of this EDA Applications and Benefits in Smart Manufacturing series, we highlighted the first of a series of manufacturing applications that leverage the capabilities of the EDA / Interface A suite of standards in leading semiconductor manufacturers. In this fourth article, we’ll highlight the application that has been the principal driver for the adoption of EDA across the industry thus far, namely Fault Detection and Classification (FDC).

Problem Statement

The problem that FDC addresses is the prevention of scrap that may result from processing material by an equipment that has drifted out of its acceptable operating window for whatever reason. The prevalent technique used by today’s leading FDC systems is to develop “reduced dimension” statistical fault models for the various production operating points based on training sets of “good” and “bad” runs. These models are then evaluated in real time with key parameters (usually trace data) collected from the equipment during processing to detect process deviation and predict impending tool failure. In the most advanced fabs, the FDC software is deeply integrated with the systems that manage process flow, and can even interdict equipment operation in mid-run to prevent/reduce scrap production. 

Of course, the challenge with this type of algorithm is developing models that are “tight” enough to catch all sources of potential faults (i.e., eliminating false negatives) while leaving enough wiggle room to minimize the number of false positives (also known as false alarms, or crying “Wolf!”). This in turn requires high quality data from the equipment, and lots of process engineering and statistical analysis expertise to develop and update the fault models for the range of production cases that must be handled. High-mix foundry environments exacerbate this situation.

Solution Components

The core of modern FDC systems is a robust multivariate statistics analysis toolbox, capable of handling large amounts of time series data. By “large,” we mean both the number of distinct equipment parameters and the number of samples for each parameter. These software tools collapse potentially hundreds of parameters into a small set of “principal components” that can be calculated on-the-fly using a limited set (say, 20-30) of equipment parameters. Some number of these principal components in aggregate represent the actual state of the process accurately enough to detect deviations from the norm, and since they can be realistically calculated in real time, the application serves as an on-line equipment health monitor.

The other major solution component for a production FDC system is a fault model library management capability that can handle large numbers of models. This is necessary because the multivariate approach includes little or no awareness of the physical meaning of the principal components (i.e., they are not “first principles” based), so different operating points for the equipment must have their own sets of fault models. The proper models for a given operating point are selected by matching the values of the “context parameters” for a specific run to those used to store the models. Even if some models can be shared across a range of operating points, the number of distinct models for a foundry megafab will still number in the thousands.

EDA (Equipment Data Acquisition) Standards Leverage

In an advanced fab, there is a spectrum of data collection alternative available for a given application, from basic lot-level summary information to detailed real-time data that can used at the substrate level or even on a die/site basis. For FDC, this spectrum of possibilities is shown in the table below.

SEMI Standard Level	Functionality	Benefit
GEM/GEM300	Fault models difficult to change after initial development if data collection requirements change	Baseline
EDA Freeze I (1105)	Easy to change equipment data collection plans as fault models evolve and require new data; Model development environment can be separate from production system	Engineering labor reduction; improved fault models and lower false alarm rate
EDA Freeze II (0710)	Use conditional triggers to precisely “frame” trace data while reducing overall data collection needs; Incorporate sub-fab component/subsystem data into fault models	Even better fault models; reduced MTTD (mean time to detect) of fault or process excursion; little or no data post-processing required
EDA Common Metadata (E164)	Include standard recipe step-level transition events for highly targeted trace data collection; Automate initial equipment characterization process by using metadata model to generate required data collection plans	Faster tool characterization and fault model development time
Factory-Specific EDA Requirements	Incorporate previously unavailable equipment signals in fault models; Update data collection plans and fault models automatically after process and recipe changes; Include recipe setpoints in the equipment metadata models	TBD (Not yet applicable)

 

The left column refers to the level of SEMI standards used to provide the necessary equipment data. The “Functionality” column describes how that data is used in an FDC context, and the “Benefit” column highlights the potential impact these functions can have. 

