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500-205 SP Optical Technology Systems Engineer Representative

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500-205 exam Dumps Source : SP Optical Technology Systems Engineer Representative

Test Code : 500-205
Test designation : SP Optical Technology Systems Engineer Representative
Vendor designation : Cisco
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Cisco SP Optical Technology Systems

Cisco Named Optical community Market leader by means of IHS | killexams.com true Questions and Pass4sure dumps

Cisco is noiseless on the leading edge, using optical trade innovation for their valued clientele. today, I’m completely pleased to announce that they hold achieved the rating of Optical community Market chief in the 2018 IHS Markit technology seller Scorecard.

principally, the record states that Cisco leads the province this 12 months in market participate momentum with NCS one thousand income to the web-scale neighborhood, combined with the birth of the Verizon 100G metro-packet optical assignment.

The only record of its form, this Scorecard evaluates the right optical network machine providers on standards the utilize of concrete facts and metrics, including direct comments from consumers, vendor market share, market participate momentum, financials, company attention, recognition for innovation, and different benchmarks. This approach eliminates subjectivity and ensures companies are assessed accurately and pretty.

day to day they set out to resolve their purchasers’ proper challenges. i want to thank their valued clientele and companions to your endured partnership – you abet gasoline their coerce to continue trade disruption and innovation. I likewise extend congratulations to the Cisco Optical techniques team and recognize their difficult labor in earning this stout difference.

be trained more about Cisco’s latest improvements on their Optical Networking site.

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Cisco systems In Talks To purchase Optical Chip Pioneer Luxtera: report | killexams.com true Questions and Pass4sure dumps

Cisco programs is in talks to purchase optical chip maker Luxtera, beating Intel Corp. and Broadcom Inc. to the punch for a deal that might achieve into the a entire bunch of hundreds of thousands of bucks, in response to a Bloomberg document.

Negotiations between networking behemoth Cisco and Carlsbad, Calif.-primarily based Luxtera are ongoing and personal, and there's nonetheless a chance the deal may additionally not amble through, Bloomberg pronounced, citing americans conventional with the plans.

Luxtera develops silicon photonics, which integrates fiber optics with silicon electronics. The "fiber to the chip" technology is said to vastly precipitate information connectivity. The company turned into based in 2001. Its President and CEO Greg adolescent labored for both Broadcom and Intel before joining Luxtera in 2007.

Luxtera, Broadcom, Intel and Cisco did not instantly reply to requests for comment Tuesday.

Kent MacDonald, senior vice chairman of strategic alliances at long View techniques, a Canada-primarily based retort issuer that works with Cisco, mentioned the acquisition can subsist a smartly-timed and "intelligent" amble through Cisco as the market continues to cope with common factor shortages.

"I descry this as a judicious and timely funding by means of Cisco to precipitate up innovation and create a competitive capabilities available in the market, peculiarly with component supply an ongoing problem for the industry," MacDonald noted.

Cisco below Chairman and CEO Chuck Robbins has made huge efforts to transition to a enterprise mannequin that emphasizes software and subscription-based revenue. The acquisition of Luxtera would probably supply the company a hardware efficiency expertise, however, as purchasers movement towards not pricey apparatus and far from expensive, proprietary device.

"Cisco's bread and butter continues to subsist in the highest-conclusion styles of connectivity, and with 5G on the horizon and Wi-Fi speeds now multi-Gig, speeds will proceed to extend exponentially," said Ben Johnson, CEO of Liberty expertise, a Griffin, Ga., solution issuer that works with Cisco.

"With the explosion of IoT, I feel or not it's absolutely vital for Cisco to hold their position in the core switching market and to abruptly enhance switching speeds," Johnson referred to. "we've got considered the liberate of 10G, 40G and even 400G switches, however even these speeds don't appear to subsist adequate to handle the bandwidth tsunami on the immediate horizon. i am excited to peer extra investment in actual, silicon-primarily based technology."


$40B+ World Optical Networking & conversation Market 2014-2024 - Segmented by pass of element, network Connectivity, conclusion-user and Geography | killexams.com true Questions and Pass4sure dumps

March 26, 2019 10:31 ET | supply: analysis and Markets

Dublin, March 26, 2019 (GLOBE NEWSWIRE) -- The "Optical Networking and conversation Market (2014-2024)" document has been brought to ResearchAndMarkets.com's offering.

This anatomize predicts that the optical networking and communication market will grow with a CAGR of 7.6% right through the forecast period. it is likewise being anticipated that by using 2024, the market will generate a earnings of $forty.3 billion. The turning out to subsist demand for networks with extravagant bandwidth and the appearance of machine-to-machine (M2M) and information superhighway of issues (IoT) applied sciences are the main increase-driving elements.

When segmented through component, the domain will likewise subsist categorized into features, utility, and hardware. among these, the hardware category is witnessing the optimum demand, as advanced hardware is capable of transmitting facts at extravagant speeds, which is needed to preserve 5G and the expanding facts traffic. other factors using the hardware class of the optical networking and communication market are yardstick upkeep and updates within the box. Technologically superior hardware fulfils utter the demands of stronger networking infrastructure, including extravagant information transfer rate, but low vim consumption, latency, and cost.

further, optical transceivers, optical fibers, optical amplifiers, optical splitters, optical circulators, optical switches, and others are the a considerable number of subdivisions inside the hardware category. among these, optical fibers are estimted to generate the maximum revenue in 2018 and likewise grow the fastest sooner or later. The expanding utilize of optical fibers in computer trying out, drugs, research, conversation, computer networking, and other sectors is anticipated to gas its growth in the optical networking and communique market during the forecast period.

despite the fact, a key style throughout the globe is the implementation of optical networks bereft of optical fibers! using the open-air optical technology, such networks carry out what is known as instant optical transmission. in this, the information and voice, in the character of optical alerts, are converted from and to instant radio frequency indicators by using diverse hubs. This technology helps transmit statistics at extravagant speeds devoid of costing too much and likewise makes the addition of recent subscribers workable by pass of multiplexing. at the moment, instant optical transmission is commonly used by using large, based businesses, but in the future, it's expected to subsist adopted by pass of smaller groups as smartly.

