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Exam Code: 920-270 Nortel WLAN 2300 Rls. 7.0 Planning & Engineering testing January 2024 by Killexams.com team
Nortel WLAN 2300 Rls. 7.0 Planning & Engineering
Nortel Engineering testing

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920-270
Nortel WLAN 2300 Rls. 7.0 Planning & Engineering
https://killexams.com/pass4sure/exam-detail/920-270
Question: 28
A new WLAN 2300 series customer does not use a RADIUS server. They want to use a local
database to authenticate users and apply restrictions to the users. Which feature will support
this?
A. user-id networking
B. MAC-id networking
C. identity-based networking
D. authentication-id networking
Answer: C
Question: 29
A new WLAN 2300 series customer uses a RADIU Sserver and has a local database on a
different server. They want the RADIUS server to be used for security on the WLAN.
Which database type would be compatible?
A. EAP
B. LDAP
C. user-id
D. password
Answer: B
Question: 30
A new WLAN 2300 series customer uses a RADIU Sserver and has a local database on a
different server. They want the RADIUS server to be used for security on the WLAN, using the
database and mutual authentication. What is required on the RADIUS server?
A. certificates
B. user names
C. client names
D. mutual ID numbers
Answer: A
Question: 31
Which statement accurately describes the Virtual Cluster feature?
A. The access points must be configured manually to map to the clustered switches.
B. All members of a configuration cluster have a local copy of the domain configuration.
C. Clustered switches act collectively as single virtual switch for wireless configuration.
D. The configuration cluster is a subset of security switches in different mobility domains.
Answer: C
7
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Thu, 13 Aug 2020 22:30:00 -0500 en text/html https://ung.edu/testing/
Test Engineers In Very Short Supply

Semiconductor design, verification, manufacturing, and test requires an army of engineers, with each playing a special role. But increasingly, these disciplines also require additional training to be able to understand the context around their jobs, and that is making it harder to fill different positions at a time when the chip industry already is severely short-staffed.

This is particularly true for engineers whose jobs traditionally came in later in the flow — the test, process, and yield engineers. Increasingly these disciplines are shifting further left, and playing a more vital role in critical applications. Knowledge in all of those areas, as well as others such as inspection, metrology and analytics, is now required to get chips out the door more quickly and to Improve reliability throughout a device’s expected lifetime.

“As a test engineer, you’re likely to pick up on characteristics, trends and anomalies that the design and production teams won’t,” says Marie Ryan, a marketing executive at yieldHUB. “It is vital that test and yield are considered early in design now, but considerations such as power and security are morphing into the test engineer’s job by default.

Much of this has to be learned on the job for a couple reasons. First, curriculums at most universities are relatively fixed, so gathering expertise often means taking additional courses outside of the required classes. And second, there are so many changes underway with the slowdown in scaling, the resulting increase in heterogeneous advanced packaging, as well as the introduction of novel architectures for new markets, that the chip industry is far outpacing the trainers.

“Test requires a lot of multidisciplinary knowledge, and it’s not something that’s only breadth — it requires depth, as well,” said Rob Knoth, product management director at Cadence. “That’s what makes it particularly challenging. Some of the better test engineers that we work with are people who maybe didn’t start off as test people. There are people who were just regular RTL designers or regular semiconductor engineers, and they broadened into test. That allows them to bring a pretty powerful toolkit to bear on the problem.”

Test engineering is a highly valued expertise, and that discipline is becoming even more valuable as chips are expected to last longer in the field, and as they are used in mission- and safety-critical markets such as servers, automotive, and medical applications. “If you don’t do that job well, it can result in a pretty big end cost to the company. And so, that’s driving it, as well,” said Knoth.

Hard to train, hard to fill
Hiring is made more difficult by the fact that for years, the chip industry has been competing for engineers against the likes of Apple, Google, Facebook and Amazon. Software was considered the future until the past several years, when AI/ML entered into the picture and Moore’s Law began to slow down. Suddenly, intelligence needed to be added into everything, and some of the most interesting engineering challenges moved into hardware rather than software.

“There is a lot of industry competition for the high-demand skill sets required for machine learning, for example, and therefore can be a challenge to fill,” said Andee Nieto, senior vice president and chief people officer at Xilinx. However, “the hard-to-fill jobs are what we call combo jobs, where companies have merged job skills together to form a new ‘hybrid’ type of role. When they combine these skill sets into one position, it makes it a lot harder to find the right person.”

Test, which for years was stuck somewhere in the middle of the pack when it came to hardware engineering, suddenly became much more challenging over the past few years. Test budgets were increased, up from a flat 2% of manufacturing costs, and analytics were added into the mix in order to make sure chips would be able to withstand environmental, electrical and even unplanned events and continue working according to spec.

“The biggest difficulty in hiring is everything related to test,” said Cadence’s Knoth. “That’s the full gamut from test architecture, down to test implementation, to post-silicon, and debug. It’s a natural outgrowth of a lot of different trends. One of the big ones that was an early driver of that was the tremendous rise of semiconductors in safety-critical, high-reliability types of situations, and the pool of candidates is pretty small. You can never have enough engineers, but you always have to make do with the budget and headcount that you have.”