Let’s say that a fab implemented the capability described on rows 3 and 4 of the table (EDA Freeze II (0710) and E164-compliant EDA Common Metadata). In this case, the process equipment will be able to provide detailed process parameters at recipe step-specific sampling rates sufficient to evaluate “feature extraction” algorithms of even the most demanding FDC models…with context data to select the precise set of models for a given process condition. And even though the specific equipment parameters are necessarily process dependent, much of the software that monitors recipe execution events, generates the data collection plans (DCPs) that provide the trace data, and assembles the context data used by the model management library can be truly generic because of the fab-wide consistency of the equipment interfaces dictated by the E164 standards.

Another aspect of the EDA standards that FDC teams can leverage is the system architecture flexibility enabled by the multi-client capability. Even while a piece of equipment is connected to a production data management infrastructure, the process engineers and statisticians who develop and refine the fault models can use an independent data collection system tailored for process behavior analysis, experimentation, and continuous improvement. When the new fault models are ready for production, the production DCPs can be updated with these new requirements.

KPIs Affected

In the introduction to this series (posted December 19, 2017), we listed some of the manufacturing stakeholders whose work objectives are directly addressed by the applications we’ll highlight in this and subsequent postings. In the second article, we explained the process used to map the careabouts of key stakeholder groups into specific EDA interface requirements which are can then be directly included in the purchasing specifications. 

In this post, we’ll explain how some of those interface requirements support an important factory application that has general applicability across all equipment types, namely “real-time throughput monitoring.” This application can realistically work with a variety of equipment types with no custom code or configuration depending, of course, on how faithfully the equipment supplier implements the SEMI standards referenced in the requirements specification. This powerful concept greatly improves the software engineering productivity of a fab’s automation team, so we’ll take some time to explain how this is possible.

Problem Statement

This application addresses the problem of monitoring equipment throughput performance in real time, and raising an alarm when it drifts away from “normal” for any reason. This is especially important for bottleneck equipment (e.g., litho tracks and scanners), because any loss of throughput ripples throughout the line, resulting in lost production and its associated revenue and profit. Stated simply, “lost time on a bottleneck tool can never be recovered.” 

Solution Components

This application requires data that includes primarily the equipment events that chronicle the movement of substrates through the equipment and execution of the recipes appropriate for this equipment type (process, metrology, inspection, sorting, etc.). With this information, the application calculates the process time “on the fly” and compares the current value with the expected (“normal”) value. 

This is not as simple as it first may seem, because the expected value will likely depend on the product type, process type, material status, layer, recipe, and several other factors. Taken together, the set of factors that determines “equivalence” of different lots for some processing purpose is called “context.” For this application, the context parameters ensure that you are comparing apples and apples when looking for variations in process time.

EDA (Equipment Data Acquisition) Standards Leverage

By “EDA,” we include not only the standards in the Freeze II / 0710 suite, but also SEMI E164 (EDA Common Metadata), E157 (Module Process Tracking), and by reference, the entire GEM 300 suite. This ensures not only the granularity and breadth of event support necessary to precisely track wafer movement and step-level recipe execution, but also specifies the naming conventions of those events and their associated parameters, regardless of equipment type or vendor.

If the equipment automation purchase specifications include clauses that state “we require that all state machines, states, state transition events, and attributes of the objects defined in the referenced 300mm SEMI standards be implemented and named exactly as specified in the standards,” then all the information you should need to write a truly generic throughput monitoring application will be available on demand.

A robust real-time throughput monitoring algorithm can be implemented with information solely from the following SEMI standards: E90 (Substrate Tracking), E157 (Module Process Tracking), E40 / E94 (Processing / Control Job Management), and E87 (Carrier Management). The Harel state diagrams, events of interest, and EDA metadata model representation* for a couple of these (E90 and E157) are shown in the figures below.

Note that as little or as much of the parameter information required to be available for each event (the rightmost picture in each figure) can be collected via the EDA construct of a “Data Collection Plan” (DCP) with one or more “Event Requests.” For more information about these capabilities, consult the SEMI E134 (Data Collection Management) specifications directly, or review some of the extensive educational material available on our web site.

