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Test Number : HCE-5420
Test Name : Hitachi Data Systems Certified Specialist - Content Platform
Vendor Name : Hitachi
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HCE-5420 test Format | HCE-5420 Course Contents | HCE-5420 Course Outline | HCE-5420 test Syllabus | HCE-5420 test Objectives


Exam Name : Content Platform implementation Specialist
Exam Number : HCE-5420 Content Platform implementation
Exam Duration : 90 minutes
Questions in test : 60
Passing Score : 66
Exam Registration : PEARSON VUE
Real Questions : Hitachi Vantara HCE-5420 Real Questions
VCE VCE exam : Hitachi Vantara Certified Specialist - Hitachi Content Platform implementation Practice Test

Section Objectives Hitachi Content Platform Solution Architecture - Identify HCP solution components and describe their function.
- Describe the Hitachi Content Platform tiering solutions components.
- Identify the connectivity features of HCP.
- Identify the capabilities of HCP as they relate to the customer environment.
- Describe the solution architecture of HCP.
- Describe HCP solutions involving compliance mode or retention.
- Identify replication concepts and topologies between Hitachi Content Platform systems. Hitachi Content Platform Pre-Installation - Identify customer network requirements prior to a Hitachi Content Platform solution implementation.
- Identify the access protocols supported in HCP solutions and describe their influence on the features used on the namespaces.
- Describe the customer requirements for integrating an HCP solution with authentication services.
- Demonstrate how to provision HCP storage according to Hitachi Vantara best practices. Hitachi Content Platform Deployment - Describe the deployment of HCP solutions in VMware environments.
- Demonstrate understanding of the parameters used during the initial configuration of a HCP solution.
- Demonstrate how to configure network management on HCP solutions.
- Demonstrate how to configure HCP software for customer use.
- Demonstrate understanding of HCP replication operations.
- Demonstrate knowledge of how to implement an HCP service plan. Hitachi Content Platform Solution Integration - Demonstrate how to configure HCP for integration with other Hitachi file-and-content solutions such as Hitachi Data Ingestor and HCP Anywhere.
- Describe how to integrate HCP into a Microsoft Active Directory environment.
- Describe the data-access privileges of HCP tenants and namespaces.
- Describe how to add storage to existing HCP nodes.
- Describe how to add nodes to an existing HCP solution.
- Demonstrate understanding of HCP storage migration. Hitachi Content Platform Management - Identify documentation that the customer can use for managing HCP.
- Describe system-level administrative roles and operations in HCP.
- Describe tenant-level administrative roles and operations in HCP.
- Describe the features that allow monitoring of HCP.
- Demonstrate how services and related policies operate on HCP.
- Describe how to use the metadata query engine in HCP. Hitachi Content Platform Solution Support - Describe HCP software-update procedures.
- Describe how to collect data from HCP for troubleshooting and problem-escalation purposes.



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Hitachi Systems Free test PDF

AI In Inspection, Metrology, And examine | HCE-5420 VCE exam and real questions

AI/ML is creeping into assorted methods in the fab and packaging residences, however not necessarily for the aim it was at first meant. The chip trade is barely starting to be taught the place AI makes sense and where it doesn’t.

In regular, AI works ultimate as a tool within the fingers of someone with deep area abilities. AI can do certain issues neatly, primarily when it comes to pattern matching across vast information units. In areas akin to metrology, verify and inspection, it’s no longer as valuable as engineers with years of journey. but collectively, AI plus experienced people often produce more suitable consequences than each and every is able to achieving in my opinion.

“The human eye can see issues that no volume of computing device studying can,” talked about Subodh Kulkarni, CEO of CyberOptics. “That’s where one of the sophistication is starting to ensue now. Their current systems use a primitive sort of AI technology. when you appear at the picture, that you can see a problem. And their AI laptop doesn’t see that. however then you go to the deep discovering form of algorithms, the place you have got very severe Ph.D.-stage americans programming one algorithm for per week, and they can become aware of all those issues. but it takes them per week to program those things, which nowadays isn't purposeful.”