Test engineering isn’t just one thing, either. There are many types of test, from lab to fab. It plays a role at the very front of the design cycle, where a test strategy needs to be incorporated at the architecture stage, or very soon afterward. If a device cannot be tested, it will never make it to market.

“Definitely, it is a skill set where it gets pushed a lot to be very productive, but engineering isn’t unique in this,” said Knoth. “This is pretty much across the board. There’s a high degree of efficiency and a big attention paid on margin, overall. And so people always have been struggling with how to do more with less. We could definitely use more test engineers, but the bigger question is whether companies can hire as much as they want. Looking at some of our partners who are trying to hire for these positions, though, it takes a long time to fill. It takes them a really long time to fill.”

The new stuff
What most engineering managers didn’t account for is just how much of an impact machine learning and data analytics would play in the test world. Test data always has been important for quality control, but there are many ways to use that data more effectively than in the past. Understanding how to utilize that data has become a focal point throughout the manufacturing flow, with an increasing amount of it being looped back to other processes in real-time.

“To control the quality of the metadata, it really has to be done at the test, by the tester, and with some kind of interface to the company’s MES system,” said Keith Arnold, senior director of solutions at PDF Solutions. “You can take as much of that away from the operator as possible, and the test programmer, and just say the test program doesn’t need all this information. But we do need to have this information. It has to get populated properly because it is too free-form at this point. You want to be able to control the test flow, you want to be able to control it even dynamically while the tester is testing.”

Data is playing a larger role everywhere, from design through manufacturing. “Today we are producing inordinate amounts of data, driven by billions of devices connected to the Internet. The future is really about extracting value from this data,” said Nieto.

As a result, a whole new skill set is being layered upon other layers of skills. And with much of the advanced manufacturing being done in places like China, Taiwan and South Korea, finding qualified people outside of those areas is becoming much more difficult than in the past.

Crossovers and adjacencies

Test is starting to push beyond its traditional boundaries in other ways, as well. This is particularly true for security, which has been intricately bound with test in the past, particularly around counterfeiting.

“Cybersecurity has been really neglected in semiconductor design,” said Andreas Kuehlmann, CEO of Tortuga Logic. “There are very limited skills in security. How do you develop secure chips? How do you test secure chips? And how do you prove it is good silicon? This is same trend I’ve seen in software. Years ago we were running around saying software developers don’t know anything about security or how to develop secure software.”

To get from point A to a secure point B, Kuehlmann said that security expertise needs to be embedded with the design, verification, test, and manufacturing teams in a federated system of security responsibility. The team has to understand who is responsible for security — someone has to have it in their title — yet everyone needs to understand security.

“We created a new job title security application engineer that’s really dedicated for the field, working with customers,” said Kuehlmann. “Security is actually an organizational and people problem. A tiny mistake design can have catastrophic consequences. You just get a few lines of code wrong and you have a big opening for cybersecurity. But security was a side job of the testing team to do security testing. We are seeing really an exponential growth right now.”

Test engineers need security expertise. But so do verification engineers. And both verification and security engineers need a working knowledge of test. “This affects everything from just regular beta testing to actually doing penetration testing on the chip of the systems,” he said.

Another area that intersects more closely with test engineering is low power. “We’re at a beautiful place where we’re able to do something about power,” said Knoth. “That’s just one subset of this broader syllabu of data analytics, where we create reams and reams of data in the process of designing semiconductor products. The whole point is now building the more efficient software and systems to do something smart with all that data.”

The power area has its dedicated job titles. While none of them specifically crosses into test, increasingly there is a focus on making sure a device can be proven to work as expected. “I’ve seen a lot of different titles like, power architect, power convergence lead, powers czar,” he said. “It all varies depending on how creative the hiring managers are, but essentially what it gets down to is an engineer who has one foot in the implementation space and one foot in the verification space. They have to be able to understand the challenges of the people who are writing the RTL, who are actually designing what’s going to become the semiconductor from a functional standpoint, as well as the people who are writing the software that’s going to be on that semiconductor device, because what sort of software they’re running on the product will greatly impact how much power it’s going to burn. They have to understand simulation, they have to understand emulation, they have to understand the RTL to GDSII flows for actually implementing the design to make sure that all the good intentions that were set up by the architects actually translate themselves into silicon. It’s a very broad skill set that requires depth in certain areas, so that it’s more than just understanding the high-level concepts, but it’s actually knowing how the second order effects could manifest themselves in bigger problems down the line.”

A good job
While many sectors continue to experience ups and downs, the semiconductor industry has been remarkably stable, in part because of all the new markets that rely on ICs. Chip demand is forecast to increase 8.4% in 2021, according to the World Semiconductor Trade Statistics (WSTS) organization. Yet a shortage of engineers combined with technological challenges — more data analytics, automation, smaller nodes, more than Moore techniques, safety critical, security, 5G mmWave — adds complexity to jobs, along with lots of new opportunities engineers and technicians.

The industry has been talking about a shortage of talent for years. And as the chip industry expands across new markets, that shortage is becoming more acute.