The other point of leverage for the EDA standards is the multi-client capability. This contributes to the productivity and responsiveness of your automation software team members by allowing them to collect and process the data for this application independently from any other application. Specifically, the throughput monitoring functions can be implemented separately from whatever systems host the GEM command and control capabilities, which are usually managed very carefully because of their potentially negative impact on fab operations.

Key ROI Factors

As we said in the initial post of this series, this application is not just something you could build and deploy with EDA-enabled equipment… in fact, this has already been done, and is delivering real production manufacturing benefit! Specifically, the ROI factors impacted (and benefit delivered) by this application include productivity excursion mean-time-to-detect (MTTD, 50% reduction), selected equipment throughput improvement (3-5%), and overall cycle time reduction (difficult to quantify precisely because of the staged implementation process). 

Of course, these results will vary depending on the manufacturer’s fab loading, operations strategy, and overall automation capabilities, but are representative for leading edge production wafer fabs running at near capacity. However, since these are very common ROI factors, most companies can easily quantify these improvements in real financial terms.

In Closing...

As always, your feedback is welcome, and we look forward to sharing the Smart Manufacturing journey with you.

*The visualizations of equipment metadata model fragments are those produced by the Cimetrix ECCE Plus product (Equipment Client Connection Emulator).

Let us know if you would like to schedule a meeting to learn more:

In the introduction to this series posted December 19, 2017, we listed some of the manufacturing stakeholders whose work objectives are directly addressed by the applications we’ll highlight in subsequent postings. Before getting into the specific capabilities and benefits of these applications, however, it seemed appropriate and timely to share a little bit about the process that the leading EDA practitioners use to ensure the equipment they are purchasing will support these applications. “Appropriate” because you may need to review and update your own purchasing specifications to clearly convey your requirements in the areas that are not directly covered by the standards (like model content, performance, and stability); “timely” because we at Cimetrix have recently conducted a number of customer workshops and seminars in which this process was effectively used and refined.

What do the factories DO with all that data?

Unlike the other postings in this series which deal with specific features and capabilities of the SEMI E30 GEM (Generic Equipment Model) standard, this blog identifies a number of the factory applications that depend on collecting data from the equipment.

Moreover, since we often hear the question “How do the factories actually use the different types of equipment information we’re expected to provide?” this posting will summarize the specific data required to support a number of these applications. This list is by no means exhaustive, but should give you an idea of the range of factory stakeholders whose objectives are supported by GEM data collection.The figure below illustrates the relationship between the Key Performance Indicators (KPIs), the factory stakeholders responsible for optimizing them, the applications used to achieve this, and data required by these applications.

The most effective way to share this kind of information is in tabular form. Within a group of related applications (e.g., scheduling, preventive maintenance), the applications are listed in generally increasing order of complexity, which is also the likely order of implementation by the factory applications development staff.

Factory Application	Equipment Data Required
OEE (Overall Equipment Effectiveness)	Transition events and status codes sufficient to classify equipment states for all time periods
Intra-equipment material flow	Material tracking events; material location state indicators and state change events
Process execution tracking	Start/stop events for all processing modules; recipe step indicators and step change events for all processing modules that support multi-step recipes
WTW (wait time waste) analysis	The combination of events required for the intra-equipment material flow and process execution tracking applications (see above) and context data required to classify material states for all time periods (see the SEMI E168 Product Time Management standard for a deeper explanation)
Time-based PM (Preventive Maintenance)	Run timers at the FRU (field replaceable unit) level
Usage-based PM	Usage parameters and accumulators appropriate for each FRU, such as time-in-state, execution cycles, fluid flow rates, consumables flow rates, power consumption, etc.
Condition-based PM	Meaningful “health indicators” for each FRU
FDC (Fault Detection and Classification)	Equipment/process parameters required by specific fault models and associated context information (this is difficult to do completely because most FDC models are “trained” with knowledge of “good” and “bad” runs, which is not known to the equipment supplier a priori)
Automated equipment interdiction	Remote stop command (e.g., issued by an FDC application sensing an existing or imminent fault)
Equipment configuration monitoring	Vector of important equipment constants with expected values and acceptable ranges; may need to support multiple sets, if the values are setup-dependent. Designed to catch human errors resulting from operator manual adjustments
Component fingerprinting	Performance parameters for key equipment mechanisms, including command/response signals at the sensor/actuator level
Static job scheduling	Setup and execution times per product/recipe combination and current setup information
Real-time job dispatching	Estimate of current job completion time; estimate of completion time for all material queued at the equipment
Factory cycle time optimization	Material buffer contents, job queue information
Operator notification	Notification codes for frequent operator actions in a non-/semi-automated environment, such as load/unload material, select/confirm recipe, provide manual “assist” if the equipment is stuck, etc.
Real-time dashboard	Equipment/component production status indicators
Equipment failure analysis	Meaningful alarm/fault codes and perhaps recent history/statistics
Run-to-run process control	Identification of recipe adjustable parameters and commands to remotely update them

