syntricity.com

Aptina Selects Syntricity for Semiconductor Yield Management

2012-04-16T23:39:00Z

SAN DIEGO, CALIFORNIA, April 16, 2012 – Syntricity Inc. announced today a multi-year agreement that will provide the latest advances in its dataConductor™ platform to Aptina.

A global provider of semiconductor CMOS imaging technology products, Aptina will benefit from an advanced, integrated yield management system, delivered through Syntricity’s Software-as-a-Service (SaaS) offering, dataConductor.com.

“After evaluating yield management system solutions, Syntricity’s dataConductor stood out for its scalability and advanced analysis capabilities,” said Ed Jenkins, Director of Product and Test Engineering at Aptina. “Continuously looking to improve our systems, we looked to Syntricity’s success in managing large, hosted data warehouses as a key factor in our decision to partner with them. Syntricity’s hosted database and dataConductor.com tool set will give us ready worldwide access to our supplier data without adding further capital and resources to our current internal IT infrastructure.”

Accessed through a web browser, dataConductor will provide Aptina with a reliable, secure, and scalable enterprise yield management system, with automated connections to Aptina’s global supply chain of wafer foundries, assembly houses, and test facilities.

“dataConductor enables large amounts of production and engineering data to be collected from around the world, analyzed, and acted upon quickly,” said Steven Griffith, President and CEO of Syntricity. “Our hosted solution is a great fit for a global fabless semiconductor company like Aptina. We’re delighted that dataConductor will be the basis for yield management, data analysis, and reporting for Aptina.”

About Syntricity, Inc.

Syntricity is the pioneer in Web-based, enterprise yield management. Its software is used by leading semiconductor companies, test vendors and wafer foundries to gather, manage and analyze data from facilities around the world to improve yield, reduce product development cycles, and increase profits. A privately-held company, Syntricity is headquartered in San Diego, California.

For more information, visit http://www.syntricity.comor contact Brian Graff at Syntricity Inc., 10525 Vista Sorrento Parkway, Suite 220, San Diego, CA 92121; Tel: (858) 678-1208; Fax: (858) 552-4493.

About Aptina
Aptina is a global provider of CMOS imaging solutions that enable Imaging Everywhere™. Using performance enhancing technologies like Aptina A-Pix™, DR-Pix™ and award winning MobileHDR™, Aptina has created a market-leading portfolio of image sensor products found in leading consumer electronics like smartphones, tablets, laptops, digital and video cameras, as well as applications in surveillance, automotive, medical, video conferencing, and gaming. Aptina drives innovation in the market with industry recognized products like the AR0331surveillance image sensor and the 16MP APS-C DSLR sensor. Privately held, Aptina’s investors include Riverwood Capital, TPG Capital and Micron Technology. For additional information on Aptina visit www.aptina.comor subscribe to the latest news from Aptina by copying the Aptina RSS feedinto your favorite RSS reader.

©2012 Aptina Imaging Corporation. All rights reserved. Information is subject to change without notice. Aptina, the Aptina logo, A-Pix, DR-Pix, MobileHDR and Imaging Everywhere are trademarks of Aptina Imaging Corporation.All other trademarks are the property of their respective owners.

What's Old is New Again

2012-02-07T17:46:15Z

It’s just a few days past Groundhog Day, and it’s not hard to anticipate spring time. As we do so here at Syntricity, we look forward to celebrating our 15^th anniversary [or birthday or whatever you’d like to call the event that commemorates the start of our company].

While everyone has their head in “The Cloud” now, 15 years ago the idea that a company would process and warehouse another company’s data and provide browser-based analysis and reporting tools for semiconductor characterization and yield enhancement was not exactly commonplace. Today, it’s the norm for companies around the world, many of whom were early customers of ours.

In a world of spreadsheets on the personal computer (or workstation) of each individual engineer, the advantages of providing access to centralized data quickly became clear. Further, having access to those datasets from anyplace/anytime, with just a web browser to point the way, let our customers solve problems in ways they just weren’t able to do before.

It’s tempting to put numbers on things. For example, we can say that after a decade and a half, dataConductor has processed several million (yes, million) STDF files. And given all those characterization and production STDF files, we could make a pretty good guess at how many WAT files have been parsed and processed in dataConductor as well. While it’s tempting, we know that summarizing things with a few metrics doesn’t really tell the story behind Syntricity and dataConductor.

We received an e-mail recently from someone who worked with dataConductor in her previous job (she’s an independent consultant now). Her comments reflect the vision that Syntricity had for dataConductor from the outset:

"From 2003-2005, before 'cloud computing' was coined, I telecommuted from Mexico for my company. My then-employer used dataConductor.com. Because of the nascent cloud computing structure available on dataConductor.com, I was able to perform my job of managing, troubleshooting, and reporting yield data even though I was two thousand miles away from the office. Syntricity has an experienced technical support team, and the data center had superb uptime, a must for cloud computing to be effective.”

We couldn’t have said it better, and thanks to all our customers for the first 15 years.

Syntricity’s dataConductor.com More Than a Passing Cloud

2011-10-23T21:22:01Z

SAN DIEGO, CALIFORNIA, October 24, 2011 – As companies become increasingly aware of the benefits of the Cloud, Syntricity’s customers continue to realize the benefits of a Cloud-based yield management system. dataConductor.com, a private cloud, provides a comprehensive, scalable, and reliable enterprise yield management system for semiconductor companies, with automated connections to customers’ global supply chain of wafer foundries, assembly houses, and test facilities.

For well over a decade, dataConductor.com has provided key benefits to startups as well as established manufacturers:

· A data warehouse designed to keep several quarters of parametric and bin data online, providing the data necessary for root cause analysis across manufacturing vendors and operations.

· Comprehensive analysis and reporting tools, with built-in drilldown analysis templates as well as customizable/shareable workflows to turn those data into bottom line results.

· Scalability to meet the needs of companies as they grow and as the amount of data collected grows as well.

· High reliability to ensure access to data any time of day, anywhere in the world.

“Our expertise in developing and managing databases and data flows is part and parcel of dataConductor.com,” said Tim Lewis, Vice President of Operations. “As a company grows and adds new suppliers and data types, Syntricity seamlessly blends these new data flows into the fold. As storage needs change, dataConductor.com is ready to accommodate. Also, since we know that data delayed means decisions delayed, we’ve forged the latest in processor, solid-stage storage, and database software technologies into a high-performance solution.”

Web-native, dataConductor is easily accessed through a web browser and offers visibility across the supply chain. Production data can be summarized at load time to bring further performance benefits. And while San Diego recently endured the most extensive power outage in its history, dataConductor.com maintained 100% availability. “When we say reliable, we mean that we’re up even if others are down,” added Lewis.