That’s beginning to change. “We’re seeing sooner deep-learning algorithms that will also be more conveniently programmed,” Kulkarni pointed out. “however the defects also are getting harder to catch through a computer, so there remains a niche. The biggest bang for the buck is not going to come from enhancing cameras or projectors or any of the device that they use to generate optical photos. It’s going to be decoding optical images.”

That equal type of considering is becoming utilized throughout other ordinary manufacturing techniques. however for metrology, this nonetheless requires satisfactory tuning for when and how to make use of AI/ML.

“You want to be in a position to manage the technique because it happens,” referred to David Fried, vice chairman of computational products at Lam analysis. “You want actual-time energetic handle of the process to get the right consequences. The best facts that you just see in real-time is the records that the device is producing. if in case you have the appropriate sensors to do the appropriate handle, that’s incredible. but when you don’t, all you have got is ex-situ metrology. You’re measuring the wafer after it processes. That’s terrific guidance, but unfortunately the system is accomplished at that aspect. you can’t go again and handle it.”

On the examine side, AI/ML is viewed as a possible approach to enhance coverage and Strengthen efficiency. for example, FormFactor is using desktop researching. to healthy designs that are equivalent for trying out. “machine getting to know has entered into their MEMS technique as well as their design process,” pointed out Alan Liao, director of product advertising at FormFactor. “So each and every of these chips, and each of those consumer-ordered probe playing cards, are custom made for their particular application or chip. Being in a position to optimize the probe card to fulfill their needs is the key to getting high yield on the wafer. They apply a huge client database for that classification of software or design, after which they could faucet into their library, which is designed to run a desktop researching algorithm to discover, for instance, 10 designs that matched this one. That’s now instantly executed behind the scenes with their design device.”

where laptop learning works, and where it doesn’tStill, the merits of using AI/ML everywhere don't seem to be at all times evident. some of the new chips and techniques being confirmed have a part of AI/ML of their logic, which makes it greater challenging to determine consistent patterns.

“if you consider about system-degree trying out, there’s a customized interface you need to Strengthen to perform device-level checks, but there’s now not a lot of algorithmic smarts behind that,” said Keith Schaub, vice chairman of expertise and method at Advantest the us. “It booted, it ran a bunch of exams on its own, after which it told us, ‘It’s first rate,’ or ‘It’s not decent.’ If now it’s embedded with some kind of AI mannequin, and that AI model adjustments over time, how do you examine for that? So one of the most issues they need to test for is whether the entire excessive-pace buses are working. They deserve to make certain the reminiscence is working. They deserve to be sure the CPU is speaking to the memory, and it can transmit video, and save it adequately, and that all of this works whereas a B and C are occurring. and they'll have pre-loaded on there some specific expectations in line with definite percentage accuracies that they expect. And if it passes, that AI is good. If it doesn’t, they are going to have full traceability via their through their AI vectors.”

precisely the place AI/ML matches into all of these procedures, and how to greatest utilize it, are works in development.

Some basic uses for computing device learning now are:

  • Wafer inspection
  • Defect detection, classification, and prediction
  • Scanning electron microscope (SEM) photo de-noising
  • there are lots of extra in R&D, however how constructive they're going to for the chip business be continues to be to be confirmed over time.

    “We’re attempting to remember superior correlations between one of the crucial ex-situ metrology facts generated after the technique has completed, and consequences got from laptop getting to know and AI algorithms that use statistics from the sensors and in-process alerts,” spoke of Lam’s Fried. “maybe there’s no intent that the sensor records would correlate or be an excellent surrogate for the ex-situ metrology information. but with computer learning and AI, they are able to locate hidden indicators. They might check that some sensor in a given chamber, which definitely shouldn’t have any referring to the process results, definitely is measuring the ultimate effects. We’re learning the way to interpret the complicated indicators coming from distinct sensors so that they can function real-time in-situ technique control, even if on paper they don’t have a closed-form expression explaining why we’d achieve this.”