“On a bigger-picture level, our industry is hard to staff in general due to, 1) people not knowing about it or having incorrect assumptions about the work; 2) losing engineers to software and social media companies, as those companies and that industry is much better known; and 3) the nature of the work in our industry being more inflexible and physically challenging (e.g. working in clean rooms, working very specific shifts, etc.),” said Shari Liss, executive director of the SEMI Foundation. “But our advantage is our jobs are steady, the pay is good, and the industry offers strong career paths. Also, our industry is high-specialized. From entry-level positions like equipment technicians all the way to engineering/project management can require training and experience that is not commonly found. We are an industry operating on the edge of innovation,” said Liss.

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Mon, 07 Dec 2020 10:00:00 -0600 en-US text/html https://semiengineering.com/test-engineers-in-very-short-supply/ Software Testing

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Engineering practices that advance testing

Testing practices are shifting left and right, shaping the way software engineering is done. In addition to the many types of tests described in this Deeper Look, test-driven development (TDD), progressive engineering and chaos engineering are also considered testing today.

TDD
TDD has become popular with Agile and DevOps teams because it saves time. Tests are written from requirements in the form of use cases and user stories and then code is written to pass those tests. TDD further advances the concept of building smaller pieces of code, and the little code quality successes along the way add up to big ones. TDD builds on the older concept of extreme programming (XP).

RELATED CONTENT: There’s more to testing than simply testing

“Test-driven development helps drive quality from the beginning and [helps developers] find defects in the requirements before they need to write code,” said Thomas Murphy, senior director analyst at Gartner.

Todd Lemmonds, QA architect at health benefits company Anthem, said his team is having a hard time with it because they’re stuck in an interim phase.

“TDD is the first step to kind of move in the Agile direction,” said Lemmonds. “How I explain it to people is you’re basically focusing all your attention on [validating] these acceptance criteria based on this one story. And then they’re like, OK what tests do I need to create and pass before this thing can move to the next level? They’re validating technical specifications whereas [acceptance test driven development] is validating business specifications and that’s what’s presented to the stakeholders at the end of the day.”

Progressive Software Delivery
Progressive software delivery is often misdefined by parsing the words. The thinking is if testing is moving forward (becoming more modern or maturing), then it’s “progressive.” Progressive delivery is something Agile and DevOps teams with a CI/CD pipeline use to further their mission of delivering higher-quality applications faster that users actually like. It can involve a variety of tests and deployments including A/B and multivariate testing using feature flags, blue-green and canary deployments as well as observability. The “progressive” part is rolling out a feature to progressively larger audiences.

“Progressive software delivery is an effective strategy to mitigate the risk to business operations caused by product changes,” said Nancy Kastl, executive director of testing services at digital transformation agency SPR. “The purpose is to learn from the experiences of the pilot group, quickly resolve any issues that may arise and plan improvements for the full rollout.”

Other benefits Kastl perceives include:

  • Verification of correctness of permissions setup for business users
  • Discovery of business workflow issues or data inaccuracy not detected during testing activities
  • Effective training on the software product
  • The ability to provide responsive support during first-time product usage
  • The ability to monitor performance and stability of the software product under real production conditions including servers and networks

“Global companies with a very large software product user base and custom configurations by country or region often use this approach for planning rollout of software products,” Kastl said.

Chaos Engineering
Chaos engineering is literally testing the effects of chaos (infrastructure, network and application failures) as it relates to an application’s resiliency. The idea originated at Netflix with a program called “Chaos Monkey,” which randomly chooses a server and disables it. Eventually, Netflix created an entire suite of open-source tools called the “Simian Army” to test for more types of failures, such as a network failure or an AWS region or availability zone drop. 

The Simian Army project is no longer actively maintained but some of its functionality has been moved to other Netflix projects. Chaos engineering lives on. In fact, Gartner is seeing a lot of interest in it.

“Now what you’re starting to see are a couple of commercial implementations. For chaos to be accepted more broadly, often you need something more commercial,” said Gartner’s Murphy. “It’s not that you need commercial software, it’s going to be a community around it so if I need something, someone can help me understand how to do it safely.”

Chaos engineering is not something teams suddenly just do. It usually takes a couple of years because they’ll experiment in phases, such as lab testing, application testing and pre-production. 

Chris Lewis, engineering director at technology consulting firm DMW Group, said his firm has tried chaos engineering on a small scale, introducing the concept to DMW’s rather conservative clientele.

“We’ve introduced it in a pilot sense showing them it can be used to get under the hood of non-functional requirements and showing that they’re actually being met,” said Lewis. “I think very few of them would be willing to push the button on it in production because they’re still nervous. People in leadership positions [at those client organizations] have come from a much more traditional background.”

Chaos engineering is more common among digital disruptors and smaller innovative companies that distinguish themselves using the latest technologies and techniques.

H2: Proceed with caution

Expanding more testing techniques can be beneficial when organizations are actually prepared to do that. One common mistake is trying to take on too much too soon and then failing to reap the intended benefits. Raj Kanuparthi, founder and CEO of custom software development company Narwal, said in some cases, people need to be more realistic. 