 

To the extent that some of the application data described in the table above can be standardized across equipment types, there is an opportunity to create generic factory applications that would only require a mapping from the supplier-specific GEM IDs (collection event IDs, status/data variables, equipment constants, etc.) to their generic counterparts. But this is a topic for another posting on the concepts of “plug-and-play” in a GEM context.

We hope this explanation helps you appreciate how valuable equipment information is for the factories that consume it, and therefore how important it is to provide a rich set of events, variables, and other detailed information in the GEM interfaces you design in the future. 

Click here to read the other articles in our SECS/GEM Features and Benefits series. 

To download a white paper on an introduction to SECS/GEM, Click below:

With the adoption of the latest SEMI EDA (Equipment Data Acquisition, also known as Interface A) standards accelerating significantly over the past 18 months, it is time to highlight the applications across the industry that make the best use of these standards, and the specific manufacturing benefits that result.

The articles in this series are not simply suggestions of what one could do by leveraging the performance, flexibility, and architectural features of these standards. Rather, they are leading edge application-specific mini-case studies derived from actual production experience, and as such, can provide genuine guidance for those companies just now contemplating potential pilot projects or even factory-wide deployments of the EDA standards.
Another important aspect of this series is that the applications described affect a broad range of stakeholders in a semiconductor manufacturing company. These include, of course, the process control engineers and statistical modeling support staff responsible for the Fault Detection and Classification (FDC) implementation strategy in all modern wafer fabs, since this application has consistently been the initial consumer of the high-density, precisely framed equipment/process data and associated context information provided through the EDA interfaces.

However, other direct beneficiaries of EDA-enabled applications extend well beyond this group, and include:

• Industrial engineers responsible for monitoring equipment and factory throughput in real-time, identifying opportunities to eliminate wait time waste in individual equipment types as well as the overall factory, and addressing bottlenecks as they shift and emerge;
• Production control staff responsible for determining the material release schedule and managing the factory scheduling/dispatching systems to accommodate changes in customer orders and/or factory status;
• Equipment engineers responsible for fleet matching and management to minimize or eliminate the need to dedicate certain equipment sets for critical process steps and thereby simplify the overall factory scheduling process;
• Maintenance engineers responsible for minimizing equipment downtime, MTTR (mean time to repair), and test wafer usage required to bring equipment back to production-ready state;
• Facilities engineers responsible for collecting and integrating sub-fab data from pumps, chillers, exhaust systems, and other complex subsystems into the production data management infrastructure for use by a growing range of analysis applications;
• Sensor integration specialists responsible for supplementing the built-in sensing and control capabilities of critical process and measurement equipment to support advanced process development…

… and the list goes on.

Despite their diversity, these application articles all share a common profile, which includes a statement of the manufacturing problem addressed; a description of the major solution components required; a discussion of how the solution leverages specific, unique characteristics of the EDA standards; and finally, identification of the key ROI (return on investment) factors that are impacted by the solution. In addition, where available, example ROI calculations will be provided so that the readers can adapt them to their own company environments to quantify the potential benefit of implementing a comparable application solution.