“A decade ago, the notion of a Web-based data analysis platform for a semiconductor company was a novelty. Now, the idea that all data/measurements are available at any time through the Web is fundamental to how many of our customers do business,” said Steven Griffith, president and CEO of Syntricity. “Our largest customer is one of our first customers. As they’ve grown into one of the leading fabless semiconductor companies in the world, dataConductor has grown alongside to meet their needs. That’s just one reason that the dataConductor platform continues to be the basis for data analysis, yield management, and reporting as companies develop the next generation of semiconductor products.”

About Syntricity, Inc.

Syntricity is the pioneer in enterprise yield management in the semiconductor industry. Its software is used by leading semiconductor companies, test vendors and wafer foundries to gather, manage and analyze data from facilities around the world to improve yield, reduce product development cycles, and increase profits. A privately-held company, Syntricity is headquartered in San Diego, Calif.

For more information, e-mail info@syntricity.com, visit http://www.syntricity.com, or call Brian Graff at Syntricity at 858-678-1208.

Syntricity Named TechAmerica High Tech Awards Finalist

2011-10-17T05:51:15Z

SAN DIEGO, Calif., October 10, 2011 — Syntricity, the pioneer in Web-based, enterprise yield management for the semiconductor industry, announced today that it has been selected as a finalist in the Software – SaaS/Cloud category for the upcoming 18^th Annual TechAmerica High Tech Awards. As the leading voice representing the U.S. technology industry, TechAmerica takes pride in recognizing outstanding regional companies every year at its annual awards ceremony. For the 2011 futuristic-themed event, finalists will be acknowledged for their achievements during a luncheon reception on October 28^th at the Hilton La Jolla Torrey Pines in La Jolla.

“We’re thrilled to receive this recognition from TechAmerica,” said Steven Griffith, President, CEO, and co-founder of Syntricity. “Initially, the notion of a Web-based data analysis platform for semiconductor companies was a stretch for some people. Now, the idea that all data are available at any time online is fundamental to how our customers do business. That kind of forward thinking is what TechAmerica is celebrating this year with its futuristic theme, and we’re honored to be an awards finalist.”

This year numerous local San Diego companies were nominated for their technological or business innovation; exceptional products or service; product marketplace validation; perseverance in the face of adversity; and community involvement for consideration in nine categories, including Software;Internet and Web Commerce; Computers and Related Products; Communications Products and Services; SaaS/Cloud; Semiconductors & Analytical Instrumentation; Clean Technology; IT Service/Contract Services; and Outstanding Emerging Growth.

Syntricity was selected as a finalist in the Software – SaaS/Cloud categoryfor its innovative dataConductor platform,which provides online access to a comprehensive, reliable and secure enterprise yield management system with automated connections to customers’ global supply chain of wafer foundries, assembly houses, and test facilities.

“This year, we received an overwhelming amount of nominations from some tremendous companies,” said Kevin Carroll, regional vice president, TechAmerica. “The plethora of nominations is not only a testament to the individual companies, but to the rapidly growing San Diego High-Tech community. Selecting the winners will certainly not be an easy task.”

For media interested in attending the High Tech Awards event please contact Sarah Lubeck at 619-234-0345 or lubeck@formulapr.com.

The 2011 TechAmerica High Tech Awards are sponsored by Bank of America Merrill Lynch, Barney & Barney, CB Richard Ellis, Deloitte, Morgan Stanley Smith Barney, Formula, Procopio,SignOnSanDiego.com, and TheLawton Group.

About Syntricity

Syntricity is the pioneer in web-based, enterprise yield management for the semiconductor industry. Its software is used by leading semiconductor companies, test vendors and wafer foundries to gather, manage and analyze data from facilities around the world to improve yield, reduce product development cycles, and increase profits. A privately-held company, Syntricity is headquartered in San Diego, California. For more information, e-mail info@syntricity.com, visit http://www.syntricity.com, or call Brian Graff at 858-678-1208.

About TechAmerica
TechAmerica is the leading voice for the U.S. technology industry, the driving force behind productivity growth and jobs creation in the United States and the foundation of the global innovation economy. Representing approximately 1,500 member companies of all sizes from the public and commercial sectors of the economy, it is the industry’s largest advocacy organization and is dedicated to helping members’ top and bottom lines. It is also the technology industry's only grassroots-to-global advocacy network, with offices in state capitals around the United States, Washington, D.C., Europe (Brussels) and Asia (Beijing). TechAmerica was formed by the merger of AeA (formerly the American Electronics Association), the Cyber Security Industry Alliance (CSIA), the Information Technology Association of America (ITAA) and the Government Electronics & Information Technology Association (GEIA). Learn more at www.aeanet.orgor www.itaa.org.

Syntricity Delivers dataConductor 7.0

2011-06-29T20:08:24Z

Enhanced Analysis Capabilities Combine with Simplified Work Flows to Drive Solutions

SAN DIEGO, June 29, 2011 – Syntricity Inc. announced the release of dataConductor®7.0, the latest version of its enterprise yield management software. dataConductor 7.0 enables customers to manage large datasets, quickly identify and address unusual results, and share their results as well as methods. Available immediately, dataConductor 7.0 is available through dataConductor.com®, a software-as-a-service (SaaS) offering, or as an enterprise installation.

“In dataConductor 7.0, our new user interface helps beginners and experts alike get more out of their analysis sessions. And now, customers can easily take best practices that they’ve implemented and turn them into standardized templates for their engineers, providing a solid foundation for various types of analysis,” said Steven Griffith, CEO and co-founder of Syntricity “With dataConductor 7.0, we continue to deliver solutions that help our customers develop the next generation of semiconductor products.”

Highlights of dataConductor 7.0 include:

- knowledge sharingthrough user-configured/shareable templates,

- multi-threaded dataloading and statistical analysis,

- new analysis capabilitiesin RaCR, the rapid characterization and root cause analysis application for dataConductor,

- operation-based selective data sampling, designed with cross-operation analysis in mind,

- interactive outlier removal, at the measurement or part level, and a

- streamlined user interface,including single-click access to common tools and features.

“Selective data sampling and enhanced, multi-threaded data loading and manipulation provide customers with fast, efficient forms of single- and multiple-operation analysis,” continued Griffith. “Slicing and dicing large datasets has never been easier, because dataConductor 7.0 simplifies access to commonly used features such as editing limits and grouping data, helping reduce the time to make decisions.”

The dataConductor platform integrates a high-performance data warehouse with flexible analytics and reporting.Drag-and-drop, template-based analysis lets customers implement best practices, while Syntricity’s consulting services help companies recognize further yield improvements.Semiconductor companies of all sizes use dataConductor to analyze engineering data, ramp products to production, and manage and improve wafer and final test manufacturing yields.

About Syntricity, Inc.