presently, the competitors in the optical networking and communication market is by a long shot excessive. Key players, reminiscent of Infinera corporation, Fujitsu limited, ZTE agency, Corning integrated, Ciena company, and ADVA Optical Networking SE, are leaving no stone unturned in coming up with inventive items. as an instance, the basic focus of ZTE supplier is strengthening its market position by means of investing closely in network virtualization and 5G products. in a similar fashion, Corning included has its optical communications segment as its main heart of attention enviornment.

issues coated

Chapter 1. research Background1.1 research Objectives1.2 Market Definition1.three analysis Scope1.3.1 Market Segmentation by means of Component1.three.2 Market Segmentation through community Connectivity1.3.three Market Segmentation via end-User1.three.four Market Segmentation by Geography1.3.5 evaluation Period1.three.6 Market information Reporting Unit1.four Key Stakeholders

Chapter 2. research Methodology2.1 Secondary Research2.2 fundamental Research2.2.1 Breakdown of fundamental research Respondents2.2.1.1 by means of region2.2.1.2 by means of industry participant2.2.1.3 by means of traffic type2.three Market dimension Estimation2.four data Triangulation2.5 Assumptions for the study

Chapter three. govt abstract

Chapter 4. Introduction4.1 Definition of Market Segments4.1.1 through Component4.1.1.1 Hardware4.1.1.1.1 Optical Fibers4.1.1.1.1.1 Single-Mode Fiber4.1.1.1.1.2 Multimode Fiber4.1.1.1.2 Optical Transceivers4.1.1.1.3 Optical Amplifiers4.1.1.1.4 Optical Switches4.1.1.1.5 Optical Splitters4.1.1.1.6 Optical Circulators4.1.1.1.7 Others4.1.1.2 Software4.1.1.three Services4.1.2 via community Connectivity4.1.2.1 Access4.1.2.2 Metro4.1.2.three long-haul4.1.3 by using conclusion-User4.1.three.1 Telecommunication4.1.three.2 Government4.1.3.three Commercial4.1.three.4 Industrial4.1.three.5 Broadcasting4.1.3.6 facts Centers4.2 price Chain Analysis4.3 Market Dynamics4.three.1 Trends4.three.1.1 increasing inclination towards instant optical networks4.three.2 Drivers4.three.2.1 starting to subsist demand for tall bandwidth4.3.2.2 Emergence of IoT and M2M4.three.3 Restraints4.three.three.1 want of skilled professionals4.3.4 Opportunities4.three.four.1 Leveraging 5G networks to enhance networking4.three.4.2 starting to subsist cellular records traffic4.four Porter's five Forces analysis

Chapter 5. world Market size and Forecast5.1 with the aid of Component5.1.1 by using Hardware Type5.1.1.1 through Optical Fiber Type5.2 by using community Connectivity5.3 by means of end-User5.four by pass of vicinity

Chapter 6. North the usa Market dimension and Forecast

Chapter 7. Europe Market size and Forecast

Chapter 8. APAC Market dimension and Forecast

Chapter 9. LATAM Market size and Forecast

Chapter 10. MEA Market dimension and Forecast

Chapter 11. competitive Landscape11.1 Market participate analysis of Key Players11.2 list of Key Players11.3 aggressive Benchmarking of Key Players11.four fresh actions of indispensable Players11.5 global Strategic traits of Key Players11.5.1 Mergers and Acquisitions11.5.2 Partnerships11.5.3 Product Launches11.5.four Facility Expansions11.5.5 customer Wins11.5.6 different developments

Chapter 12. enterprise Profiles12.1 ADVA Optical Networking SE12.1.1 company Overview12.1.2 Product and repair Offerings12.1.three Key monetary Summary12.2 Ciena Corporation12.three Cisco programs Inc.12.4 Corning Incorporated12.5 FiberHome Telecommunication applied sciences Co. Ltd.12.6 Fujitsu Limited12.7 Huawei know-how Co. Ltd.12.8 Infinera Corporation12.9 Viavi options Inc.12.10 Nokia Corporation12.11 ZTE supplier

For more information about this record visit https://www.researchandmarkets.com/analysis/jkh3x8/40b_world?w=12

research and Markets additionally presents custom research functions proposing concentrated, finished and tailor-made analysis.

CONTACT: ResearchAndMarkets.com Laura wood, Senior Press supervisor press@researchandmarkets.com For E.S.T workplace Hours designation 1-917-300-0470 For U.S./CAN Toll Free designation 1-800-526-8630 For GMT office Hours designation +353-1-416-8900 linked topic matters: Networks

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SP Optical Technology Systems Engineer Representative

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CCDC’S Road Map to Modernizing the Army: Future perpendicular raise | killexams.com true questions and Pass4sure dumps

Third in a succession of articles on how the U.S. Army Combat Capabilities progress Command, formerly the Research, progress and Engineering Command and now piece of Army Futures Command, is supporting the Army’s six modernization priorities.

by Maj. Gen. Cedric T. Wins

When Soldiers from the 2nd Battalion, 7th Cavalry dropped from UH-1 Huey helicopters into a minute clearing in the Ia Drang Valley in November 1965, they became piece of what is considered the first large-scale helicopter assault and the first large-unit date of the Vietnam War. Though immediately surrounded by thousands of North Vietnamese soldiers, American forces were able to combine air assault with the more traditional hefty artillery champion to withhold from being overrun. Hundreds of American Soldiers died during the battle that followed, but the air champion was one key understanding they were able to inflict a much heavier toll on the North Vietnamese army.

Helicopters were indispensable during the Vietnam War because of their aptitude to consume off and land vertically and to hover in a country covered in dense jungle. They were used to transport Soldiers and supplies to the war zone, conduct reconnaissance missions, strike targets and evacuate injured Soldiers for treatment. Many years later, Army aviation continued to play an indispensable role in Iraq and Afghanistan, where the varied desert and mountainous terrain presented problems including limited maneuverability and brownout conditions.

Helicopters remain captious to Army operations, but some of the vertical-lift platforms in current utilize are more than 50 years old. To achieve the performance that next-generation aircraft will require, they are working on many captious areas, including lethality, survivability, lighter and stronger airframes and rotors, and advanced manned and unmanned teaming.

ONE TEAM, ONE PLAN

Before affecting into the U.S. Army Futures Command and becoming Combat Capabilities progress Command (CCDC), the U.S. Army Research, progress and Engineering Command (RDECOM) launched an across-the-board propel plot to gain greater visibility of their operations and become more efficient and efficient. This propel continues to scrutinize their integrated technology development, how they manage their talent and other resources and how they communicate this to their partners and the American public.

