Industry News, Trends and Technology, and Standards Updates

In the fourth article of this series, Fault Detection and Classification, we highlighted the application that has been the principal driver for the adoption of EDA (Equipment Data Acquisition) standards across the industry thus far, namely Fault Detection and Classification (FDC). In this posting, we’ll discuss another important application that effectively leverages the capabilities of the EDA standard: Fleet Matching and Management. 

Problem Statement

The problem that fleet matching (which also covers chamber and tool matching) addresses is maintaining large sets of similar equipment types at the same operating point in order to maximize lot scheduling flexibility by the real-time scheduling and dispatching systems that run modern wafer fabs. This avoids the situation where specific equipment instances are dedicated to (and therefore reserved for) critical layers of certain products, processes or recipes, which can reduce the effective capacity of the affected process area. This situation can arise because tools naturally “drift” apart over time, especially when manual adjustments are made to the equipment, or other factors (maintenance actions, consumable material changes, key sub-system replacements, etc.) affect the equipment’s operating envelope. 

Of course, part of the problem is choosing which equipment should be the one matched to—the so-called “golden tool.” And depending on the breadth of the fab’s product/process mix, there may be multiple targets to choose from, further complicating the task. 

Solution Components

The solutions for many of today’s complex manufacturing problems require lots of high-quality equipment data, and fleet matching is no exception. Like FDC, choosing the golden tool(s) also requires some information about which recent lots exhibited the highest yields, which must be correlated with the equipment used throughout the process. Unlike FDC, however, it is NOT necessary to build hundreds (if not thousands) of multivariate fault models specific to the various context combinations, because the underlying principle of chamber/tool/fleet matching is that “if all the fundamental operating mechanisms of a set of equipment are working consistently, then the behavior of the equipment in aggregate should likewise be consistent.” This means that the matching process can be largely recipe independent, which is a major simplification over other statistically based applications.

This is not as simple as it may first appear, because a complex equipment may have scores of these mechanisms (pressure/flow control, multi-zone temperature control, motion control, power/phase generation, etc.) for which thousands of parameters must be collected to characterize and monitor equipment behavior accurately. Static and dynamic equipment configuration information also comes into play, since similar (but not identical) tools may be interchangeable for certain processes. 

This is where the EDA standards enter the picture.

EDA Standards Leverage

Although not explicitly required by the SEMI EDA standards, the intent and expectation of its designers was to support a far richer (read “more detailed”) equipment metadata model than is practical in most SECS/GEM implementations. With respect to fleet matching and management, this would include not just the high-level status variables for key equipment mechanisms (listed above), but also the setpoints, internal control parameters, and detailed status of their underlying components. 

The metadata model must also include the complete set of equipment constants that govern tool operation, since these “constants” are sometimes changed “on the fly” by an operator within some allowable range. While this may be an acceptable production practice, it nevertheless affects the tool’s operating window, and must be accounted for in the matching algorithms. 

Moreover, the communications interface should support sampling and data collection of these detailed parameters at a frequency sufficient to observe the complete real-time operation of these mechanisms so the process and equipment engineers can more deeply understand how the equipment actually works. Support for this level of equipment visibility was also a stated requirement for the EDA standards.

Once this data is collected, a variety of analysis tools can look for similarities and anomalies in the equipment parameters to identify the factors that matter most in achieving consistent process performance. At this writing, a number of companies are looking at this domain as an ideal application for Artificial Intelligence and Machine Learning technology. Stay tuned for exciting developments in this area. 

KPIs Affected

The KPI (Key Performance Indicators) most impacted by the fleet matching and management application is overall factory cycle time, since the scheduling systems can make optimal use of all available equipment to move material through the fab.

Equipment uptime is also improved, because the continuous equipment mechanism “fingerprinting” process which is fundamental to fleet matching also catches potential problems before they cause the entire tool to fail. Finally, when more equipment instances are available for running experimental lots (rather than having dedicated tools for this), the yield ramp for new processes can be shortened as well.

If keeping a large set of supposedly identical equipment operating consistently is a challenge you currently face, give us a call. We can help you understand the approaches for building a standards-based Smart Manufacturing data collection infrastructure to support the machine learning algorithms that are increasingly prevalent in this latest generation of manufacturing applications… including fleet matching and management. 

To Learn more about the EDA/Interface A Standard for automation requirements, download the EDA/Interface A white paper today.

 

Hwal Song of Cimetrix Korea talks about EDA/Interface A and Korean Equipment Makers. Read now in Korean or below in English.