Syntricity is the pioneer in web-based, enterprise yield management in the semiconductor industry. Founded in 1997, its software is used by leading semiconductor companies, test vendors and wafer foundries to gather, manage and analyze data from facilities around the world to improve yield, reduce product development cycles, and increase profits. A privately-held company, Syntricity is headquartered in San Diego, California. For more information, e-mail info@syntricity.com, visit http://www.syntricity.com, or call 858 552-4485.

Just a Sample Opinion

2011-05-11T18:51:23Z

We don’t always have the luxury of measuring an entire population, so we take samples. For good or for bad, there is plenty of advice when it comes to sampling. Sir R. A. Fisher said that if all the elements in your population are identical, you need only a sample size of one. John Tukey noted that in general you never improve your estimate of variability more than when you go from a sample size of one to a sample size of two. George Runger has said that he’d rather have a small sample of parts collected over a couple of weeks of production than a couple of thousand parts from one batch produced this afternoon. At the risk of making a sweeping generalization, we see a lot of work done with samples of size 30 or so. Is there something magical about a sample size of 30?

With a lot of determination and a little help from some friends (including Fisher), William Gosset (aka “Student”) developed the properties of the t distribution. Gosset’s work at the Guinness brewery in Dublin led him to look closely at what it means to rely on a small sample to estimate the population mean and the population standard deviation (I’m pretty sure he worked more with yeast than he did with beer, by the way). His key observation was that for small samples, there’s uncertainty not only in our estimate of the mean value but there can be considerable uncertainty in our estimate of the standard deviation. The probability distribution function of a t distribution looks similar to that of a normal distribution, but t distributions have (among other things) heavier tails (accounting for the uncertainty in the estimate of standard deviation). As sample sizes increase the t distribution look more and more like the normal (Z) distribution (infinite sample size and the t is the Z – that makes sense, as an infinite sample size means we’re looking at the population).

Introductory statistics books and classes often discuss the notion of Z-tests and t-tests to compare two sample means. T-tests are appropriate when the sample standard deviation is the best we’ve got as an estimate of the population sigma (actually, you could argue that t tests are always appropriate), while Z-tests are appropriate when the population standard deviation is known. For my own part, the last time I knew the population standard deviation was when it was given to me in a homework problem in an introductory statistics class, so it seems like the t-test is the way to go. Yet, we know there’s more to the story, including the fact that the normal distribution is just so convenient.

Many texts suggest than when the sample size is around 30 you can safely use the normal distribution instead of the t-distribution when computing, for example, a confidence interval on the mean (standard deviation unknown, but estimated from the data). An important point to note is that a distribution of sample averages tends toward the normal distribution as the sample size increases (look to the central limit theorem for guidance here). The key, of course, is that we're talking about a distribution formed from average values, not individual values. A large sample size doesn't turn (for example) a lognormal distribution into a normal distribution, it just helps turn a distribution of sample averages from a lognormal distribution into something that approaches a normal distribution.

Keith Bower has written: “Regarding n = 30, I'm fairly sure the prevalence is due to one of Egon Sharpe Pearson's papers … which runs thru many simulations to assess robustness of t-tests with regard to some non-symmetry in the underlying distribution. I'm fairly sure it was Shewhart who had recommended … to investigate it, in some correspondence between the two. Statisticians should always preach the ‘it depends’ mantra though, as I'm sure Pearson would be the first to agree with.”

Again, having a sample size of 30 does not somehow magically turn the underlying distribution into a normal distribution. If your data are uniformly distributed or lognormally distributed or gamma or beta or whatever, then individual values are modeled quite differently as compared with a normal distribution. What does happen is that for many cases, a sample size of 30 gets you to a point where the difference between using Z and t is relatively small for such things as confidence intervals of the mean. However, it doesn’t guarantee that your estimate of the mean is somehow spot on. Even with a sample size of 30, a 95% confidence interval based on a sample mean of 100 and sample standard deviation of 10 is ~ 96.3 to 103.7 (if you use Z instead of t, the interval would be 96.4 to 103.6).

By the way, for that same parameter with estimated mean 100 and estimated standard deviation 10, a 95% CI for the capability index Cpk is around 1.00 to 1.67. From a PPM standpoint, that’s around 2700 to about 0.57. To put it nicely, that’s a difference of a few orders of magnitude. In a related vein, Somerville and Montgomery point out that the assumption of normality would lead to an underestimation of 1428 PPM in the case where Cpk is calculated to be 1.00 with the underlying distribution actually t with 30 degrees of freedom.

My wife used to work for a relatively large computer products manufacturer. She was in a meeting one day when the topic of proportion defective came up. That is, someone started a discussion about what sort of sample size would be needed to detect a particular proportion defective. For illustration, let’s say they were concerned about one particular kind of defect, and assume 1 out of 50 widgets had this issue. What would you infer about this problem if you took a random sample of 5 widgets? 50? 500? 5000? In the meeting, someone suggested that if you could take a ‘perfect’ sample of say 50 widgets and you saw that 1 of them had the defect then you knew that your proportion defective was exactly 2% in the population. That’s an interesting sentiment, but it is essentially meaningless. Sampling isn’t about perfection, it’s about practical ways of dealing with uncertainty.

All Experiments are Designed Experiments

2011-02-25T00:19:25Z

One of the perks of my job is that I get to poke at a lot of datasets from customers and prospective customers. PCM/WAT, wafer sort, final test, other production datasets, and all sorts of engineering datasets (gauge R&R, repeatability experiments, and on and on). It’s no surprise that one of the themes we see is a concern on the part of our customers that they have accurate and consistent test solutions. In recent weeks, some of the datasets that I’ve looked at data include a part run 30 times in a repeatability experiment, multi-site wafer sort data with a distinctive site-to-site failure pattern, production ramp results for thousands of parts run at hot and room temperatures to check consistency of results across temperature, and gauge R&R data (from a prospective customer) for ~50 parts runs two times each (repeatability) across program revisions (reproducibility).

An interesting aspect of the gauge R&R data was that we essentially ended up having to resolve for ourselves the actual method that our prospective customer (let’s call them “PC”) is currently using for its gauge R&R studies. For example, when we asked about a particular aspect of the measurement study, PC replied that they were using a t-test to compare results across program revisions (and they sent us the formula for a standard t-test from the help system from their current solution). It took some doing, but the first thing I noticed is that their results were not based on a standard t-test but rather a paired t-test (which is what you might expect since they ran the same parts across program revisions). A key notion to consider here is that a statistically significant difference in a paired t-test does not necessarily translate to a practical difference. (A subtle but consistent difference between two factors may prove to be statistically significant, but even if it is practically significant it may actually be the result of calibration differences or other factors.)