As piece of that campaign, they reorganized their portfolio and management structures to mirror the Army’s modernization priorities, naming a lead heart for each modernization priority. While the CCDC Aviation and Missile heart leads science and technology (S&T) for Future perpendicular Lift—the Army’s third modernization priority—our eight major and three international centers and laboratories labor together to interface with both the requirements community and the cross-functional teams developed as piece of the Futures Command. This synergy enables their labs to bear a unified position and focus on the most captious technologies required for future perpendicular lift.

CCDC supports the Future perpendicular raise team at multiple levels, such as with a dedicated S&T representative who provides aviation expertise, access to their labs to exchange technology, war-gaming exercises for collaboration and problem solving, topic matter experts and program progress support.

The command likewise brings the expertise of and relationships with its extensive network of domestic and international academic and industry partners, the U.S. Army Aviation heart of Excellence and the Program Executive Office (PEO) for Aviation to develop and demonstrate new technologies for future perpendicular raise that will provide increased range, protection, lethality, agility and mission flexibility.

TECHNOLOGY FOR THE FUTURE FIGHT

We are developing and demonstrating several technologies for future perpendicular raise to inform concepts of operation and retain air power in multidomain operations, which will require commanders to fight with joint forces across multiple spheres to overcome adversaries.

The Integrated Mission outfit for perpendicular raise Systems is a digital backbone of open architectures that will enable the Army to update and modernize outfit much faster and more effectively than currently fielded systems. This technology will not only meet evolving vertical-lift requirements, but may subsist used on other platforms in the future, including combat vehicles. The elastic backbone will enable a plug-and-play capability, which will allow the Army to update systems easily with new technology. An added profit will subsist the aptitude to extend readiness by programming the aircraft with the right capabilities for a mission before the aircraft departs.

In March 2018, the Aviation & Missile Center—formerly known as the Aviation and Missile Research, progress and Engineering Center, or AMRDEC—conducted demonstrations on Modular Missile Technologies, a line of modular open systems architecture test missiles. The missiles were launched from a fixed stand and flew the ballistic path the team had planned. The highly adaptable open architecture hardware and software design of the Modular Missile Technologies will not only reduce life cycle costs for future aviation weapons, but likewise will provide greater flexibility and the aptitude to originate improvements rapidly.

Another locality that they are exploring is air-launched effects, including unmanned aerial vehicles and missiles. These can subsist launched from current platforms such as an Apache assail helicopter or a Gray Eagle unmanned aircraft system, or from platforms noiseless in development, such as the Future assail Reconnaissance Aircraft (FARA). These platforms will hold a variety of payloads to degrade or demolish advanced unmanned aerial systems and provide champion to troops on the ground.

The Aviation & Missile heart is developing a FARA prototype, which will subsist a smaller variant than the Future Long-Range Assault Aircraft that is likewise in development. These future aircraft will hold multiple types of unmanned aerial systems with lethal and nonlethal effects that can operate in communications- and GPS-denied environments.

WHAT IT NEEDS TO DO

Based on multidomain operational concepts, emerging requirements for Future perpendicular raise involve the aptitude to coast farther and faster, to carry heavier payloads, subsist easier and less expensive to sustain, to team with unmanned systems and perform inescapable optionally piloted missions.Aviators need to subsist able to operate day or night in utter types of weather, including degraded environments such as sand, smoke, smog, clouds, fog, rain, snow, and brownout or whiteout conditions. Degraded visual environment (DVE) technology will enhance operations, making it workable to descry the enemy without being seen, which will greatly extend lethality and survivability. piece of readiness is being able to operate in different environments, so DVE will originate a captious repercussion when it’s fielded by increasing combat power as well as preventing mishaps.

To champion aviation survivability, they are exploring innovative technologies that will caution aircrews of incoming small-arms or machine-gun fire early enough for them to consume evasive action and launch a counterattack. These technologies will outpace evolving threats with coordinated effects that will detect, avoid or overcome threats by reducing platform susceptibility and vulnerability.

A number of their efforts that will enable Future perpendicular raise to perform both manned and unmanned operations link directly to the Army’s priorities, including robotics, autonomy and artificial intelligence (AI).For example, they are leveraging multiple areas of expertise across the command, including engineers who can bear technology that allows platforms to perform involved navigation and a communications system that will operate in anti-access and locality denial environments. To champion this effort, they started the Advanced Teaming for Tactical Aviation Operations program in FY19. Both industry and DOD hold invested in this effort, so their challenge is to rapidly select the best components from industry that will labor on military aircraft.

MULTITASKING SENSORS

Existing sensors are used for a single purpose, but it is no longer enough to hold part sensors for targeting, survivability and navigation. As a result, they are developing multipurpose sensors that will not overload the size, weight and power of the aircraft and will reduce the cognitive tribulation on pilots from data overload. These next-generation, multifunction electro-optical and infrared sensor systems will provide situational awareness in anti-access and locality denial environments and automate targeting capabilities.

DEMONSTRATORS AND PROTOTYPES

The Aviation & Missile heart is working closely with industry to design and build a Joint Multi-Role Technology Demonstrator for Future perpendicular Lift, which will incorporate existing and experimental vertical-lift capabilities for future programs. The Army is using the technology demonstrator to conduct ground and flight demonstrations, which will abet inform requirements for next-generation Army aircraft.

Additionally, the Army already has directed competitive prototypes for the FARA to subsist developed by the Aviation & Missile Center. The FARA will subsist a light-attack and reconnaissance aircraft that will subsist able to avoid radar detection and operate in densely populated megacities. Requirements for the FARA ,include enough AI to coast unmanned at least piece of the time, a secure communications network to control specialized drones, an open architecture, precipitate up to 235 miles per hour and the aptitude to achieve targets 155 miles away. The Army plans to conduct flight testing on the prototypes in 2023 and originate a procurement conclusion in 2024, then province this new capability to a combat unit soon afterward.

TEAMING WITH PARTNERS

CCDC has hundreds of cooperative research and progress agreements with many industry partners, including Boeing Co., Lockheed Martin Corp., Karem Aircraft Inc. and AVX Aircraft Co. Their academic partners on Future perpendicular raise involve Penn status University, the University of Maryland and the Georgia Institute of Technology. Their international partners are the United Kingdom, France, Israel and Germany. These agreements enable both parties to trade access to labs, equipment, data and other resources for technical knowledge.