    Defining the problemAI and desktop discovering are sometimes-misused terms, which adds yet another level of bewilderment to their adoption. there's a major amount of hype surrounding these labels, commonly pushed by means of marketing departments that exaggerate exactly what they’re including in their products.

    “everybody wants to make use of the phrase desktop learning nowadays,” mentioned Dennis Ciplickas, vp of superior solutions at PDF options. “Some people will say computer gaining knowledge of, and all they’re in fact doing is ax = b beneath the hood. There’s nothing involving neural networks or AI algorithms, however they’re advertising it as laptop studying.”

    AI is an umbrella term that contains the entire ingredients that make a system mimic some variety of human intelligence. desktop getting to know and its subsets — neural networks, deep studying neural networks — are part of the AI system. because the mind within the gadget, the laptop gaining knowledge of must be proficient to seem to be at the statistics and make a classification, decision, advice — an inference. desktop researching is dynamic. It also has to be proficient to constantly make changes to its inferencing on its own. The desktop can be taught without being explicitly programmed.

    “in case you suppose in regards to the definition of laptop discovering, it encompasses lots of things that people wouldn’t necessarily name laptop researching appropriate off correct of their head,” talked about Jeff David, vp of AI solutions at PDF solutions. “as an instance, linear regression. That’s laptop studying. i was doing linear regression twenty years ago. I wasn’t calling it machine getting to know then. That’s an easy example, of course. computer gaining knowledge of gets plenty extra complex.”

    It also crosses varied departments. Linear regression is time-honored in laptop researching. It additionally belongs to facts.

    lots of the techniques proven on the eBeam Initiative’s these days posted record of deep discovering and desktop studying initiatives in semiconductor manufacturing are using deep convolutional neural networks (DCNNs). ‘Deep’ implies varied layers. CNNs (convolutional neural networks), RNNs (recurrent neural networks), and generative adversarial neural networks (GANs) additionally exhibit up on the list.

    for example, Hitachi high-Tech Corp. is the use of DCNNs to increase photo first-class to notice defects with high sensitivity in SEM studies. NuFlare know-how, Inc has a SEM defect classifier that makes use of a DCNN. Imec more desirable classification for OPC metrology the use of an Autoencoder Neural community deep studying. TASMIT makes use of an RNN and GANs for semiconductor wafer metrology and inspection equipment. Siemens EDA makes use of vector pushed neural networks for design evaluation and hotspot detection in its Calibre Wafer Defect Engineering with Deep learning that accelerates check chip development and improves yield and reliability in the fab through detecting yield limiters.

    KLA’s first procedure manage device with AI is the eSL10 e-beam patterned wafer defect inspector. The eSL10 makes use of deep getting to know algorithms to discover subtle defect alerts and demo and manner noise, based on the company.

    Fig. 1: Optical crucial dimension (OCD) combined with AI physics-based mostly modeling to increase metrology efficiency. source: Onto Innovation

    Likewise, CyberOptics has been the use of machine learning for defect detection for wafer-stage and advanced packaging inspection and metrology. “no longer all computer learning programs are equal,” spoke of Tim Skunes, vice chairman of R&D at CyberOptics. “You want your desktop researching algorithm to be constructive. You need to get good efficiency in reality straight away. as an instance, computing device learning algorithms reminiscent of AI2, the place you teach with the aid of showing decent/defect-free images or pictures of defects, can increase tactics and yields. The operator can instantly teach then video display, study from the consequences and Strengthen and adapt with the aid of updating the training units if required. They design their desktop gaining knowledge of algorithms to be biased with the goal of no escapes so no dangerous product leaves the manufacturing facility.”

    gradual going to get data and inferencing rightTo keep away from expensive blunders, the semiconductor machine business and its purchasers are relocating cautiously. building working towards units and getting clear statistics off lots of gadget is probably the most critical first step to get correct earlier than the usage of computing device getting to know on that facts.