In their summary table, PC had a column labeled “R&R” that was really the precision to tolerance ratio (PTR). It’s easier said than done, but we recommend using standard terms (well, standard as can be) and not developing your own terms. Given that gauge R&R studies are designed to estimate precision, once you recognize that ‘tolerance’ is just another way of saying ‘width of spec limits’ then the term precision to tolerance ratio makes a lot of sense. Further, there was no indication in the summary table that PC was using 5.15 as the sigma multiplier and not 6.0. As we’ve noted before, a sure-fire way of improving your PTR metric is to use 5.15 instead of 6. Of course, PTR will be lower but it doesn’t mean your measurements are better. (Your workplace may dictate 5.15, but if you share your results with a broader audience you might want to note your choice of 5.15.) Note that when you use 6 as the multiplier, the ratio of %GRR to PTR is the formula for the process capability ratio Cp.

A much more interesting aspect of PC’s current method is that it does not include an interaction component, while the dataConductor method (standard gauge R&R using analysis of variance) does include interaction. While this isn’t always critical, in one case we noted a test with a strong interaction component and a PTR of 30% that our prospect considered to have a PTR of 14%. If there were no process variation, a PTR of 14% suggests a measured Cp of greater than 6, while a PTR of 30% suggests a Cp less than 3. The point here is that there really is process variation, and a PTR of 30% is usually considered on the high side. By using a method that essentially looked at one-factor-at-a-time but ignores interaction, PC had set itself up for some hiccups during the production ramp.

Design of Experiments guru Dr. Douglas Montgomery of Arizona State University likes to say that all experiments are designed experiments – some are designed well, and some are designed poorly. The basic design of this gauge R&R experiment that we’ve been discussing was fairly sound, but the analysis was lacking. While it’s hard to extract useful conclusions out of a poorly-designed experiment, a well-designed experiment still depends on a proper analysis.

Syntricity Enhances Data Warehousing and Analysis Performance through Oracle Linux™

2011-02-02T17:10:52Z

SAN DIEGO, CALIFORNIA, February 02, 2011 – Syntricity, Inc. announced support for dataConductor on the Oracle Linuxoperating system. In December, Syntricity completed the first full deployment of dataConductor on Oracle Linux at dataConductor.com, bringing immediate performance gains to all dataConductor.com™ customers. Enterprise customers with an interest in Linux now have a lower cost option for deploying dataConductor to a Unix environment.

Syntricity’s dataConductor.com brings high-performance data analysis and data warehousing to customers ranging from start-ups to some of the largest fabless semiconductor companies in the world.

“The 64-bit Oracle Linux and Oracle Database, woven with the latest in multi-threaded microprocessors and a new memory tier based on solid-state storage, deliver substantial benefits to our customers,” says Tim Lewis, VP of Operations for Syntricity. “Data insertion times have been improved by 75-80% on average, with data extraction and analysis times improved by up to 90% compared to our benchmarks. Within days of deployment, one of our customers noted an order of magnitude improvement in a large data extraction. That’s the kind of performance gain that everyone wants to claim, but our customers are actually experiencing it.”

Syntricity announced in October 2010 that it had accelerated data warehouse performance by replacing spinning hard disks with advanced, solid-state storage in the Oracle-based portion of its data warehouse. Combined with the latest in microprocessor technologies, and driven by 64-bit Oracle Linux, Syntricity has built the ideal environment for RaCR, its interactive, drag-and-drop analysis and reporting platform. Advanced hardware integrated with database enhancements running in a 64-bit environment provide obvious performance gains for data-logged customers.

dataConductor.com is a software-as-a-service (SaaS) offering for semiconductor companies that provides the throughput necessary for data-intensive companies to turn data into solutions. It is the hosted version of dataConductor, a web-native data analysis and reporting solution for the semiconductor industry. Accessed through a web browser, dataConductor.com provides companies of all sizes access to a powerful, reliable and secure engineering yield management (EYM) system scalable to tens of gigabytes of uploaded data per day and multiple terabytes directly accessible online.

Syntricity is a Gold level member of Oracle PartnerNetwork.

About Syntricity, Inc.

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Common Guidelines for Gauge R&R Metrics

2010-11-02T23:55:15Z

There’s an old saying that history repeats itself. Someone long ago modified that to, “History repeats itself; historians repeat each other.” When it comes to guidelines for gauge r&r studies, there’s a lot of repetition going on. Search for information about gauge r&r studies online and you’ll find a lot of material merely referencing other material without any objective commentary. It’s kind of like a mutual admiration society for measurement systems analysis, but we can do better.

Several guidelines have been suggested and documented over the years to help people decide whether or not a measurement system is capable. Three common guidelines read like this:

1) The precision-to-tolerance ration (PTR) should be less than 10%, and if greater than 30% the system is unacceptable.

2) The percentage gauge r&r (%GRR) should be less than 10%, and if greater than 30% the system is unacceptable.

3) The number of distinct categories (ndc) should be 5 or greater, and a value of 0 or 1 implies the system is unacceptable.

Let’s take a closer look at these guidelines and see how they might fit into your measurement systems analysis.

PTR = 6 times stdev(gauge)/tolerance -- The PTR guidelines are based on the notion that a measurement device should be calibrated in units 1/10 as large as the final required measurement accuracy. This notion may hold for your measurement system, but it may not. We know that you may have guidelines established by internal or external customers, but we recommend determining your own guidelines based on your data and experience. Note that if you blow open your limits for whatever reasons, PTR can look quite impressive but not be meaningful. Or, if you have great repeatability and reproducibility, you might consider tightening your limits and fighting back against pesky defects.

Remember that 6*sigma is obviously going to give you a bigger value than 5.15*sigma. 6*sigma represents 99.73% of a normal distribution, while 5.15 sigma represents 99% of a normal distribution. You might ask, What’s 0.73% among friends?, but 6/5.15 is about 1.165. Most mentions of the PTR guidelines ignore this fact. To put it another way, reducing measurement error is harder than merely changing a multiplier from 6 to 5.15.

%GRR = stdev(gauge)/stdev(total) -- In terms of round numbers, the %GRR guidelines are generally the same as the PTR guidelines. BTW, a %GRR of 30% is the same as saying that the measurement system variance is 9% of the total variance (in other words, less than 10%).

Note that if the part-to-part variation increases, %GRR goes down. This does not mean you should ask your friends in the fab to increase part-to-part variability. Ratios are just that – ratios. If your part-to-part variability is extremely low than your %GRR doesn’t compare directly with someone else’s %GRR where there is considerable part-to-part variability. If you're going to do a gauge r&r study, don't just pick two or three parts. You're either going to underestimate part variability or over estimate it, neither of which is helpful.

Also note that if you use 6 as your sigma multiplier for PTR, then %GRR divided by PTR (approximately) equals Cp.

Again, use your data and experience to determine how the %GRR metric can help you decide whether your measurement system is capable.