From the above description, you may be tempted to assume that the series focuses mostly on the careabouts of semiconductor manufacturing companies (IDMs and foundries)… but this is not the case. Since the performance of the highlighted applications depends heavily on the “quality” (for lack of a better term) of the equipment interfaces supplying the data, the equipment suppliers have a major role to play in achieving the promised benefit. Specifically, the metadata models (specified by SEMI E120, E125, and E164) that define the parameters, states, events, exceptions, and other data available from the equipment and structure this information for external access essentially form the data collection “contract” between the equipment suppliers and their factory customers. For this reason, the detailed requirements for this aspect of the EDA implementation must be carefully specified and negotiated. This will not happen overnight, as the implications for future equipment design are significant.

As a number of industry experts have already expressed, it is an exciting time to be in the semiconductor industry, regardless of your position along the value chain. For those involved in the collection and use of equipment data to optimize factory performance, we hope you will find the coming series of articles especially useful in formulating you own company’s EDA implementation roadmap.

To view additional resources on EDA/Interface A or other topics, click on the resources link below.

As always, your feedback is welcome, and we look forward to sharing the Smart Manufacturing journey with you.

Over the past several months, we’ve highlighted the importance of explicit and standardized models in the context of equipment communications interfaces and some of the “smart” factory applications they support. The manufacturing stakeholders impacted by these applications run the gamut from process, equipment, maintenance, and industrial engineering to production operations to traceability regulatory compliance… yet these only scratch the surface.

The question you may be asking now is “So what?” or “What should I do with this information?” The answer to these important questions depends on your company and your role. 

For example, if you’re part of a semiconductor manufacturing enterprise in today’s market environment, you know that you can probably sell every good device you make, so there is intense pressure to simultaneously maximize product quality, volume, and [factory and engineering] productivity–a perfect storm. Since lead times for new equipment needed for capacity expansion are at all-time highs, this means getting as much as you can out of your existing facilities while waiting for new deliveries. New applications to monitor and improve these KPIs are being developed continuously, but the one thing they have in common is a reliance on detailed, high quality, easily accessible and interpretable equipment data.

For 300mm equipment with the latest generation of SEMI EDA (Equipment Data Acquisition) interfaces, this means having “good” E120/E125/E164-compliant equipment metadata models as a foundation. On top of this foundation, however, the models must also include the specific parameters, events, state machines, and other items that fully describe the behavior of the equipment according to your unique manufacturing requirements… which can only be achieved by mapping these requirements into specific equipment model elements, and updating your purchase specifications to close whatever gaps you find between what is currently offered by the equipment suppliers and what you really need. Fortunately, we have been through this process a number of times, and can help clarify your manufacturing priorities and translate them directly into updated interface purchase specifications.

Admittedly, this may take some time, but remember that you always only get what you are willing to accept. It brings to mind the old adage: “The best time to plant a tree was 20 years ago; the second 

best time is today.” 

As another example, if you are part of the embedded control system development team of an equipment supplier, you can anticipate not only increasingly explicit model content requirements, but also more stringent performance and testing requirements for the standard EDA communications interface as your customers raise their reliance on this technology to realize manufacturing competitive differentiation. We at Cimetrix have seen this demand build over the past 18 months, and are well prepared to support you throughout the entire equipment development life cycle.

This article is the sixth and final in the series announced earlier this year in the Models in Smart Manufacturing blog series. From here, we’ll soon begin a new series on advanced EDA applications and benefits based on best practices of the industry leaders – be sure to watch for this early next year!

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

 

*The visualizations of equipment metadata model fragments are those produced by the Cimetrix ECCE Plus product (EDA Client Connection Emulator).

Austin, Texas was the site of this year’s conference, going back to its roots after almost 30 years. Because of its unique focus on equipment and process control technology for the semiconductor industry, and the consistently high quality of its technical content, this conference continues to attract both industry veterans and newcomers to this domain, with this year’s attendance over 160.

Cimetrix has been a regular participant and presenter at this event, and this year was no exception. Alan Weber made a presentation entitled “ROI-based Approach for Evaluating Application Data Collection Use Case Alternatives” that was jointly developed with Mark Reath of GLOBALFOUNDRIES. The key message of this talk was that data collection should not be viewed as an all-or-nothing proposition but rather a spectrum of alternatives within which an approach can be chosen that best fits the problem to be addressed. As examples, the presentation described specific FDC use cases that resulted in significant savings through reduced false alarm rate and fewer/less severe process excursions. For a copy of this presentation, follow the link at the bottom of this posting.