We likewise lead the perpendicular raise Research heart of Excellence program, a collaborative worry between government and academia to develop, evaluate, demonstrate and test advanced vertical-lift technologies.

The aviation community is close-knit, fostering captious transition champion and sharing information across organizational lines. They hold developed a road map to transition captious technologies in the short, medium and long term. This will ensure that future increments of vertical-lift platforms will maintain overmatch well beyond initial and complete operational capability. They hold transition agreements with both the U.S. Army Aviation heart of Excellence and PEO Aviation on captious efforts to originate confident that both the requirements and acquisition communities are ready to transition on time.

CONCLUSION

Under the Army Futures Command and as a captious member of the Future coerce Modernization Enterprise, they are informing new concepts of operation and expanding what’s workable in many captious technologies, including those required for Future perpendicular Lift. These technologies will provide commanders with increased reach, protection and lethality where they are most needed—on the battlefield of the future.

MAJ. GEN. CEDRIC T. WINS is the commanding generic of CCDC. He graduated from the Virginia Military Institute and was commissioned in the province Artillery in July 1985. His military education includes province Artillery Officer Basic and Advanced Courses, U.S. Army Command and generic Staff College and the National War College, where he earned an M.S. in national security and strategic studies. He likewise holds an M.S. in management from the Florida Institute of Technology.

This article is published in the Spring issue of Army AL&T magazine.

Date Taken: 03.28.2019 Date Posted: 03.28.2019 13:06 Story ID: 316023 Location: US Web Views: 7 Downloads: 0 Podcast Hits: 0 PUBLIC DOMAIN  

Monitoring Routine Pharmaceutical Freeze Drying with Tunable Diode Laser Absorption Spectroscopy | killexams.com true questions and Pass4sure dumps

The water content in pharmaceuticals is extremely indispensable to the finished product, tickle give an overview of how the water content impacts pharmaceutical product quality?

Freeze drying is a long and expensive fill/finish operation that is required for many pharmaceutical and biotechnology products to ensure product stability, efficacy and long shelf life prior to distribution to patients.

One of the key parameters affecting product trait is residual moisture (RM) content. The RM is typically lowered to a plane that will no longer champion chemical reactions (or biological growth).

RM is lowered to control chemical and physical degradation processes. Although most drug products are dried to RM values of <1%, some of the new biotechnology products may require an intermediate RM value, 2-5%, to maximize product stability, safety and efficacy.

Despite the import of RM, there is currently no real-time monitor capable of determining RM throughout the secondary drying angle of lyophilization.

What effect would too much, or too minute water hold on a finished pharmaceutical product?

The effect of residual moisture on product stability has been studied for numerous products.  In most cases lower levels of residual water improves stability and the manufacturer needs to poise increased drying times that result in lower water levels with a reduction in process efficiency and additional processing complications to achieve and maintain the extremely low levels of residual moisture.

In some cases, lower levels of residual water can lead to a reduction in product stability, specifically for protein-based products and vaccines. It has been reported that a minimum plane of residual water is required for conformational stability and over-drying can lead to a loss in product trait and stability.

What does Physical Sciences Inc. tender to monitor and manage water removal?

Physical Sciences Inc. has developed a Tunable Diode Laser Absorption Spectroscopy (TDLAS) based water vapor mass flux rate monitor, called the LyoFlux®, that provides continuous measurements of the water removal rate during pharmaceutical freeze-drying operations.

The sensor uses a non-intrusive, near IR optical absorption technique to monitor the water vapor concentration and gas flux velocity in the duct connecting the lyophilizer product chamber and condenser.

These measurements are used in combination with the duct cross-sectional locality to figure the instantaneous water vapor mass flux rate (grams/second) in the duct. These measurements can subsist integrated to provide a continuous, real-time determination of the total amount of water removed during the freeze-drying process.

We note that the determination of product RM is a minute harder to determine. The finish user cannot simply subtract the integrated TDLAS measurements of water removed during freeze-drying from the total amount of water added to the batch of vials undergoing freeze-drying.

This is because the total solid content of the formulations is typically in the 5% - 10% range. Measuring RM of the remaining solid following drying would require a measurement mistake of 0.05% - 0.1% for a product RM content of 1%.

This is beyond the capability of the TDLAS sensor, and in fact most analytical instruments. In collaboration with the University of Connecticut, TDLAS-based determination of RM has been demonstrated based on correlating the TDLAS measured water vapor concentration at the finish of primary drying to Karl Fisher based RM measurements.

The progress of the correlation curve for a specific product is performed once in a laboratory scale freeze-dryer and then has been used for subsequent laboratory and pilot scale experiments. This correlation curve essentially erases the measurement mistake accumulated during primary drying and enables accurate integration of the measurements during secondary drying to forecast product RM throughout secondary drying with a useful measurement accuracy of +/-0.5%.

What features does PSI tender to ensure a dependable freeze-drying process?

The LyoFlux® sensor provides real-time data that can subsist used to efficiently develop and monitor freeze drying processes.  utilize of the sensor helps process engineers reclaim time and money in developing their cycles and supports rapid personality of product by production engineers following a process disruption.

PSI, through a long and productive collaboration with the University of Connecticut, has demonstrated numerous applications of the LyoFlux® technology for monitoring freeze-drying processes including the determination of:

  • Primary and secondary drying endpoints
  • Vial heat transfer coefficients, Kv
  • Product temperature at the bottom heart of the vial, Tb
  • Product temperature at the sublimation interface, To
  • Continuous determination of the ice and dryer layer thicknesses
  • Product resistance to drying, Rp
  • The maximum lyophilizer outfit capability: mass flux as a role of pressure
  • Residual moisture content
  • Many of the pharmaceutical industry finish users hold likewise demonstrated these measurements and additional applications of the technology, including the progress of trait by Design (QbD) information and design spaces.

    Back in the late 1990’s and early 2000’s a pair of indispensable papers were published that demonstrated that through the utilize of gravimetric determinations of mass flux and the well-accepted steady status model of heat and mass transfer of vial-based freeze-drying, a product, vial and dryer specific process design space could subsist calculated and graphically displayed that provided lyophilization process engineers with a comprehensive overview of the drying process.