NDC = square-root[2*variance(process)/variance(gauge)] -- The number of distinct categories derives from another gauge metric, the discrimination ratio. Technically, the ndc can be interpreted as the number of non-overlapping confidence intervals that cover the range of the product variation. (Less technically, ndc can be interpreted as “never don’t concentrate” if you’re a Simpson’s fan.)

More practically, you can view the ndc as the number of distinct categories that the measurement system “sees” within a given parameter. Relatively large amounts of measurement error mean that two parts that are truly quite different from each other may look very similar to each other when measured. Relatively small amounts of measurement error mean that the measurement system can differentiate between two parts that are similar but not identical to each other.

The usual ndc guidelines state that ndc should be 5 or more, and that values less than 2 suggest a non-capable measurement system. An ndc of 5 is actually equivalent to a %GRR of around 27.1%, so the ndc and %GRR guidelines are not consistent with each other. See Some Relationships Between Gage R&R Criteria by William H. Woodall and Connie M. Borror in Quality and Reliability Engineering International (2008; 24:99-106) for more information.

Use your data and experience to determine if the ndc metric can help you measure and improve your measurement system.

Remember that dataConductor’s gauge r&r results can be easily filtered and sorted, and in combination with other statistics you can quickly spot unusual results. It's easy to drop in a line plot or build a scatterplot to compare appraisers. Sorting the min/mean/max plot from low to high in the default gauge r&r output is a great way to spot whether variability changes as the absolute measurement changes.

Remember too that gauge metrics are there to help you improve your measurement system, but the focus should be on the substance of the metrics and not just the repetition of their use.

Syntricity Enhances Performance by Giving Traditional Disk Drives the Boot

2010-10-11T16:35:16Z

SAN DIEGO, CALIFORNIA, October 11, 2010 – Syntricity, Inc. announced today that it has deployed an enhanced, higher-performance data warehouse for its dataConductor.com™customers. A software-as-a-service (SaaS) offering for semiconductor companies of all sizes, dataConductor.com provides the throughput necessary for data-intensive companies who need to quickly turn data into solutions.

Given today’s system-on-chip (SOC) designs and time-to-market demands, semiconductor companies are generating and storing ever-increasing amounts of data but have less time in which to analyze the results. With customers collecting data from manufacturing facilities around the globe, Syntricity chose a fresh approach to accelerating data warehouse performance by removing spinning hard disks entirely from the equation.

Enter Fusion-io: By using the new flash-based memory tier from Fusion-io (ioMemory), which has the capacity of 1000 memory modules, and the performance of 10,000 disk drives, Syntricity is providing faster and more consistent performance for data loading, summarization, and access.

“With the reduced latency and higher bandwidth of the Fusion-io tier-one storage system, we’re able to take the dataConductor.com data warehouse to the next level,” says Tim Lewis, VP of Operations for Syntricity. “We chose the Fusion-io products because they are easy to work with, deliver excellent performance, and are backed by a truly exceptional technical team.”

“Syntricity is the pioneer in web-native data analysis and yield management solutions for the semiconductor industry, so it’s no surprise that it chose Fusion-io to deliver enhanced data throughput for its customers,” said Tyler Smith, Vice President of Alliances for Fusion-io. “We’ve pioneered a new memory tier of flash-based solid-state technology, and we’re pleased to see Syntricity realize significant performance gains through the application of our Fusion-io products.”

dataConductor.com is the Internet-hosted version of the award-winning dataConductor™ Enterprise Yield Management (EYM) system. Accessed through a web browser, dataConductor.com provides companies of all sizes access to a completely manageable, reliable and secure EYM system scalable to tens of gigabytes of uploaded data per day and multiple terabytes directly accessible online.

About Syntricity, Inc.

Syntricity’s dataConductor 6.1 Lets the Data Do the Talking, Uninterrupted

2010-10-06T16:28:50Z

SAN DIEGO, October 06, 2010 – Syntricity, Inc. has released dataConductor 6.1, the latest version of its enterprise yield management software for the semiconductor industry. The dataConductor platform integrates a high-performance data warehouse with flexible analytics and reporting, enabling engineers to quickly characterize new products, speed the ramp to volume production, and monitor and optimize production yields.

Available immediately, dataConductor 6.1 is available through dataConductor.com®, a software-as-a-service (SaaS) offering, or as an enterprise installation.

Highlights include interactive data sub-selection and drill-down, increased flexibility for production lot dispositioning, improved collaboration/reporting capabilities, end-to-end data management for eFuse/OTP products, and enhanced analytics.

”This release continues our emphasis on flexible solutions for characterization as well as production data analysis,” said Steven Griffith, CEO and Co-founder of Syntricity. “For example, interactive data sub-selection and drilldown enables engineers to quickly distinguish between the news and the noise, while enhancements to retest-based analysis make it easier to disposition production material. We are executing on our vision to significantly improve how engineers analyze and model semiconductor data.”

New features and enhancements to RaCR (a rapid characterization and root cause analysis suite) include flexible line plots and scatterplot tools, ideal for common engineering tasks like tester-to-tester correlation and qualification studies; enhanced drag-and-drop analysis templates; and interactive data sub-selection with successive drilldown capabilities to focus on the key results. With RaCR eBinders® any RaCR-based analysis can now be saved for distribution and collaboration.

The 6.1 release dovetails with recent enhancements to dataConductor.com, which now takes advantage of the latest in solid-state storage to provide data-drenched customers the throughput necessary for all their analysis and reporting needs.

About Syntricity, Inc.

Syntricity is the pioneer in web-based, enterprise yield management in the semiconductor industry. Its software is used by leading semiconductor companies, test vendors and wafer foundries to gather, manage and analyze data from facilities around the world to improve yield, reduce product development cycles, and increase profits. A privately-held company, Syntricity is headquartered in San Diego, California. For more information, e-mail info@syntricity.com or visit http://www.syntricity.com.

Syntricity Provides End-to-end Data Management and Analysis for eFuse-Enabled Products

2010-09-09T19:21:15Z

SAN DIEGO, CALIFORNIA, September 9, 2010 – Syntricity Inc., a leader in data management and analysis for the semiconductor industry, has developed an end-to-end solution for eFuse programmable identification registers. Used for material tracking, fine-tuning individual devices, piracy and hacking prevention, and other applications, eFuse (aka one-time programmable or OTP) registers are generally configured at wafer sort to provide a unique die identification. With Syntricity’s dataConductor® platform, customers now can easily store, track, and analyze data at the die-ID level from wafer sort through final test.

“Increasingly, our customers are relying on eFuse/OTP registers for die-level traceability. This can be a powerful technique, but just tagging a given die with a unique ID is merely the starting point” according to Technical Account Manager Iraj Eklassi. “We’ve added storage and querying capabilities specifically designed for eFuse IDs, including the use of genealogy data, and simplified wafer mapping, correlation by die ID, and other analyses based on the unique eFuse/OTP ID.”