“If I don’t have anything in place, then I get my basics right, [create] a road map, then step-by-step instrument. You can do it really fast, but you have to know how you’re approaching it,” said Kanuparthi, who is a big proponent of Tricentis. “So many take on too much and try 10 things but don’t make meaningful progress on anything and then say, ‘It doesn’t work.”

Tue, 01 Sep 2020 12:00:00 -0500 en-US text/html https://sdtimes.com/test/engineering-practices-that-advance-testing/
Civil Engineering Materials Testing Equipment No result found, try new keyword!Civil engineering testing equipment is used in the quality control processes associated with the analysis of soils, concrete, asphalt, bitumen, cement and mortar, steel, aggregates, and other ... Thu, 02 Jun 2011 12:27:00 -0500 en text/html https://www.azom.com/materials-equipment.aspx?cat=116 Engineering writing test

The EWT is offered several times per year, generally near the beginning and the end of every semester. Upcoming test dates are posted on the EWT registration portal, as well as on the information board in the Centre for Engineering in Society, near EV 2.249.

You must register in advance to take the test.

If you are a current Gina Cody School undergraduate student, you may register yourself on the EWT website. Use your ENCS account and password to log in and register. From outside Concordia, you must use VPN/MFA to access this app.

If you are not a current Gina Cody School undergraduate student, or if you any difficulties registering for the test, you should contact the test coordinator to register. Please see the test coordinator’s contact information on the lower right-hand side of this page.

Once you register for an EWT session, you must take the test. Unexcused absences count as failed attempts. If you have a valid reason for not attending the test after you have registered, you must contact the test coordinator before the date of the test to cancel your registration.

The results of the EWT are posted within one week of the test. You will find the results online in the registration portal, and also posted on the information board in the Centre for Engineering in Society, near EV 2.249.

If you pass the EWT, your results will be transmitted to Student Academic Services and you will be released to register for ENCS 282.

If you fail the EWT, you may attempt the test a second time, or you may choose to enroll in ENCS 272. After two failed attempts, students will be blocked from further registration into the EWT and will have to take ENCS 272 in order to fulfill the writing skills requirement.

Bring your student ID card to the test, as well as several pens. Please note that you may NOT bring the following into the EWT:

  • Dictionaries
  • Phones
  • Calculators
  • Other electronic devices
Thu, 09 Apr 2015 13:42:00 -0500 en text/html https://www.concordia.ca/ginacody/students/new-students/writing-test.html
Testing ICs Faster, Sooner, And Better

The infrastructure around semiconductor testing is changing as companies build systems capable of managing big data, utilizing real-time data streams and analysis to reduce escape rates on complex IC devices.

At the heart of these tooling and operational changes is the need to solve infant mortality issues faster, and to catch latent failures before they become reliability problems in the field.

“People are looking at how can they attain the highest device quality, and we need to start thinking about solving this problem in a different way,” said Eli Roth, engineering director at Teradyne. “One approach certainly gets into data analytics, AI and machine learning, because when you’re at one part-per-billion defect rates, you’re looking way beyond Six Sigma. You’re really looking at tails of the distribution and trying to find infant mortality issues. It’s a question of piecing together data from inspection steps and the electrical characterization, and all the data our products can provide to reach the quality requirements.”

Engineers in the test community face numerous challenges today. “Device scaling and transistor density increases continue, creating some interesting defect modes and process variability that we have to deal with in the test world,” said Matthew Knowles, product management director for hardware analytics and test at Synopsys, in a latest presentation at ITC. “The packaging revolution is happening and we need 3D integration and chiplets. And when we put these systems of systems into larger systems, they get more complex. There’s an enormous amount of data that needs to be understood and aggregated to understand the reliability and performance of these systems. So there’s defect and yield optimization that needs to be done reliably to reach extremely high test coverages for hyperscaler and automotive applications, for example.”

Knowles pointed to latest innovations introduced by test companies (see figure 1). “Over the past couple of years we’ve seen a number of developments happen in the industry,” he said. “These include test fabric and very advanced compression techniques to handle high pattern densities. RTL DFT has been around to help us shift left, enabling time-to-results by making improvements upstream. And power-aware ATPG has been introduced for safe, controlled testing.”

Fig. 1: AI-driven automation, monitoring and analytics help to address escalating test requirements. Source: Synopsys

Fig. 1: AI-driven automation, monitoring and analytics help to address escalating test requirements. Source: Synopsys

One way chipmakers are improving quality is by making better use of design and verification data downstream. “There’s an entirely new effort to take all of the perspective that you’ve gathered during the validation and verification period and ensure that it’s well tied to how you measure the device as it goes into production,” said Robert Manion, vice president and general manager of the Semiconductor and Electronics Business Unit at NI Test & Measurement, an Emerson company. “In production, you’re significantly reducing the overall number of tests that you might be running on the part, but you still need to validate that it’s going to maintain the performance you observed through the validation and characterization cycles.”

Nearly every equipment provider in the semiconductor industry is employing ML or AI to help perform new analyses of the data or to conduct operations more quickly and cost effectively. Nowhere in manufacturing is the triangle of yield, quality, and cost more critical than in semiconductor test.

Data analytics and AI clearly are being embraced by the industry as enablers. However, much data processing must happen before the first program is run.