    This approach calculated shelf and product temperature isotherms of the water vapor mass flux rate as a role of process pressure. When combined with the information of the outfit capability limit, likewise displayed as mass flux as a role of process pressure, the graphical output provided a QbD based routine of developing the lyophilization process design. In addition, the process engineer could forecast how the product temperature would respond to workable process shocks in pressure and shelf temperature.

    The only problem with the gravimetric based approach was that the data required to figure the information and design spaces would require weeks to months to obtain. That is because gravimetric based experiments required for the data collection had to subsist precipitate in steady state.

    These data were used to determine the key parameters including the pressure conditional values of Kv, the continuously changing value of Rp (as a role of parch layer thickness) and the pressure conditional outfit capability limits.

    Use of the TDLAS sensor to acquire the identical data means that experiments no longer had to subsist precipitate at a fixed set of conditions (e.g. pressure) and this shortened the data collection period to approximately 1-2 weeks. The utilize of the TDLAS sensor to develop the process design space will hold the highest repercussion on pharmaceutical freeze-drying process development.

    The LyoFlux sensor from PSI are likewise available to subsist fitted into SP Scientific freeze-dryers, tickle give an overview of your partnership and how this collaboration helps both companies wait at the cutting edge of freeze-drying.

    PSI has had a relationship with SP Scientific from the initial experiments that demonstrated the feasibility of the TDLAS measurements in a Lyostar 2 freeze dryer back in 2004. During the past ~5-years, this relationship has been strengthened and is now producing product improvements and new products that directly profit their pharmaceutical customers.

    This collaboration has enabled efficient application of the TDLAS technology to different scale lyophilizers, such as the miniature LyoCapsule and the larger LyoConstellation S20 pilot scale dryer.

    We hold successfully demonstrated accurate mass flux measurements and importantly, accurate determinations of batch mediocre product temperatures when the mass flux measurements were combined with the heat and mass transfer model of freeze drying. Thus, these tools enable more rapid process progress and scale-up, ultimately allowing finish users to streamline their processes and glean their products to the market faster.

    A key improvement has been the progress of software that allows direct communication between the freeze-dryer and the TDLAS sensor. This communication provides the sensor with continuous measurements of the freeze dryer product chamber pressure, shelf temperature and the product temperature data.

    The pressure and shelf temperature values provided to the sensor remove the need for user inputs to the sensor throughout the lyophilization process and allow truly autonomous operation of the sensor 24/7/365.  This is a stout profit to the finish user, saving time, resulting in fewer data collection errors and providing a single location for capturing and plotting the process data for subsequent analysis.

    Process recipes are input into the dryer and the user initiates the freeze-drying cycle, from this point the process runs without operator intervention to apply this technology. At the confiscate times in the process the dryer opens and closes the isolation valve to determine measurement offset values and initiates sensor integration of the mass flux measurements.

    The TDLAS sensor measurements and utter of the resulting heat and mass transfer model data products calculated using the sensor and freeze dryer data are transferred to the lyophilizer control computer for storage using the dryer data historian. This ensures a common time stamp for utter of the data and easier pomp and processing by the finish user.

    The collaboration between SP Scientific and PSI keeps us both at the cutting edge of freeze-drying technology by allowing us to labor together on research and progress projects. The establishment of communication (and control) between the sensor and the freeze-dryer has likewise provided the foundation for more advanced applications of the sensor technology. PSI and SP Scientific recently completed a collaborative R&D worry that has created the TDLAS SMART Freeze Dryer technology.

    The TDLAS SMART FD enables the progress of a freeze-drying cycle using a single experiment. It combines the existing Manometric Temperature Measurement (MTM) based SMART algorithm with TDLAS determinations of the water vapor mass flux to create a new product that overcomes the limitations of the original MTM based product.

    The SMART™ freeze drying technology automatically generates a freeze-drying cycle based on user inputs of product characteristics and vial dimensions, combined with non-intrusive TDLAS-based in-process product temperature measurements and a pseudo-steady status heat and mass transfer model of freeze drying in vials.

    This progress enables the application of the SMART FD technology for a much wider ambit of product formulations, including tall weight percentage amorphous biological products, on both laboratory and pilot scale freeze dryers, supporting both process progress and scale-up.

    PSI and SP Scientific continue to explore additional applications and automation of cycle progress with the ultimate goal of sensor-based cycle control. They recognize that automated cycle control will require many years of additional R&D and industry testing, but achievement of this goal is directly in-line with the FDA Process Analytical Technology (PAT) and trait by Design (QbD) initiatives.

    What features champion your customers in increasing their understanding and robustness of the routine freeze-drying process?

    The recent progress and rollout of sensor and freeze dryer software packages enable direct communication and control between the sensor and the lyophilizer are indispensable improvements for their customers. The communication (and control) capability significantly improves user application of the sensor technology, reducing the potential for end-user errors in applying the LyoFlux® sensor.

    The data sharing between the two systems likewise enables real-time and accurate calculation of the heat and mass transfer model data products and storage of the data on the freeze-dryer. Their goal is to eventually liquidate the sensor user interface, preparing the combined system for application in a manufacturing environment.

    This will provide finish users with a moneyed library of data for each batch of the product that is freeze-dried. Currently these true time data sets provide process progress scientists with the information needed to extend their understanding of the drying process, life cycle management, and enable progress of robust processes using trait by Design principles. In the future this data will subsist available to enable more rapid release of lyophilized product and more rapid personality of product following process deviations.

    How does the TDLAS sensor integrate with other freeze dryer scales in the manufacturing process to ensure a smart approach to production and a dependable trait of the product?

    The LyoFlux® sensor is applicable to utter size freeze dryers as long as they can insert an optical spool in between the freeze dryer product chamber and the condenser. PSI has demonstrated application on dryers ranging from the SP Scientific LyoCapsule® miniature, seven, 20mL vial lyophilizer, which has a 2-inch spool, up to a manufacturing scale lyophilizer which has a 30-inch diameter spool.

    The sensor control electronics, data acquisition and processing algorithm are common for utter size dryers. A fiber optic and electronic signal umbilical cable connects the sensor control unit to the freeze dryer optical spool.

    A common optical platform is used for utter spools, thus the only change with dryer scale is the size of the spool and the location of the optical ports. Because they are using absorption spectroscopy, longer absorption pathlengths associated with larger spools provide higher signal to noise ratios.

    To date their most common installation has been on the SP Scientific Lyostar 3 laboratory scale freeze-dryer, but they hold fielded many recent inquiries for potential installations on larger dryers.