    “Automation can be labor intensive. nowadays, laptop learning algorithms nonetheless need to be proficient with labeled records,” spoke of Mark Shirey, vice chairman of marketing and functions at KLA. “With admire to inspection, originally, it takes an funding of time to build up classified defect libraries. besides the fact that children, once here is done the algorithms work neatly when it comes to accuracy and purity. And in the end, by producing superior excellent facts, they in the reduction of the time vital to identify defect sources and take corrective action. it is exciting to peer today’s machine getting to know algorithms assisting to construct tomorrow’s AI chips, and they are optimistic that unsupervised laptop discovering purposes will continue to develop right through the semiconductor ecosystem.”

    That requires an understanding of no matter if the desktop discovering is working effectively, too.

    “The industry uses practising information and verification facts sets,” spoke of Advantest’s Schaub. “The verification information sets are used to sanity check that the ML is working thoroughly. they are able to seemingly need to set up a continuous training and monitoring procedure. strategies float, which ability the records drifts, which potential you deserve to continuously computer screen your records and trigger retraining because the manner drifts. They be aware of the way to try this. The challenge is to know the way an awful lot to educate and the way frequently to re-instruct. How plenty go with the flow earlier than I set off retraining?”

    In some situations, building practising records and getting clean information ought to ensue concurrently in order to make progress. In impact, you need to have faith the data being generated.

    “The person should still all the time be validating and monitoring what it's that’s going into making those predictions. This leads into the explainability of the model, and why that’s important,” observed PDF’s David. “once you instruct a mannequin, many times — almost always — you need to be mindful what it's that mannequin is using to make its choices. After you teach it, definite sorts of fashions will tell you certain sorts of tips about what it’s the usage of when it gets predictions. So the person has to examine that and say, ‘Oh, this makes feel.’ There’s got to be some person instinct there. That’s vitally vital in lots of stuff that they do. They in reality spend loads of time making sure that they are able to clarify their predictions, and additionally heaps and tons of drill downs underneath that.”

    When it comes down to the usage of models to foretell where a failure could be with the intention to prevent further high priced, pointless checking out, “it’s so important for their consumers to trust that mannequin,” spoke of David. “if they don’t agree with it, they’re now not going to set up it and have confidence it. And in case you’re in a circumstance the place you’re continually retraining your mannequin, it’s going to get a hold of new inputs that go into that mannequin. The consumer is going to study that and make sure they perpetually agree that that makes feel.”

    as a result of AI-certain chips could be powering the system, constantly gathering ground truth is crucial. “Our deep statistics approach is to invariably computer screen the efficiency through monitoring the timing margins on huge number of paths and alert after they turn into smaller than a pre-described threshold,” noted Nir Sever, Sr. Director of Product at proteanTecs. “additionally, they to at all times display screen the atmosphere of the chip such, including results of voltage, temperature, vigour, and clock network integrity, and of course stress.” “

    Making bound the AI chip that runs the inferencing works correcting is not trivial. “certainly, here is a real challenge and i believe the industry is still struggling to locate decent check methodologies for these gadgets,”  observed Sever. “lots of it is done the usage of simulations and the idea that in case your equipment was manufactured appropriately, it'll nonetheless function as it should be over time. growing old can battle with this assumption, so that you need to find easy methods to display screen timing degradation one by one from practical validation.”

    concentrated on AI/MLStill, for certain techniques in the fab and assembly houses, this technology is proving to be constructive.

    “AI learns some issues really neatly,” stated Mike Kelly, vice president of advanced packaging development and integration at Amkor expertise. “At a very primary degree, it’s like least-squares optimization. but it’s statistical. It’s only nearly as good as the information and the information set used to pressure it. It still looks like it can be a very fantastic optimization device for managing issues like hotspots or section shift within the clock — things that can be optimized on the fly.”

    What isn’t clear yet is the place to attract the boundaries.