Cimetrix Korea와 국내 파트너사인 링크제니시스는 EDA에 대한 세미나를 7 월 19 일에 개최했습니다. 삼성, SK 하이닉스를 포함한 글로벌 반도체 제조사들이 공정 장비의 추가 통신 프로토콜로 EDA를 채택함에 따라 EDA가 더욱 각광을 받게 되면서 개최한 세번째 세미나였습니다. 지금까지 200 명이 넘는 엔지니어가 이들 세미나에 참석하였으면, 이에 따라 한국에서의 EDA 연착륙이 진행 중입니다. 이들 세미나의 목적은 EDA의 이해부족로 오는 시행착오를 줄이고 첫번째 EDA구현을 가장 빠른 시간에 구현하기 위함입니다.

반도체 제조사의 EDA 채택은 차세대 분석 기능을 구현하고 딥러닝 및 머신 러닝 등과 같은 빅 데이터 및 AI의 새로운 기술을 수용하기 위한 주요 노력의 일환입니다. 대부분의 공정장비들이Freeze II 및 E164가 포함된 EDA 기능의 장착이 요구되거나 요구될 예정이여서,  Cimetrix Korea는EDA의 일반적인 개념뿐만 아니라 심도 있는 구현 방법론에 대한 이해를 위한 도움이 각 장비 제조사의 소프트웨어 엔지니어들에게 필요함을 인지하였습니다. 이러한 요구를 충족시키기 위해 다음과 같이 내용으로 세미나가 준비되었습니다.

• EDA관련 업계 동향 및 채택 현황 -  E164의 중요성 포함

• 반도체 제조사가 EDA 사양을 어떤 과정을 거쳐 준비하는가?

• 장비제조사의 소프트웨어팀 관점에서 EDA 구현의 모범 사례와 방법론

• 여러 장비제조사의 소프트웨어팀을 코칭한 컨설팅 및 지원 팀의 관점에서 EDA 구현 모범 사례와 방법론

• 구현중이거나 구현된 EDA 적합성 테스트의 모범 사례

활발한 토론과 Q&A를 통해서 한국의 장비 제조사들의 EDA에 대한 높은 관심을 엿볼 수있는 행사였습니다.

궁금한 점이 있으시면 언제든 연락주시기 바랍니다.

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Cimetrix Korea and our local partner, Linkgenesis, held a seminar about SEMI EDA on July 19, the third one since 2016. EDA continues to gain momentum as global chip makers like Samsung and SK hynix have adopted EDA as an additional communication protocol for process tools. So far, over 200 engineers have attended these seminars, promoting the soft landing of EDA in Korea. Their goals include minimizing trial and error due to the lack of understanding on EDA, and speeding up the first deployment of EDA.

Adoption of EDA by chip makers is one of the major efforts for factories to implement their next-generation analytic capabilities and also embrace new technologies from big data and AI such as deep learning and machine learning. As most process tools are or will be required to ship with EDA capability, with Freeze II and E164, Cimetrix Korea recognized that Korean software engineers employed at equipment makers were looking for help in understanding in-depth deployment methodologies as well as understanding the general concept of EDA. In order to accommodate these needs, our seminars have been put together with following agenda:

• General industry trends of EDA and its adoption – the importance of E164
• How an EDA specification is put together by a chip maker
• Best practices & methodologies for implementation of EDA – from the perspective of a software team at a tool maker
• Best practices & methodologies for implementation of EDA – from the perspective of a consulting and support team who have coached many software teams in tool makers
• Testing best practices during and after EDA implementation

Active discussions and Q&A showed the high level of interest in EDA among Korean equipment makers. 

Please feel free to contact us whenever you have any questions.

During the SEMICON West exhibition in San Francisco this past week (July 9-10), the North American Information & Control Committee and its Task Forces met to continue SEMI standards development. Here is a brief summary of the proceedings.

The GEM 300 task force, in addition to reapproving E90, also approved minor title changes to the E39, E39.1, E40 and E40.1 standards. Each SEMI standard must be revised or reapproved to avoid becoming inactive. A few years ago, SEMI changed regulations that mandate that each standard declare its classification, such as a “guide” or “specification”. Since then the task force has been slowly correcting the titles. The E37.1 standard is in the middle of such classification, but has been riddled with reapproval complications due to minor concerns and some needed corrections in the standard. The ballot to make these corrections, 6349, failed for the second time at SEMICON West. The ballot will be slightly reworked and resubmitted for another round of voting. Another ballot, 6348 proposed to clean up the GEM E30 standard, to improve its readability and to bring the standard in conformance with current SEMI regulations and its current style guide. The forefront of the discussions was surrounding the confusing use of acronyms DVNAME, DVVAL, SVV and other such acronyms where the meaning and use of the acronyms was confusing to new readers. The 6348 ballot also failed, but hopefully the task force is progressing towards reaching an agreement. One major challenge is that ballot 6348 is a major revision ballot, where the entire specification is opened up for review and scrutiny, as opposed to line item ballots where only specific sections of a standard are modified.