    We believe that this indicates a natural evolution of the product application from the laboratory to manufacturing scale. It may subsist occurring at this time due to widespread industrial information of the technology and the numerous publications describing application of the sensor, including a publication by the FDA.

    Companies that adopt the LyoFlux® technology for monitoring their processes on utter scale freeze dryers will hold common data sets from progress through scale-up to manufacturing.  This should result in improved process understanding, likely faster time to market and more efficient assessment of product trait in the event of process disruptions.

    What’s next for Physical Sciences Inc?

    Physical Sciences Inc. continues to labor with both their academic and industrial collaborators to develop next generation sensors, and to ameliorate application of the TDLAS technology, with the eventual goal of using the LyoFlux® sensor and other PAT sensors for real-time process control.

    PSI is currently collaborating with university and industrial partners to apply the TDLAS technology and process models for improved lyophilization process progress and scale-up. This includes the progress of a new aseptic, pilot scale lyophilization facility to enable testing of new sensors and process progress models in an environment that is representative of a manufacturing facility.

    This will champion the pharmaceutical industry and the training of the next generation scientists and process engineers on lyophilization process progress and scale-up. This facility, which is expected to Come on-line in descend 2019, will subsist available as a user facility to both academia and industry. In summary, they are noiseless actively pursuing numerous R&D projects to advance the sensor and its application.

    Where can users find more information? About Bill Kessler

    Bill Kessler received his undergraduate and graduate degrees in chemistry from Boston University, performing ultra-high-resolution gas spectroscopy. His research focused upon multi-photon excitation of aligned ionic states.

    In 1986 Bill joined Physical Sciences Inc. in Andover, Massachusetts as a staff scientist. His labor at PSI has spanned numerous disciplines including combustion, atmospheric, and chemical laser research. Bill’s focus shifted to the progress of diode laser-based sensors and sensor networks which leveraged the tools created by the optical telecommunication industry for industrial sensor applications.

    In December 2000 Bill became a founding member of the PSI-spinout company, Confluent Photonics Corp., focused on developing tall index contrast optical waveguides for optical telecommunications. Bill served as the Director of Applications Engineering while at CPC and its partner, the MIT Lincoln Laboratory, developed silicon-on-insulator waveguide tapers for efficient laser source coupling to single mode photonic circuits.

    He returned to PSI to pursue the progress and application of advanced optical sensors for pharmaceutical process monitoring and control. Bill is now the Vice President of the Applied Optics traffic enterprise within PSI and is the technical lead for the tunable diode laser absorption spectroscopy based LyoFlux® product. He oversees two additional traffic units, the Biomedical Optics Technology and Aerothermal System groups.

    Contact information: [email protected], 978-738-8253


    Finding Defects In Chips With Machine Learning | killexams.com true questions and Pass4sure dumps

    Chipmakers are using more and different traditional instrument types than ever to find killer defects in advanced chips, but they are likewise turning to complementary solutions fancy advanced forms of machine learning to abet unravel the problem.

    A subset of artificial intelligence (AI), machine learning has been used in computing and other fields for decades. In fact, early forms of machine learning hold been used in metrology and inspection in fabs since the 1990s to pinpoint defects in chips and even forecast problems using pattern-matching techniques. Machine learning isn’t a instrument or outfit character per se, but rather it’s a set of software algorithms used by the system to abet find defects. Now the industry is either exploring or starting to utilize systems with more advanced machine learning algorithms based on larger data sets. This, in turn, supposedly speeds up the cycles of learning.

    This will not supplant the traditional methods, at least in the short term. So far, more advanced forms of machine learning are not widely deployed throughout the fab, and some gaps remain. But the industry is making progress as it strives to unravel the daunting challenges in defect detection.

    In today’s fabs, chipmakers utilize various inspection and metrology systems to find defects in chips. Inspection is the science of finding defects, while metrology is the expertise of measuring structures. Both technologies are used to locate problems in devices, and they abet ensure yields in the fab.

    Still, at each node, the devices and structures are becoming smaller. In some cases, the structures are well below 1 angstrom, which is equal to 0.1nm. Finding defects at that scale is far more difficult and costly.

    Tools finish exist for that purpose, and many incorporate some plane of machine learning. So far, utilization has been spotty for more advanced forms of machine learning. But as advanced machine learning algorithms are developed for defect detection, this could change. At issue is whether enough profitable data is available, which would allow manufacturers and packaging houses to locate defects in a snappily and accurate manner. If the data sets are inadequate, a system can generate questionable or even inaccurate results.

    In either case, defect detection with machine learning will continue to subsist used for some apps in the fab. As the technology advances, though, it might descry broader utilize in the industry.

    “Machine learning is an retort for some metrology problems.” said David Fried, vice president of computational products at Coventor, a Lam Research Company. “There will subsist more and more problems where that solution becomes applicable. It’s not a panacea. It’s not the right retort for everything.”

    Nonetheless, the industry continues to ameliorate the technology. Here’s are some of the latest efforts in this arena:

  • Imec and Nova developed a pass to forecast electrical performance in chips using machine learning. Separately, GlobalFoundries and Nova developed a similar technology.
  • Imec devised a CD-SEM with abysmal learning.
  • ASML and SK Hynix improved optical proximity correction (OPC) accuracy using the technology.
  • IBM and USC devised a neural network for improved defect detection.
  • Inspection/metrology challengesToday’s 300mm fabs are automated plants that process wafers in a step-by-step flux using a variety of equipment. An advanced logic process could hold from 600 to 1,000 steps, or more. At different stages, a chip undergoes various metrology and inspection steps.

    These steps are critical. A missed defect can repercussion capitulate in the fab, or escape into the province and understanding a failure at a later date.

    At 28nm and above, metrology and inspection are straightforward. For example, a logic transistor is a planar with big features. With relative ease, chipmakers can measure and inspect devices. This is more difficult with finFETs at 16/14nm. But as chip designs migrate to 10nm/7nm and beyond, the structures are smaller and harder to measure. The identical is suitable for the latest DRAMs and NAND devices.

    Both finFETs and recollection devices are 3D-like in nature. So in the case of metrology, the tools must not only obtain 2D measurements in structures, but they must likewise obtain them in three dimensions in a cost-effective manner.