“We talk about a lot of these challenges — data security, data access, who owns the data, data IP, secure transfer, and data corruption,” said Nitza Basoco, technology and market strategist at Teradyne, in an ITC presentation. “Identifying the right data and its context (including metadata) are critical for good outcomes with AI, but so are the models. Imperfections in the model can lead to unexpected results. You also need to ensure the data is properly structured for the system to interpret, and that it’s delivered securely without any opportunity for modification by an unauthorized systems. What I see most often overlooked is the importance of a clear objective for the AI. It is key that the system is set up to optimize for the criteria that matter to you.”

It’s also important for companies to collaborate in order to share data, which often is kept in siloes that are too narrow and outdated. “Sometimes you realize you need information you don’t have, and that’s an opportunity to collaborate,” added Basoco. “Companies are collaborating at every level to aggregate all the data and apply machine learning models to gain insights.”

Insights involving performance typically are gained through data collected from sensors integrated into devices and equipment. “The next evolution that’s really going on involves gaining device insights, and that’s where the customers are finding competitive advantage based on what they infer from their own devices,” said Teradyne’s Roth. “The nirvana step is when the customer can do feedback and feed-forward for real-time responses in production. That’s why we’re trying to establish an open environment, where the data is available and secure, to enable customers to develop and provide that competitive advantage.”

An open architecture can utilize analytics from third parties or other applications best suited to the devices being tested (see figure 2).

Fig. 2: Open analytics solution can provide local test optimization and rapid data analysis in a secure, bi-directional data stream. Source: Teradyne

Fig. 2: Open analytics solution can provide local test optimization and rapid data analysis in a secure, bi-directional data stream. Source: Teradyne

A big part of today’s test environment involves prompt data access. “Getting real time data is probably one of the most important pieces that we all ignore but we know we need — how fast you get that data,” said David Vergara, senior director of sales at Advantest. “What’s important when you test is, when a device fails or doesn’t fail, you can figure out the cause. You have the data behind it to go and solve the problem.”

Advantest Cloud solutions is a real-time data infrastructure that typically combines on-die sensor readings or monitors, an edge computing to execute complex analytics workloads, and a unified data server. The edge computer sends outcomes of inferences back to a unified server on the test floor that securely downloads analytics during test sessions. The data processing needs span from on-chip monitors to wafer probe, package test and system level test (see figure 3).

Fig. 3. Testing needs now include greater test content from wafer probe through SLT. Source: Advantest

At the same time companies are streamlining advanced test pattern generation (ATPG) and improving design for test (DFT) methodologies. With each new device generation, testing requirements get upgraded.

Device needs change
Testing requirements change depending on the expected performance and expected lifespan of the device or system under test.

“If we look at the RF and wireless space, for example, our customers are trying to prepare for new standards from bodies like 3GPP, which adds new measurement requirements,” said NI’s Manion. “As we look at things like FR1, FR2, FR3, companies are regularly having to adapt their test approach to ensure they’re validating their product against parts of those standards. This means new frequency ranges, new bandwidth, and new waveforms. It changes the number of tests they have to run, the types of interference they might be looking for, or corner cases that they may have to address. This is what’s ultimately driving a lot of test development activities early in the cycle — to try to prepare for proving those things out on new designs.”

Manion points to dynamic changes in the MEMS space, as well. “We’re seeing an explosion of new and increasingly digital and smart sensor devices, which require high-performance analog measurements that were not required previously,” he said. “This is driving our customers to increase the amount of analog testing that they’re doing with those devices.”

ATPG
The function of advanced test pattern generation (ATPG) is to enable the test equipment to differentiate between correct circuit behavior and faulty circuit behavior caused by defects. Many companies are using AI/ML algorithms to settle on fewer test patterns, and to do it faster.

“Automatic test pattern generation is both an art and a science,” said Synopsys’ Knowles. “You have an ATPG engine that’s going to be doing the simulations, and an engineer needs to come up with an optimal set of parameters that gives the coverage they want while minimizing that pattern count and test cost. But it doesn’t always work perfectly in the first round, and so the engineer has to iterate. Each one of these iterations can be very, very long — days even — and the quality of results is probably not optimal, even after all that time. And you can’t even predict how long it will take, which is a problem. That’s where test-based optimization comes in. By taking user targets and settings, the AI can explore the parameter space automatically, learning as it goes along. It tells the user up-front how long the analysis will take. We’ve run many types of designs with many different patterns, and we’re seeing the test optimization provide up to an 80% reduction in pattern count and a 20% average decrease in test costs.

Predictability is a big win for increasingly complex scheduling. “It ensures the efficiency of the engineering tasks with expert-level productivity without the expert,” Knowles said. “We’ve taken this concept and infrastructure and pushed it upstream into the DFT configuration. We’ve applied that AI engine by including a synthesis step, and that DFT configuration and initial results are showing very promising outcomes.”

Synopsys recently introduced its AI-driven workflow optimization and data analytics platform, which employs AI across the whole EDA stack into manufacturing, test, and out to in-field monitoring of chips. “This data continuum allows us to connect all these different phases, from design to ramp to production to in-field, leveraging all these data sources,” he said. “And once you have unified data, you can build semantic models that also can be leveraged across these different domains, and that’s when we get the true power of the AI.”