Boyd Finlay’s (GLOBALFOUNDRIES) keynote presentation was undoubtedly one of the highlights of the conference. His presentation, “Raising the Bar: Foundry Expectations for Equipment Capability and Control,” painted a compelling picture of how future semiconductor manufacturing equipment must be able to support the growing demand for semiconductors in almost all aspects of modern life, especially in self-driving cars and their supporting infrastructure. For example, one of the specific expectations is that “Fab engineers expect fully integrated instrumentation on and around equipment to provide well established unambiguous high-volume manufacturing sensor supporting BKMs (best-known methods).” This presentation is well worth your time to review regardless of your job function in the industry, so follow the link below for a copy.

Samsung also offered some very interesting insights in a presentation titled “Wafer Level Time Control for Defect Reduction in Semiconductor Manufacture FABs.” It correlated defect densities to position in the FOUP and explained 2 sources for these: 1) outgassing of wafers after certain kinds of processes (which can be addressed with N2 purging), and 2) the difference in post-process waiting time, which must now be considered at the individual wafer level rather than the lot as a whole.

This conference and its sister conference in Europe are excellent venues to understand what manufacturers do with all the data they collect, so if this topic piques your interest, be sure to put these events on your calendar in the future. In the meantime, if you have questions about any of the above, or want to know how equipment connectivity and control fit into the overall Smart Manufacturing landscape, please contact us!

 

As predicted, Cimetrix had a very busy and fruitful week (September 11-15) in Taiwan. In addition to hosting numerous customers, prospects, and friends at our booth, Cimetrix made multiple technical presentations focused on Smart Manufacturing, both at the show and at the eMDC Conference in Hsinchu. Two of these were jointly presented with Mark Reath of GLOBALFOUNDRIES. Use the links at the bottom of this post if you’d like a copy of this material.

In addition to our participation in these technical events, Cimetrix demonstrated the new Cimetrix EDATester™ product to a number of factory customers who now need a validated method for verifying incoming equipment compliance to the SEMI Equipment Data Acquisition (EDA) suite of standards. The timing for this product is ideal, based on the increased rate of adoption for these standards we have seen in Taiwan. 

Given the industry’s current momentum and near-term outlook, the mood in Taiwan is overwhelmingly positive, and the trade show almost had a celebratory quality to it. Fittingly, the Leadership Gala Dinner held Wednesday night was a feast for all the senses, from the food to the entertainment to the array of dignitaries who attended. 

For the second year in a row, President Tsai attended and expressed her gratitude for the industry’s role in advancing the quality of life across the nation and her administration’s unwavering support for its continued prosperity. Dr. Nicky Lu, one of Taiwan’s well-known business and technology icons, was honored with the industry’s most prestigious Leadership Award, and shared his personal perspective on some of the exciting semiconductor-enabled products now in the works. 

Dr. Lu went into even more depth about semiconductor technology evolution in his keynote address for the eMDC Conference (Joint Symposium of e-Manufacturing & Design Collaboration Symposium 2017 and ISSM 2017) on Friday, outlining a powerful vision that he’s labelled as “HIDAS: Heterogeneous Integrated Design/Architecture/System for Silicon-Centric Nano-System in Si4.0” – if you want to know more, you’ll want to contact him directly!

All in all, is was a great week to be in Taiwan, especially since Typhoon Talim decided to take a turn to the north without dampening the spirit of SEMICON. Please contact us with any questions, and we look forward to seeing you at future industry events.

Device Scaling vs. Process Control Scaling: Advanced Sensorization Closes the Gap

Smarter Manufacturing through Equipment Data-Driven Application Design

Smart Manufacturing Requirements for Equipment Capability and Control

The definition for a traditional dashboard is fairly simple—“the panel facing the driver of a vehicle or the pilot of an aircraft, containing instruments and controls”—and well understood by anyone with much time behind the wheel.