    “equipment-degree design is all about figuring out the efficiency of the individual pieces, understanding the range of that, what envelope are they in, and making bound all of them hook together in a method that’s compatible with the last goal,” PDF’s Ciplickas talked about. “This determines what you set in the chip, the way you measure that, what’s the capacity of your size tool, and what class of records is produced. And what’s the skill of your analytic gadget to process all of that to get to the ultimate characterization outcomes? You need to examine every particular person element. What does it do? What’s its spec and its variation? after which, do all of them healthy collectively to construct your huge photo?”

    And, of course, the greater decent data, the more desirable the outcomes.

    “often in larger agencies you've got loads of statistics with failures. You additionally see disasters that ensue only one or two instances,” talked about Andy Heinig, head of branch effective Electronics at Fraunhofer IIS’ Engineering of Adaptive techniques Division. “To find correlations at this factor, you in fact could use AI. but the difficulty is you may also locate very good correlations, however commonly you don’t locate the root cause, because AI doesn’t aid you to locate the root cause. You handiest have the correlation. this is valuable, however you need an important volume of statistics. the place you have got screw ups, and you've got lots of pictures from the identical failure or from the same device, then you definitely see some correlations.”

    assist from yield management records dashboardsIn the end, it all comes down to clean facts. And the greater clear the statistics, the more advantageous

    “Realistically speaking this is probably the most main factors that things can go incorrect for your entire AI equipment,” observed David. “It’s now not just the computer gaining knowledge of element. you've got obtained to make sure your facts is basically there.”

    Dashboards for test data promise to make life easier for yield engineers and mission managers if the facts is clean adequate. These dashboards also may deliver a compelled clean-information element as they ingest information from all aspects in manufacturing and test, making actionable machine researching feasible.

    Some of these involve yield and lifecycle administration, which are anticipated to have expanding amounts of computer discovering know-how. Synopsys has a flexible analytics dashboard equipment that takes all of the statistics from equipment involved in manufacturing and check and presents it visually. This helps since the human brain can spot patterns quickly in visible facts.

    using AI is spreading to different areas, as smartly. Lavorro Inc. is engaged on an AI/ML-pushed wise-Bot for semiconductor manufacturing, which makes use of PDF options’ platform to fill its dashboard with records. Cimetrix Sapience is the general data collection interface and information distribution community that helps the mixture of device on a manufacturing facility floor.

    Making experience of that facts remains challenging, though. Some query even if facts coming off semiconductor manufacturing techniques will need to be condensed into fashions.

    “building of the types of models described requires four issues — the capability to handle high-density information, equipment expertise, facts science abilities to in the reduction of the large quantities of facts to smaller sets, and software capacity to assemble fashions which can be correlated to end of line metrics,” observed Jason Shields, vice chairman of equipment intelligence at Lam research. “The chance of large facts and computer researching requires close collaboration between manner gadget suppliers and equipment manufacturers to enforce options to bring these effects. method device suppliers supply big records management from their device, and the recognize-how to circumstance data and obtain information reduction. gadget producers supply end of line results. The utility to implement the correlation is always provided by means of the device manufacturers, as they are going to optimize the efficiency of their solution for their device. so far, third-birthday celebration or customer-driven options haven't been in a position to achieve identical degrees of efficiency as machine company fashions, as they lack sufficiently enormous equipment facts sets or the be aware of-how to without difficulty in the reduction of the facts.”

    ConclusionAI systems the usage of computer gaining knowledge of recommendations are getting used now in inspection, metrology, and test of semiconductors. in the end, because the chip business becomes extra prevalent with how to apply, regulate and keep computer studying, these clever techniques doubtless will assist to velocity up the inspection, metrology, and verify workloads.

    “equipment manufacturers are increasing their funding in big statistics management and records science teams to strengthen in-line models, which investigate satisfactory for every wafer,” pointed out Shields. “manner gadget suppliers are participating with equipment manufacturers to permit predictive equipment models that can deliver the necessary wafer first-rate at the identical or lessen chance and price than natural metrology strategies.”

    Alongside of that, engineers will should learn how most excellent to utilize and follow this expertise. That may additionally prove to be the larger problem.

    — Anne Meixner contributed to this file.


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