Fig. 3: Increasing the efficiency of ATPG by automatically minimizing the pattern count for the targeted test coverage within a scheduled period of time. Source: Synopsys

Fig. 4: Increasing the efficiency of ATPG by automatically minimizing the pattern count for the targeted test coverage within a scheduled period of time. Source: Synopsys

Basoco gave the example of using assistive intelligence for test co-generation. “Rather than building every test program from scratch, a more powerful method is to combine a test plan with a set of library codes,” she said. “This can be done using an AI program generator, but it still needs engineering insights.”

Adaptive test and real-time computations
Adaptive test targets test to where it’s needed most. In other words, each device receives the right test content to validate its performance, while using the minimum number of tests to get there. Adaptive test takes data generated by the tester, and relevant data from previous measurements, to predict the testing needs — either adding tests for risky parts to increase quality (reduce DPPM) or eliminating tests that capture no failures.

“In adaptive test, you removed or added from your test flow to Improve quality or Improve throughput,” said Roth. “The automotive space trying to move to one part-per-billion quality rate likely means more tests, because you’re trying to try to flush out any issues prior to going to market. But more tests adds cost, so that’s where we think an analytic model using AI or ML can provide an advantage.”

Adaptive test is all about testing smarter. It begins at wafer probe and ends at system-level test. As IC products become more complex, the analytics that govern adaptive test strategies move from being relatively simple to utilizing more complex statistical models and machine learning. Design and testing companies are building real-time data infrastructures to enable adaptive test among other capabilities.

For example, at wafer sort an engineer might examine a stacked wafer map of 25 wafers to identify clusters of failures in one zone on the map. An algorithm identifies failure severity among the cluster and surrounding chips. Additional tests are then applied at final test of the parts deemed risky while minimally impacting test time. [1]

In a second example, adaptive test permits the adjustment of test limits and outperforms DPAT methods. Sensors embedded in a chip can monitor operational metrics like power and performance. Here, a sensor-aware method identified a correlation between sensor data and the results of a specific VDD consumption test. The sensor-aware bivariate model enabled more accurate limits on speed/power consumption test, which resulted in improved quality through lower DPPM.

Data security
Data security is essential, and the industry is adopting zero-trust methodologies for handling data between testers, servers, etc. “A zero trust model of security and this provides some advantages and changes in the way you have to think about architecting and deploying your services and equipment. We’re trying to protect IP and we’re doing that by authenticating and encrypting every node connection along the way,” said Brian Buras, production analytics solution architect at Advantest America. “And you need to share data from one facility to another, and from one insertion to another insertion in the process. Some of our customers have spent a lot of resources and time developing complex analytics and they want to protect their IP, so they want to know when they deploy into our systems that it is secure.”

Conclusion
Tester companies are setting the stage for real-time data access, data analytics, and closed-loop feedback to testers, which enable better ATPG, DFT, and adaptive test. But the larger goal of securely managing data between tools and between equipment suppliers, customers, and third parties is still being ironed out.

The ties between design, test and manufacturing are becoming tighter out of necessity. “First silicon bring-up is a very, very busy time for companies in this space,” said Manion. “After first silicon comes back, their test solutions have to be ready to validate all of the original design requirements. And increasingly, our customers are spread out in sites around the world that are all trying to work in coordination. We’re helping those teams gather data in a similar way and with similar algorithms with similar measurement science, so that ultimately they can compare results across the various sites.”

Reference
G. Cortez and K. Butler, “Deploying Cutting-Edge Adaptive Test Analytics Apps Based on a Closed-Loop Real-Time Edge Analytics and Control Process Flow into the Test Cell,” IEEE International Test Conference, 2023, P5.

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New approaches, from AI to telemetry, extend well beyond yield.


Mon, 11 Dec 2023 10:00:00 -0600 en-US text/html https://semiengineering.com/testing-ics-faster-sooner-and-better/
Research Facilities

The Civil, Architectural, and Environmental Engineering Department laboratories provide students with fully equipped space for education and research opportunities. 

Structural and Geotechnical Research Laboratory Facilities and Equipment

Structures lab

The geotechnical and structural engineering research labs at Drexel University provide a forum to perform large-scale experimentation across a broad range of areas including infrastructure preservation and renewal, structural health monitoring, geosynthetics, nondestructive evaluation, earthquake engineering, and novel ground modification approaches among others.

The laboratory is equipped with different data acquisition systems (MTS, Campbell Scientific, and National Instruments) capable of recording strain, displacement, tilt, load and acceleration time histories.  An array of sensors including LVDTs, wire potentiometers, linear and rotational accelerometers, and load cells are also available.  Structural testing capabilities include two 220kips capacity loading frames (MTS 311 and Tinius Olsen), in addition to several medium capacity testing frames (Instron 1331 and 567 and MTS 370 testing frames), two 5-kips MTS actuators for dynamic testing and one degree of freedom 22kips ANCO shake table.  The laboratory also features a phenomenological physical model which resembles the dynamic features of common highway bridges and is used for field testing preparation and for testing different measurement devices.  