Finally, and most exciting is ballot 6114B; a revision to the SECS-II E5 standard. The ballot proposed a set of new messages for transferring any large items between a host and equipment. Typically, one item in a message is limited to about 16.7 MB. The new messages are specifically targeting the transfer of equipment recipes, but the messages are written generic enough so that anything else can be transferred, too. The new messages support two styles of item transfer. Either the item can be transmitted in a single message, or broken into parts for transfer with the expectation to be concatenated by the recipient. Or the item can be transmitted in multiple messages, broken into parts with each part sent in a separate message and the same expectation to be concatenated by the recipient. An item is identified by its “type”, “id” and “version”. The messages are intended to resolve current issues with recipes where some equipment suppliers are using recipes that surpass 16.7 MB. And the messages open the door to be used by other SEMI standards and to be customized for specific applications. After passing this ballot, the task force intends to make the messages part of the GEM standard. Even though the ballot 6348 failed, the task force seems to have finally reached consensus on the message formats and continues to work out minor details.

The DDA Task Force continues to work on the next version of the Equipment Data Acquisition (EDA) standards. In the latest cycle of voting, changes were proposed to E138 (ballot 6336), E134 (ballot 6335) and E132 (ballot 6337). Although one part of E134 passed, most of E134 failed and the other ballots failed. All of the failed ballots will be reworked and resubmitted for voting. Additionally, during the task force meeting additional proposed changes were reviewed and discussed. The task force continues to make plans to move from HTTP 1.1 and SOAP/XML to HTTP 2.0 and Protocol Buffers. Specifically, the plan is to recommend using gRPC. Testing done to date indicated an 18 times performance improvement and significant bandwidth reduction. The task force also discussed changes to simplify the equipment model metadata handling. Finally, Cimetrix proposed the implementation of a new method of data sampling designed for higher data collection frequencies. The current trace data collection messages, while very effective for speeds up to maybe 80 Hz, become inefficient when trying to collect data at even faster rates. The concept is called a “cached data sample” where the equipment collects the data at a specified frequency and then reports the data in an array syntax. When using HTTP 2.0 and Protocol Buffers, this will be an especially efficient format expected to allow much higher frequencies.

The client specifies the data collection frequency as well as the reporting frequency. For example, a client might specify a frequency of 10 kHz and a reporting frequency of 1 s, where 10,000 data samples would be reported each second. Such proposal if accepted, combined with the faster Protocol Buffer, will open the door for a number of new data collection applications.

A lot of people are wondering when EDA freeze III will be done. Probably not until late next year. How soon this happens mostly depends on how efficiently task force members provide feedback on the ballot drafts.

Subscribe to our blog in the upper right corner of this page to be sure not to miss that or any of my future updates on the North American Information & Control Committee.

SEMICON West 2018 is fast approaching and the Cimetrix team is gearing up for a great show.  The show runs from July 10^th – 12^th at the Moscone Center in San Francisco and we’re looking forward to meeting with all our present and future clients.

This year SEMICON West is unveiling the new Smart Manufacturing Pavilion to showcase the entire manufacturing process from silicon to systems, including Front End, Back End and PCB Assembly. Cimetrix is excited to announce that we are a sponsor and will be participating in the Smart Manufacturing Pavilion showcase, both as part of the Front End segment as well as in the PCB Assembly area.

The Smart Manufacturing Pavilion includes a “Meet the Experts Theater” featuring presentations from two of our own Cimetrix thought leaders.  Alan Weber will present “Making Smart Manufacturing Work: The Stakeholder-driven Requirements Development Process” on Wednesday, July 11^th at 11:00 am. This process has already been used successfully to support the significant growth of SEMI EDA standards usage in Asia, but is equally relevant for a wide range of related Smart Manufacturing technologies.

Later on Wednesday afternoon at 3:00 pm, Ranjan Chatterjee and Dan Gamota of Jabil will present “Convergence of Technologies and Standards Across the Semiconductor, SMT and OSAT Segments.” 

Cimetrix will be exhibiting at booth #1122 in the South Hall, just a short walk from the Smart Manufacturing Pavilion. Stop by our booth or find us at the Pavilion to talk to our experts about your specific needs. We will have onsite product demonstrations as well as information about our company available.  You can also schedule in advance a time to meet with us at the show by filling out a quick form with your meeting request.  

See you at SEMICON!

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.

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).