    “You need to know what the shape looks like. Then, you need to subsist able to descry things fancy materials composition, so you know that it’s uniform. A lot of the defects are underneath the layers,” said Dan Hutcheson, chief executive of VLSI Research.

    Indeed, defect detection is challenging. For example, so-called quiescent defects may crop up in devices. These defects finish not show when a device is shipped, but they are in some measure activated in the province and could finish up in a system. “Sometimes, it takes three or four different things to befall utter at once for a defect to actually subsist a killer defect in a particular location,” Hutcheson said.

    Compounding the challenges, no one metrology and inspection instrument character can find utter defects. For example, more than a dozen metrology instrument types are required to characterize finFETs in the fab.

    Ideally, chip manufacturers want tools with better sensitivities with higher throughputs at lower costs. “What they need is to consume much larger sample sizes, so they need more productivity in the tools,” Hutcheson said.

    Going forward, suppliers of metrology and inspection gear will continue to ameliorate their systems. Meanwhile, in a parallel path, metrology/inspection vendors continue to develop machine learning techniques using neural networks. In neural networks, the system crunches data and identifies patterns. It matches inescapable patterns and learns which of those attributes are important.

    Neural networks consist of multiple neurons and synapses. A neuron could consist of a recollection cell with logic gates. The neurons are daisy-chained and connected with a link called a synapse.

    Neural networks role by calculating matrix products and sums. It consists of three layers—input, hidden, and output.

    Fig. 1: DNNs are largely multiply-accumulate. Source: Mythic

    In operation, there might hold a hundred different defect types. Each defect character is imaged and the information is withhold into the input layer in the network.

    Then, each defect character is moved into an individual neuron in one of the hidden layers (layer 1) and assigned a weight. In another hidden layer (layer 2) the defects might subsist sub-divided into different classes, such as edge, protrusion, and others. They are likewise assigned weights.

    In the fab, a system detects a defect. In each layer, the neuron reacts to the data. Using a weighted system of connections, one neuron in the network reacts the strongest when it senses a matching pattern. The retort is revealed in the output layer.

    Machine learning is used by search engine and sociable media companies, as well as other fields. “Deep learning is mighty because it actually gives you an chance to finish things that are more accurate faster,” said Aki Fujimura, chief executive of D2S. “In medical imaging, for example, you are really honing in on exactly which cells are cancerous. Using a abysmal learning engine, they can narrow it down to exactly which cells are bad. That’s a medical example. But you can imagine the identical benefits that can subsist derived in semiconductor production.”

    The IC industry is using machine learning for circuit simulation, charged spot detection and locating defects. “The applications are huge,” said Philippe Leray, litho process and patterning control group leader at Imec. “You can utilize it for lithography, etch and utter the different steps. You can utilize it for machine maintenance.”

    One of the challenges with machine learning in generic is that you must feed the system with enough data. Metrology/inspection systems result the identical principle. You need to feed a system enough data to originate it work. This can subsist a costly and difficult task. But if you don’t provide enough data, a problem can surface.

    “You can finish bizarre things and you can subsist fantastically wrong,” Leray said. “All of the vicissitude is in training your data set. If it’s stout enough, representative enough, and unbiased enough, then you can hold a profitable answer. That’s a stout challenge. You can fool yourself very easily just by providing a set of training that is inadequate or biased.”

    If the data is inadequate, the outcomes are undesirable and could result in a untrue negative or a untrue positive. A untrue negative indicates that a chip doesn’t hold a defect, when it actually does. A untrue positive is a test result that is incorrect.

    Nonetheless, the technology is becoming a key piece of metrology and inspection. “Machine learning and abysmal learning are rapidly being adopted, improving training and the integrity of output results. Networks are trained to result process changes, filter out outliers, and untrue distributions,” said Ofer Adan, director of metrology and process control and distinguished member of the technical staff at Applied Materials. “Machine learning can utilize history and pre-defined information to ameliorate performance. abysmal learning has a huge advantage to extract information and attributes from images, which sometimes are too complicated for human or even yardstick machine methods to handle. However, there is no magic. If they know the physical model, they could glean better results than DL/ML. So, if they know something on the physical model they can utilize it to abet the abysmal learning model. One pass to finish it is to utilize it in the cost role which the abysmal learning is using for optimization. So the retort is they should utilize a combination of both.”

    There is another pass to gawk at the technology. “All metro/inspection vendors utilize it in various ways. The key question is does it provide any unique profit to the fab? The indispensable things to note is that machine learning is simply another enabling technology, which needs product integration and a abysmal plane of customization to subsist useful to a fab,” said Kartik Venkataraman, director of product marketing at Nanometrics. “What is more common is for vendors to utilize a machine learning approach to extract the maximum information from their tools, which is why signal quantity and trait is noiseless critical. Machine learning can only subsist as useful as the raw signals it is fed.”

    Where is machine learning?Today, machine learning is used by some but not utter chipmakers. Some utilize it in various steps in the process flow. It depends on the company.

    In the fab, some metrology and inspection systems utilize machine learning to abet find defects. Supposedly, machine learning automates the process, but it doesn’t always labor that way. At times, the system requires manual intervention by the operator to pull and examine the data.

    But the bigger issue is that a instrument from one vendor incorporates proprietary software, and is unable to communicate with systems from other companies. Some are working on integrating their systems to create an end-to-end feedback flow, but the technology is noiseless in R&D, according to experts.

    Ultimately, chipmakers would fancy an end-to-end quick-witted feedback solution with tools from different vendors. Some are developing the technology, although this requires investment and resources.

    In addition, the industry likewise would fancy tools with more advanced machine learning capabilities, particularly in wafer inspection. “Machine learning will become more indispensable as tools become more powerful,” said brand Smith, technical champion engineer at KLA.

    Wafer inspection involves two main instrument technologies—e-beam and optical. Today, optical inspection is the workhorse instrument in the fab. E-beam inspection is used in R&D and some parts of the fab. E-beam inspection has better resolutions than optical, but it is slower.

    In a simple example, an inspection system inspects a wafer and the data is compared to a die or a database. Then, using a neural net, it spots the defects using pattern recognition techniques and automatically classifies them.

    This technique, which has been around since the 1990s, is based on traditional methods. Companies continue to develop the traditional techniques, although they are likewise working on more advanced forms of machine learning.