The Woodring Laboratory hosts a wide variety of geotechnical, geosynthetics, and materials engineering testing equipment.  The geotechnical engineering testing equipment includes Geotac unconfined compression and a triaxial compression testing device, ring shear apparatus, constant rate of strain consolidometer, an automated incremental consolidometer, an automated Geotac direct shear device and a large-scale consolidometer (12” by 12” trial size). Other equipment includes a Fisher pH and conductivity meter as well as a Brookfield rotating viscometer. Electronic and digital equipment include FLIR SC 325 infrared camera for thermal measurements, NI Function generators, acoustic emission sensors and ultrasonic transducers, signal conditioners, and impulse hammers for nondestructive testing.

The geosynthetics testing equipment in the Woodring lab includes pressure cells for incubation and a new differential scanning calorimetry device including the standard-OIT.  Materials testing equipment that is available through the materials and chemical engineering departments includes a scanning electron microscope, liquid chromatography, and Fourier transform infrared spectroscopy.

The Building Science and Engineering Group (BSEG) research space is also located in the Woodring Laboratory.  This is a collaborative research unit working at Drexel University with the objective of achieving more comprehensive and innovative approaches to sustainable building design and operation through the promotion of greater collaboration between diverse sets of research expertise.  Much of the BSEG work is simulation or model based.  Researchers in this lab also share some instrumentation with the DARRL lab (see below). 

CAEE Lab - Beakers

Environmental Engineering Laboratory Facilities and Equipment

The environmental engineering laboratories at Drexel University allow faculty and student researchers access to state-of-the-art equipment needed to execute a variety of experiments. These facilities are located in the Alumni Engineering Laboratory Building and includes approximately 2000 SF shared laboratory space, and a 400 SF clean room for cell culture and PCR.

The major equipment used in this laboratory space consists of: Roche Applied Science LightCyclerÔ 480 Real-time PCR System, Leica fluorescence microscope with phase contrast and video camera, Spectrophotometer, Zeiss stereo microscope with heavy duty boom stand, fluorescence capability, and a SPOT cooled color camera, BIORAD iCycler thermocycler for PCR, gel readers, transilluminator and electrophoresis setups, temperature controlled circulator with immersion stirrers suitable for inactivation studies at volumes up to 2 L per reactor, BSL level 2 fume hood, laminar hood, soil sampling equipment, Percival Scientific environmental chamber (model 1-35LLVL), custom-built rainfall simulator.

The Drexel Air Resources Research Laboratory (DARRL) is located in the Alumni Engineering Laboratory Building and contains state-of-the-art aerosol measurement instrumentation including a Soot Particle Aerosol Mass Spectrometer (Aerodyne Research Inc.), mini-Aerosol Mass Spectrometer, (Aerodyne Research Inc.), Scanning Electrical Mobility Sizer (Brechtel Manufacturing), Scanning Mobility Particle Sizer (TSI Inc.), Fast Mobility Particle Sizer (TSI Inc.), Centrifugal Particle Mass Analyzer (Cambustion Ltd.), GC-FID, ozone monitors, and other instrumentation.  These instruments are used for the detailed characterization of the properties of particles less than 1 micrometer in diameter including: chemical composition, size, density, and shape or morphology. 

In addition to the analytical instrumentation in DARRL, the laboratory houses several reaction chambers.  These chambers are used for controlled experiments meant to simulate chemical reactions that occur in the indoor and outdoor environments.  The reaction chambers vary in size from 15 L to 1 m3, and allow for a range of experimental conditions to be conducted in the laboratory.

Computer Equipment and Software

The Civil, Architectural, and Environmental Engineering Department at Drexel University has hardware and software capabilities for students to conduct research. The CAEE department operates a computer lab that is divided into two sections; one open access room, and a section dedicated to teaching. The current computer lab has 25 desktop computers that are recently updated to handle resource intensive GIS (Geographic Information Systems) and image processing software. There are a sufficient number of B&W and color laser printers that can be utilized for basic printing purposes.

Drexel University has site-licenses for a number of software, such as ESRITM ArcGIS 10, Visual Studio, SAP 2000, STAAD, Abaqus and MathworksTM Matlab. The Information Resources & Technology (IRT) department at Drexel University provides support (e.g., installation, maintenance and troubleshooting) to the abovementioned software.  It is currently supporting the lab by hosting a software image configuration that provides a series of commonly used software packages, such as MS Office and ADOBE Acrobat among others.  As a part of ESRI campus license (the primary maker of GIS applications, i.e. ArcGIS) the department has access to a suite of seated licenses for GIS software with necessary extensions (e.g., LIDAR Analyst) required for conducting research.  

Edmund D. Bossone Research Center

The Bossone Research Enterpirse Center includes 48 teaching laboratories, 37 lab support spaces, eight conference rooms, 77 offices and a 300-seat auditorium. The College of Engineering will occupy most of the building and will provide facilities for faculty and students from various departments in the University.  The Bossone Center is home to the Centralized Research Facilities (CRF), a collection of core facilities which contains resources for materials discovery and innovation, including structure, property characterization and device prototyping. Led by faculty and professional staff, the CRF serves a user base of more than 250 students, staff and faculty from across the University, and from its academic, national laboratory and industry partners in the Delaware Valley and beyond.