Syntricity’s support for eFuse/OTP-based analysis means that it’s easy to pair data across operations, such as correlation between wafer sort and final test for production or engineering material. In the case of an RMA, the eFuse/OTP capabilities include searching the data warehouse based on the unique die ID and generating reports based on all data collected for that part at all operations. Customers with multi-chip modules (MCMs) who are already using dataConductor’s genealogy tools can now combine them with eFuse support, for a finer level of material traceability.

In many situations, not all tests can be performed at wafer sort (or at least not at the required operating speed). With dataConductor, customers can use eFuse IDs to analyze final test data (for packaged parts) in wafer map tools, looking for patterns or areas of failures just like traditionally done for wafer sort data. By driving yield issues back from final test to the fab, customers realize significant cost savings.

About Syntricity, Inc.

Syntricity is the pioneer in enterprise yield management. Its software is used by leading semiconductor companies, test vendors and wafer foundries to gather, manage and analyze data from facilities around the world to improve yield, reduce product development cycles, and increase profits. A privately-held company, Syntricity is headquartered in San Diego, California.

For more information, contact Syntricity Inc., 10525 Vista Sorrento Parkway, Suite 220, San Diego, CA 92121; Tel: (858) 552-4485; Fax: (858) 552-4493. E-mail info@syntricity.com or visit http://www.syntricity.com.

What's in a Name?

2010-09-07T20:40:21Z

Stigler’s Law of Eponymy states, "No scientific discovery is named after its original discoverer." It was named by Stephen Stigler, who credits the notion to Robert K. Merton (1910-2003). Stephen Stigler’s father, George Stigler, won the Nobel Prize in Economics in 1982. Robert K. Merton’s son, Robert C. Merton, won the Nobel Prize in Economics in 1997.

The Pareto chart that we know today was introduced in 1950 by Joseph Juran² (1904-2008). It’s named for Vilfredo Pareto (1848-1923), an Italian economist who had noted that, for instance, about 85% of the wealth in Milan was owned by 15% of the population and that 80% of the land in Italy was owned by 20% of the population. Juran observed that in many quality improvement situations, a high percentage of the problems were the result of a handful of causes. At one time, Juran referred to this Pareto principle as the ‘vital few and the trivial many’ but later changed that expression to the ‘vital few and the useful many.’ The Juran Trilogy - Quality Planning, Quality Control, and Quality Improvement - is named for Juran, who himself referred to it as the Quality Trilogy.

Student’s t-distribution was developed by William Sealy Gosset (1876-1937), and originally was referred to by the letter z (which in later years came to represent the normal distribution). Gosset worked for the Guiness brewing company, and published under the pseudonym “Student” for reasons of confidentiality. Most of Gosset’s work was published in Karl Pearson’s (1857-1936) journal Biometrika, but it was Sir Ronald Fisher (1890-1962) who had the greater appreciation for Gosset’s work with small sample sizes. Fisher and Pearson could not stand each other, perhaps in part because Fisher exposed a flaw in Pearson’s use of degrees of freedom in the Chi-squared test.

The Bland-Altman plot is a scatterplot of mean differences (represented by the y axis) vs. average values (represented by the x axis). It was introduced in the early 1980s by Martin Bland (1947 - ) and Douglas Altman (1948 - ). It is identical to the Tukey mean-difference plot, introduced several years earlier by John Tukey (1915-2000).

The Cooley-Tukey algorithm for Fast Fourier Transforms was developed by James Cooley (1926-) and John Tukey, whose results were published in 1965. Some years later, it was observed that much of the Cooley-Tukey algorithm had been invented in 1805 by Carl Friederich Gauss (1777-1855), the great German mathematician known for his many mathematical and scientific contributions, including his work with the normal distribution.

The probability density function for the normal distribution, often called the Gaussian distribution for Carl Friederich Gauss, was first described formally by Abraham de Moivre (1667-1754) in 1733. Gauss contributed to the development and use of the normal distribution, especially as it relates to measurement error (‘the normal law of error’), as did Pierre-Simon LaPlace (1749 –1827) and others. Sir Francis Galton (1822-1911) referred to the normal distribution as the normal distribution in 1889; his protégé Karl Pearson referred to the normal distribution as the Gaussian distribution in 1905.

The Deming Cycle - Plan, Do, Study, Act - is named for Dr. W. Edwards Deming² (at one time, the cycle included the term Check instead of Study). Deming credited Walter Shewhart for the development of what we now call the Deming Cycle. It is the precursor to DMAIC (define, measure, analyze, improve, and control), popularized in six-sigma programs. DMAIC itself was originally MAIC, but someone figured out you had better define what you’re solving before you start measuring.

Walter Shewhart (1891-1967) developed the first control charts in the 1920s, which are often referred to as (appropriately enough) Shewhart control charts. Sometimes people refer to control charts that came after Shewhart’s work as ‘modern control charts’ which indeed are younger than Shewhart charts but still in many cases 50 or 60 years old.

Murphy’s Law, commonly cited as “Whatever can go wrong, will go wrong” is named for Edward A. Murphy (1918-2000), an aerospace engineer who said something along the lines of, “Well, I really have made a terrible mistake here, I didn’t cover every possibility.” The person who made Murphy famous is Dr. John Paul Stapp (1910-1999), known to many for his rocket sled research (“Gee Whiz”) and at one time the fastest person on earth (632 MPH on a sled). In a press conference, Stapp referred to Murphy’s Law as we know it today and it took off from there. Those who have studied the origin of Murphy’s Law usually conclude that “Murphy’s Law applies to Murphy’s Law.”³

Gauge Repeatability & Reproducibility

2010-08-24T16:27:32Z

Six-sigma projects and other factors have contributed to the increased popularity of gauge r&r studies. The DMAIC (define, measure, analyze, improve, control) problem solving framework commonly used in six-sigma projects starts by defining a problem followed by the measurement step. Measurement systems analysis is based on the notion that you need to determine the capability of your measurement systems. To put it another way, you need to measure your measurement system.

Gauge R & R is a form of measurement systems analysis, designed to determine the precision of a measurement system. R&R studies are not, in general, designed to directly determine the accuracy of a measurement system. Accurate measurements are centered close to the true value (of course, we usually don’t know the true value), while precise measurements are ones that are close to each other. This means, for example, that you can be accurate but not precise – an average near the true value makes for accuracy, but lots of variability makes for a lack of precision. In general, gauge R&R studies focus attention on the capability of the measurement system to get consistent results.