    “Many of KLA’s core detection technology is based on traditional image processing techniques, but they are doing labor with machine learning as well,” KLA’s Smith said. “A pair of areas where machine learning is having a really stout repercussion are in image classification and review sampling.”

    There are some challenges to making utter of this work. “Deep learning, the most advanced algorithms, is making headlines now. They confidence on very big data sets to calibrate and train the models,” Smith said. “This isn’t always workable when searching for a new defect of interest (DOI), so they are taking a hybrid approach. There is noiseless a lot of steam in some of the not-so-new approaches, if you add more information about how the inspection tools labor and device information. For example, they commonly utilize design layout information as piece of their algorithms.”

    Others likewise are working on the technology. For example, in a recent paper, ASML and SK Hynix described a routine of improving the accuracy of OPC in photomasks. This is accomplished using an e-beam instrument and abysmal learning.

    OPC makes utilize of tiny shapes, or sub-resolution assist features (SRAFs). The SRAFs are placed on a mask, which modifies the mask patterns to ameliorate the printability on a wafer.

    At each node, OPC runtimes and cost are increasing. “By adding a lot of CD and edge-placement gauges, they reduce the model mistake by about a third. And by further changing the model profile from traditional models to abysmal learning models, they glean another 18% gain in accuracy,” said Yu Cao, senior vice president at ASML.

    Then, in another example, IBM and the University of Southern California (USC) recently presented a paper about e-beam inspection with machine learning. Traditionally, wafer inspection is conducted using die-to-die or die-to-database. With machine learning, though, IBM and USC passed the images through a trained model. This in eddy would finish the classification without the aid of a design or golden image. The trained model would subsist an inference engine, according to Ravi Bonam, member of the research staff at IBM. With this, researchers obtained an accuracy of 96.96% with 96.87% sensitivities.

    Besides inspection, machine learning likewise is used in metrology. “It’s highly used and rapidly being adopted across the metrology and inspection space, including CD SEMs, OCD and even optical in-situ metrology such as optical emission spectroscopy or reflectometry,” Applied’s Adan said. “It can subsist used when you hold a lot of information about a measure of interest but no direct measurement. Machine learning allows you find the correlation between a measure of interest and various parameters that might strike such a measure. It can likewise subsist used to forecast instrument performance, up time and other functions.”

    Metrology itself is challenging. For example, planar transistors require five to six measurements. In finFETs, though, it requires 12 or more different CD measurements, such as the gate height, fin height, fin width and sidewall angle.

    To handle the measurements, chipmakers require several metrology instrument types, such as a critical-dimension scanning electron microscope (CD-SEM), optical CD (OCD) and others.

    CD-SEMs, the workhorse metrology instrument in the fab, consume top-down measurements of the dimensions in a structure.

    In a recent paper, Imec described a CD-SEM with machine learning. In effect, the technology can “de-noise” the CD-SEM. In the tool, Imec added another layer in the neural net, dubbed a generative adversarial network (GAN). By passing a loudly SEM image through the trained GAN, researchers obtained a noiseless model image.

    Meanwhile, one OCD type, called scatteromentry, can measure the CDs, profiles and film thicknesses in finFETs. OCD is fast, but there are some drawbacks. OCD is a model-based technique. In many cases, the tools don’t measure the actual device. Instead, they measure surrogate or simple planar structures, which portray and behave fancy the actual device. The measurements between these structures and the actual devices are hypothetical to correlate.

    OCD modeling likewise takes a long time, however. Then, in another approach, the OCD community has developed a model-less technology, which is a profile of machine learning. It doesn’t supplant traditional OCD. Instead, it is a complementary technique that leverages OCD.

    “For some time, they hold used minute OCD models for device structures and targets, as well as their optical systems. But there are places where they hold had gaps for unmodeled components; this is where machine learning is having a stout impact. Machine learning has delivered improvements in metrology instrument fleet matching and in precision. With the proper reference metrology, machine learning can likewise lead to improvements in time to results,” said Stilian Pandev, director of advanced algorithms in the Patterning division at KLA.

    This could subsist a standalone computing system. Data is collected from OCD measurements and fed into the computer, which crunches the numbers and produces results.

    In one example, Imec and Nova recently presented a paper on predicting the electrical performance in chips using OCD spectra and machine learning. “The OCD technique is based on rigorous coupled-wave analysis (RCWA), which is designated for occasional structures. Applying this technique for non-periodic structures, such as electrical structures, is a challenge,” said Sayantan Das, an R&D engineer at Imec.

    “Machine learning algorithms can overcome these challenges, and becomes a complementary approach,” Das said. “Our labor with Nova has shown that machine learning using OCD spectra can forecast electrical performance with tall R2 values. This enables profitable correlation to reference CD. It demonstrates profitable correlation to diffraction-based overlay and SEM-based overlay. It improves the correlation between measured and predicted resistance and capacitance compared to the OCD model.”

    Meanwhile, in another example, GlobalFoundries and Nova recently used machine learning to forecast the resistance of copper interconnects in chips. The companies used spectra collected from both OCD measurements and electrical test sites. “The predicted resistance correlation to the actual e-test value is improved in comparison with OCD results for multiple metal levels of various products. In the FEOL sector, they hold demonstrated initial feasibility to forecast the fin CD values from an inline measurement using machine learning,” said Padraig Timoney, a metrology engineer at GlobalFoundries, in a paper. Others from GlobalFoundries and Nova contributed to the work.

    These and other results are impressive, at least in R&D. But are these results profitable and snappily enough for the fab?

    As before, machine learning for defect detection will continue to subsist used for some but not utter apps. “You really hold to understand and consider the trait of data you are feeding into these systems,” Coventor’s Fried said. “It’s the quality, the density and amount of data you are feeding into these systems that will determine the applicability.”

    Clearly, machine learning is no longer a novelty. The technology has been around for a long time. But in many ways, it’s just scratching the surface.

    Related StoriesUsing Sensor Data To ameliorate capitulate And UptimeDeeper understanding of outfit deportment and market needs will hold broad repercussion across the semiconductor supply chain.Inspecting Unpatterned WafersFinding smaller defects earlier is becoming more difficult and expensive.Variation’s Long, Twisty Tail Worsens At 7/5nmMultiple sources of variability are causing unexpected problems in everything from AI chips to automotive reliability and time to market.Fabs Meet Machine LearningD2S’ CEO sounds off on the repercussion of abysmal learning, EUV and other manufacturing advancements.



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