Machine Shop

Drexel University's College of Engineering offers a full-service fabrication Machine Shop on its University City campus. The facility has four full-time machinists with a combined industrial and academic experience of more than 100 years. The Shop is a multi-purpose machining facility capable of meeting all design needs. The Shop and its staff specialize in the research and academic environment, scientific instrumentation, biomedical devices, testing fixtures and fabrications of all sizes.

Mon, 01 Oct 2018 06:59:00 -0500 en text/html https://drexel.edu/engineering/academics/departments/civil-architectural-environmental-engineering/department-research/facilities/
Maximizing engineering resources with quality engineering

Modern software development can often feel like a Catch-22: to keep customers happy, companies must deliver new features faster. But deliver too fast without enough testing and bugs can slip into production, frustrating the customers who eagerly awaited the new feature in the first place. This paradigm often pits quality assurance against developers as they deliberate over the balance between speed and quality. 

Adding to the stressful mix is the pressure from business leaders to make engineering teams as lean and efficient as possible to navigate increasingly unpredictable market conditions and widespread supply chain disruptions. In the face of these demands, software teams need to rethink how they approach quality to maximize their output and minimize the risk of customer-facing defects. They need to adopt quality engineering principles, which aim to integrate testing throughout the software development life cycle in order to deliver a positive user experience. 

Testing Early and Often Minimizes Effort to Fix Bugs

When continuous testing as part of a quality engineering practice is an integral part of the entire development process, the overall risk of major defects being discovered at the last minute or in production is greatly reduced. Fully DevOps teams that have embraced continuous testing are almost three times more likely to identify defects early in development. This means that fully DevOps teams are much less likely to be frantically rewriting code days (or even hours) before a release date. 

When defects are discovered earlier in development, resolving them is a faster, simpler process:

Most DevOps teams that test early and often can fix bugs within a single business day, and roughly a quarter can find solutions in minutes. In contrast, the bulk of aspiring DevOps organizations are spending up to a full work week resolving bugs. Discovering defects earlier in development reduces the time and effort needed to resolve issues, making software development teams more efficient and more focused on customer retention.

Harnessing AI and Machine Learning for Efficient Development

Though many organizations are struggling to successfully implement AI – an estimated 85% of AI projects fail to deliver on their goals – testing is a prime opportunity to showcase the value of AI tools. According to Gartner’s Market Guide for AI-Augmented Software Testing Tools: “By 2025, 70% of enterprises will have implemented an active use of AI-augmented testing, up from 5% in 2021.” Development teams looking to unlock faster development with AI would be smart  to consider starting AI adoption with high-impact areas like software testing. 

AI accelerates software testing by reducing the amount of rote work of test maintenance through autohealing — a capability that enables tests to evolve with the product without requiring hours of quality engineering effort. When there’s less time needed for test maintenance, quality engineers can spend more time performing exploratory testing, collaborating with developers, or improving test coverage. The result: faster delivery cycles that don’t sacrifice the user experience. Gartner predicts that: “By 2025, organizations that ignore the opportunity to utilize AI-augmented testing will spend twice as much effort on testing and defect remediation compared with their competitors that take advantage of AI.”

In other words, investing in AI-backed testing tools that enable software teams to deliver quality products more efficiently is investing in a competitive advantage. 

Clear Communication Minimizes Wasted Engineering Hours

When it comes to rectifying high priority bugs, speed and clear communication are critical to maximize engineering effort. The longer a development team spends trying to figure out what tests failed and why they failed, the more hours are spent chasing information. 

Leaning into tools that make sharing information between quality engineers and developers significantly reduces the effort needed to resolve bugs. Considering that 26% of knowledge workers say that app overload slows them down at work, this single step can dramatically Improve how engineering organizations collaborate on quality. Even better, simply standardizing quality workflows, communication, and tools is a low-cost way to make software development teams more efficient. 

Quality engineering is one of the few common threads throughout the SDLC, functioning as a common thread between code and the customer. As more engineering organizations look to streamline how quickly they build new features – without alienating customers through poor user experiences — investing in software testing is a high impact opportunity that makes everyone’s lives easier. 

To read the full Gartner report, download it here.

Tue, 05 Jul 2022 12:00:00 -0500 en-US text/html https://sdtimes.com/test/maximizing-engineering-resources-with-quality-engineering/
OnDemand | Social Engineering, Phishing & Pen Testing: Hardening Your Soft Spots

Brian Reed

Chief Mobility Officer, NowSecure

Brian Reed brings decades of experience in mobile, apps, security, dev and operations helping Fortune 2000 global customers and mobile DevSecOps trailblazers while growing NowSecure, Good Technology, BlackBerry, ZeroFOX, BoxTone, MicroFocus and INTERSOLV. With more than 25 years building innovative products and transforming business processes, Brian is a dynamic speaker and compelling storyteller who brings unique insights and global experience. Brian is a graduate of Duke University.

Sat, 01 Oct 2022 11:13:00 -0500 en text/html https://www.govinfosecurity.com/webinars/live-webinar-social-engineering-phishing-pen-testing-hardening-your-w-4357




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