The first ‘R’ represents repeatability, also known as pure measurement error. If we measure a parameter on the same widget several times with the same measurement system and get about the same result each time, we can say that we have good repeatability. Measuring something over and over and getting about the same result each time does not make those results accurate, but it’s almost always good news. If we determine that measurements are not especially accurate but they are repeatable, then we can try to find the source or sources of the inaccuracy and work them out of the measurement process. For instance, we may have a calibration issue, and if we can fix that issue suddenly we have accuracy and precision combined.

In the R&R world, the second ‘R’ represents reproducibility, which is a measure of (for example) whether two or more operators can measure the same parts and get the same average results. Organizations such as AIAG and ASTM consider equipment and time as reproducibility factors. You might collect data across multiple program revisions, or run an experiment with different settling times or sampling rates, and consider these to be reproducibility factors. Repeatability and reproducibility, combined, form the total measurement system error (or gauge error). This may sound a bit awkward, but reproducibility is not the ability of operators (or equipment) to get the same level of repeatability. The repeatability results for different operators could be quite different but if the average measurements are comparable then the reproducibility is good.

What constitutes good repeatability or good reproducibility? The answer is, of course, “It depends.” Still, there are several, commonly used gauge metrics aimed at helping you decide whether your r&r results are acceptable. We’ll comment on that in an upcoming data blog.

SYNTRICITY'S DEPLOYMENT STRATEGY TURNS DATA INTO SOLUTIONS

2010-08-23T13:40:50Z

SAN DIEGO, CALIFORNIA, July 7, 2010 – Syntricity’s experience in deploying its dataConductor semiconductor yield managements system, including broad support for standard as well as proprietary data formats, continues to help customers establish the best-practices necessary for data collection, automated file feeds, and data analysis to turn data into solutions.

After more than a decade of deployments, the message is clear: Whether it is product characterization, faster ramping to volume production, yield improvement, or die traceability, optimum results depend upon the data infrastructure established during deployment.

Syntricity’s deployment process starts before the customer is a customer. That is, deployment begins when a customer-to-be chooses to do a formal evaluation of dataConductor. Customers understandably want to evaluate software based on their own data, which lets them see the potential of dataConductor to bring new insights about the data. This is possible because of Syntricity’s support for the most common data formats (such as those from the top foundries and ATE vendors), and its ability to quickly build the capability for custom formats. By working with companies closely in the evaluation phase, the basis of a solid data infrastructure is formed from the outset.

“Some of our customers have just a few data formats, while others have a dozen or more, fed from facilities across the globe. By seeing deployment as a process where we learn about customer needs and they learn about our capabilities, we’ve gained tremendous knowledge about what needs to be done as well as how to actually get it done,” said Steven Griffith, CEO of Syntricity.

Available as an enterprise application or through a hosted, software-as-a-service arrangement, dataConductor is helping large and small companies alike turn data into solutions.

About Syntricity, Inc.

For more information, contact Syntricity Inc., 10525 Vista Sorrento Parkway, Suite 220, San Diego, CA 92121; Tel: (858) 552-4485; Fax: (858) 552-4493. Or visit http://www.syntricity.com.

The Myth of Seven

2010-07-01T07:15:03Z

Samurai use swords, not bullets

It's been 50+ years since Dr. George Miller published The Magical Number Seven, Plus or Minus Two: Some Limits on our Capacity for Processing Information (1956) in The Psychological Review. Over the years, many people (knowingly or not) have used this article to support the notion that you should, for example, limit the number of items in a list to seven, the number of bullets on a slide to seven, or the number of steps in a procedure to seven (all plus or minus two, of course). Dr. Miller made many comments on this misinterpretation for many years, including the following statement: "7 was a limit for the discrimination of unidimensional stimuli (pitches, loudness, brightness, etc.) and also a limit for immediate recall, neither of which has anything to do with a person's capacity to comprehend printed text."

Some comments and correspondence between Mike Halpern and George Miller can be found at http://members.shaw.ca/philip.sharman/myth.html

Thinking Outside the Boxplot

2010-07-01T07:15:35Z

Boxplots and Outside Values

"The normal distribution, for example, is clearly the template for the selection of fence locations." Dr. James Thompson, The Age of Tukey,Technometrics, August 2001.

The basic boxplot is a graphical representation of the five-number summary: the minimum, 25th percentile (also called first quartile), 50th percentile (the median, or second quartile), 75th percentile (or third quartile), and maximum values. You don’t need to assume this or that distribution in order to determine these five numbers – just sort the data and determine the appropriate percentiles.

Beyond the basic boxplot, however, is Dr. John Tukey’s exploratory data analysis (EDA) boxplot that includes the notion of “fences” and “outside values.” An outside value is a value which is below the lower or above the upper fence. Fine, but how are fences defined? First, note that the interquartile range (IQR) is defined as the difference between the 75th and 25th percentiles: That is, IQR = 75th percentile – 25th percentile. Also, note that there are at least two types of fences: inner and outer. Inner fences are defined as: lower inner fence = 25th percentile – 1.5*IQR and upper inner fence = 75th percentile + 1.5*IQR. Outer fences are defined as: lower outer fence = 25th percentile – 3*IQR and upper outer fence = 75th percentile + 3*IQR.

If you assume a Gaussian (normal) distribution, how can we interpret these fences? A Gaussian distribution’s 75th percentile corresponds to the mean + 0.6745 standard deviations, and its 25th percentile corresponds to the mean – 0.6745 standard deviations. This means the IQR represents 1.349 standard deviations. Inner fences represent mean +/- 2.698 standard deviations or 99.30% of the data, while outer fences represent mean +/- 4.7215 or 99.9998% of the data.

What if the data are Gaussian (normal) and the sample size is 1000? 99.3% of 1000 is 993, which suggests that we might see around 7 outside values. If these values are just outside the inner fences, we shouldn't be surprised. However, if values are outside the outer fences then (given the sample size) we need to investigate further.

What if the underlying distribution is not symmetrical (say, lognormal)? Even with relatively small sample sizes, you shouldn't be surprised to see outside values, and possibly even values outside the outer fences.

SYNTRICITY REPORTS PROFITABILITY FOR 2009 - SEES INCREASED DEMAND FOR ITS PRODUCTS AND SERVICES

2010-07-01T01:29:15Z

SAN DIEGO, CALIFORNIA, January 21, 2010 - Syntricity Inc. announced today profitability for the calendar/fiscal year 2009, including four successive quarters of profitability. The company pointed to increases in sales both of products and services as well as improved operational efficiency as key achievements. Significant milestones in 2009 include the release of its rapid characterization and root cause (RaCR) analysis suite, the rollout of a powerful gauge repeatability and reproducibility application, and the announcement of the ability to store and process over a trillion measurements per year using its proprietary database architecture.