However, information technology dashboards in a business context take a few more words to describe. From Wikipedia, “dashboards often provide at-a-glance views of KPIs (key performance indicators) relevant to a particular objective or business process (e.g., sales, marketing, human resources, or production).” An example of such a dashboard for a single manufacturing process follows.

Although only recently popularized by commercial BI (business intelligence) software packages, dashboard-style display technology has been around a long time. Specifically, the PLC (programmable logic controller) industry saw early on that the PC (personal computer) was an ideal user interface platform for machine operators, providing what most would call today an interactive “dashboard” for a piece of equipment or portion of a manufacturing process. PLCs were originally designed as solid-state replacements for the relay panels used for sequence control for small- to medium-sized manufacturing equipment of limited complexity. Over time, they grew in sophistication to include PID (proportional, integral, differential) control capabilities for unit processes across a wide range of industries, and became a vital component of major manufacturing facilities worldwide.

Despite the number of vendors that provided PLCs and the variety of applications they supported, all PLCs shared a common internal architectural feature called an “image register,” which is a section of memory that contains the process and state variables representing the complete status of the machine at any moment. Even though there were initially no industry standards that dictated the exact structure of an image register, they were similar enough that a basic PLC-specific driver was sufficient to map any PLC’s image register into the standard widgets of a dashboard-style operator interface, providing real-time display of process status and sometimes interactive control capabilities. One of the most successful such packages was the Wonderware InTouch software product, shown below in a batch process context.

Until recently, the lack of standardization in the embedded control system architectures for semiconductor manufacturing equipment made the implementation of equipment-oriented factory-level dashboards fairly challenging. However, with the advent of the SEMI EDA (Equipment Data Acquisition) standards and, in particular, the increasing fidelity of the equipment models required by these standards, all that has changed. Especially for equipment suppliers who follow the SEMI E164 (EDA Common Metadata) standard, the structure and content of the embedded equipment model are sufficient to provide direct access to most of the parameters and events you’d expect to find in a dashboard. Displaying some equipment KPI's, such as OEE, may require a little additional calculation and perhaps some minimal user input, but the most of information needed to compute these metrics is readily available.

For example, if you want to see the list of jobs active on a piece of equipment, look no further than the JobManager logical element of the metadata model (see below*).

If you want to display the material status of a piece of equipment—for example the carriers, lots, and substrates that are present—the MaterialManager logical element contains all of this information.

To display the current performance status and recent history of the major equipment modules, use the state information and reason codes in the SEMI E116 (Equipment Performance Tracking) EPTTracker logical elements to achieve this objective.

Recipe execution status information for each module capable of processing material is found in the ModuleProcess state machine within the relevant Process Chamber.

And finally, if you want to show the current operations status of the equipment as a whole, this information is found in the GEM variables present in the metadata model.

You can see from the above examples that despite the lack of standardization in the embedded equipment controller architectures across the semiconductor industry, the information needed in equipment level dashboards is directly provided by the industry standards that define the EDA interfaces. This provides yet another use case for factories to drive for the adoption of these standards.

In addition to the standardized data access, another feature of EDA that makes it ideal for dashboard implementation is its multi-client capability. The software implementing a factory-level dashboard can communicate with many pieces of equipment at once, since the data volume required from any single equipment is small. From the equipment point of view, the dashboard system would appear as a separate client from the other application client(s) with more intense data collection requirements. This separation of clients also means that the dashboard content can be changed easily, since this is accomplished by modifying the relevant DCPs (data collection plans) rather than changing the data collection application itself.

Last but not least, since SEMI E164 standardizes the actual event and parameter names in the metadata model, the DCPs that collect this information can be programmatically generated and activated for all the equipment that is E164-compliant. This represents a significant engineering cost reduction over the conventional methods used to identify, collect, and manage the information required to animate a real-time dashboard.

This article is the fifth in the series recently announced in the Models in Smart Manufacturing Series Introduction posting – be sure to watch for at least one more posting that wraps up this overall theme.

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

 

*The visualizations of equipment metadata model fragments are those produced by the Cimetrix ECCE Plus product (EDA Client Connection Emulator).