"We are obviously very pleased to have been profitable for the year," said Steven Griffith, president and CEO of Syntricity. "It’s no surprise that in today’s economic environment, our customers are more focused than ever on improving yields and reducing costs. We know that 2009 was a difficult year for many, but we are excited to have added new customers during the year. It was also very satisfying to sign a three-year extension with our largest customer, a Fortune 500 company that has been working with us for over a decade."

Given the need to analyze and manage ever-increasing amounts of data as quickly as possible, Syntricity developed RaCR to bring the power of drag-and-drop analytics to engineers solving advanced characterization and yield enhancement issues. Template-based analysis tools are augmented with powerful applications in RaCR, including Syntricity’s Gauge R&R application for releasing hardware into volume production. With the growing emphasis on parametric data collection, the data warehouse has been enhanced through pipelined data loading to meet customers’ current and future requirements.

"A decade ago, the notion of a Web-based data analysis platform was a novelty. Now, the idea that all data are available at any time through the Web is fundamental to many businesses," continued Griffith. "Given the global nature of the supply chain, our customers need ready access to accurate and complete data to make the best possible decisions in a timely manner. That’s why the dataConductor platform continues to be the basis for data analysis, yield management, and reporting for our customers as they develop the next generation of semiconductor products. We’re looking forward to continued success in 2010 and beyond."

About Syntricity, Inc.
Syntricity is the pioneer in enterprise yield management in the semiconductor industry. Its software is used by leading semiconductor companies, test vendors and wafer foundries to gather, manage and analyze data from facilities around the world to improve yield, reduce product development cycles, and increase profits. A privately-held company, Syntricity is headquartered in San Diego, Calif.

For more information, contact Syntricity Inc., 10525 Vista Sorrento Parkway, Suite 220, San Diego, Calif. 92121; Tel: (858) 552-4485; Fax: (858) 552-4493. Or visit http://www.syntricity.com.

SYNTRICITY'S dataConductorEP 6.0 MAKES DATA-DRIVEN DECISIONS AS EASY AS DRAG-AND-DROP

2010-07-01T07:28:26Z

SAN DIEGO, CALIFORNIA, September 9, 2009 - Syntricity Inc., the pioneer in web-native analysis and reporting for the semiconductor industry, has released dataConductorEP 6.0, which combines powerful, template-based analytics with an enhanced, high-performance data warehouse. Ease-of-use continues to be a hallmark of the product, with drag-and-drop analysis objects, intuitive filtering, and point-and-click sorting. The power and flexibility of the new offering redefine how engineers will interact with data to reach conclusions and make decisions.

"When dataConductor debuted, our vision was to provide a powerful, easy-to-use product that helped engineers make decisions faster," said Steven Griffith, president and CEO of Syntricity. "This latest release uses analysis templates to build powerful applications, including a gauge repeatability and reproducibility app, the first to be built on our RCR Web 2.0 analytic platform. It is much easier to use than the gauge capabilities in other packages because it fits precisely with how semiconductor product engineers collect data in back-end operations. We’re delighted that our largest customer, a Fortune 500 company, has selected the 6.0 gauge r&r application as its new standard for gauge studies."

Building upon the industry’s most powerful data warehouse architecture, dataConductorEP 6.0 now traces multiple dice that go into a package, and the non-die components as well. "The customer can use the system to identify bad components of, for example, an MCM/SIP RMA, in addition to identifying other MCMs built from problematic components," continued Griffith. "Warehouse enhancements have also been made to improve dataConductor’s ability to analyze all ATE hardware at both a detail and aggregated level. ATE hardware includes site-level information as well as every piece of equipment connected to the test head."

Features such as flexible attribute grouping and thumbnail previews have long provided dataConductorEP customers with an easy-to-use platform for making data-driven decisions. dataConductorEP 6.0 delivers powerful, template-based analytics that can be used as-is or easily customized with drag-and-drop analysis objects, point-and-click sorting, and flexible filtering. Any customized template can be saved and reused with any dataset, reducing the time it takes to reach conclusions and make decisions.

Available immediately, dataConductorEP 6.0 is supported as an enterprise installation or hosted by Syntricity on dataConductor.com, a software-as-a-service (SAAS) offering.

About Syntricity, Inc.
Syntricity is the pioneer in enterprise yield management in the semiconductor industry. Its software is used by leading semiconductor companies, test vendors and wafer foundries to gather, manage and analyze data from facilities around the world to improve yield, reduce product development cycles, and increase profits. A privately-held company, Syntricity is headquartered in San Diego, California.
For more information, contact Syntricity Inc., 6175 Nancy Ridge Drive, Suite 100, San Diego, California, 92121; Tel: (858) 552-4485; Fax: (858) 552-4493. Or visit http://www.syntricity.com.

SYNTRICITY BREAKS THE ONE-TRILLION MEASUREMENTS-PER-YEAR BARRIER

2010-07-01T02:45:57Z

SAN DIEGO, CALIFORNIA, June 17, 2009 - Syntricity Inc., a leader in semiconductor yield management solutions, announced today the capability of loading over a trillion measurements per year using its newly enhanced dataConductorEP™ warehouse. Anticipating that the data collection and analysis needs of its customers will continue to grow, Syntricity has produced a 10x improvement over what was already an industry leading warehousing architecture.

"While the technical details are impressive, it’s the practical implications that we are excited to share with our customers," said Steven Griffith, president and CEO of Syntricity. "Many people think of bin results when they think of yield management, but parametric data really are at the core of yield monitoring and improvement. Syntricity is convinced that the latest dataConductor warehouse innovations will support not only today’s needs for our customers, but tomorrow’s demands as well."

Designed with the semiconductor supply chain in mind, the dataConductorEP warehouse allows decision makers access to the entire spectrum of product data. By strategically uniting standard and proprietary methods, Syntricity provides its customers with the ability to keep several quarters of parametric and bin data online, instantly available for analysis and reporting. This capability is available to all customers, including those hosted by Syntricity on dataConductor.com, a software-as-a-service (SAAS) offering.

Given the combination of sophisticated system-on-a-chip designs and very high manufacturing volumes, the sheer volume of data collected on a product can be in the hundreds of millions or even billions of measurements per day. To handle these requirements, the warehouse combines a multithreaded parsing process with parallel data loading. Customers benefit immediately because the new warehouse requires no modifications to existing parsers, and there are no special commands or complicated query languages to learn.

About Syntricity, Inc.
Syntricity is the pioneer in enterprise yield management in the semiconductor industry. Its software is used by leading semiconductor companies, test vendors and wafer foundries to gather, manage and analyze data from facilities around the world to improve yield, reduce product development cycles, and increase profits. A privately-held company, Syntricity is headquartered in San Diego, California.
For more information, contact Syntricity Inc., 6175 Nancy Ridge Drive, Suite 100, San Diego, California, 92121; Tel: (858) 552-4485; Fax: (858) 552-4493. Or visit http://www.syntricity.com.