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Adopting Predictive Maintenance On Fab Tools


Predictive maintenance, based on more and better sensor data from semiconductor manufacturing equipment, can reduce downtime in the fab and ultimately cut costs compared with regularly scheduled maintenance. But implementing this approach is non-trivial, and it can be disruptive to well-honed processes and flows. Not performing maintenance quickly enough can result in damage to wafers or the... » read more

AI-Driven Big Data Analytics Enables Actionable Intelligence, Improving SoC Design Productivity


As the latest systems on chip (SoCs) grow in size and complexity, a vast amount of design data is generated during verification and implementation. Design data is business critical and, with the proliferation of artificial intelligence (AI) use in chip design, provides designers an opportunity to carry forward learnings and insights with every new design. To achieve first-pass success deliverin... » read more

The Drive Toward More Predictive Maintenance


Maintenance is a critical behind-the-scenes activity that keeps manufacturing facilities running and data centers humming. But when not performed in a timely manner, it can result in damaged products or equipment, or significant system/equipment downtime. By shifting from scheduled maintenance to predictive maintenance, factories and electronic system owners can reap substantial benefits, in... » read more

Making The Most Of Data Lakes


Having all the semiconductor data available is increasingly necessary for improving manufacturability, yield, and ultimately the reliability of end devices. But without sufficient knowledge of relationships between data from different processes and computationally efficient data structures, the value of any data is significantly reduced. In the semiconductor industry, reducing waste, decreas... » read more

Finding Frameworks For End-To-End Analytics


End-to-end analytics can improve yield and ROI on tool purchases, but reaping those benefits will require common data formats, die traceability, an appropriate level of data granularity — and a determination of who owns what data. New standards, guidelines, and consortium efforts are being developed to remove these barriers to data sharing for analytics purposes. But the amount of work req... » read more

Elevating Production Testing With Deep Data Analytics And ACS At The Edge And Cloud


The level of system integration continues to increase at a rate of greater than 30% per year — fueled by the industry’s desire for increased capability, advanced process nodes, and "more than Moore" packaging techniques. Co-optimization of the hardware and software have also been required not only at the design stage, but at test and in the field. This white paper will present how to ele... » read more

The Power Of Big Data: Or How To Make Perfect 30-Minute Brownies In Only 30 Minutes


You're scrolling online, and the picture stops you in your tracks, grabs you, captivates you. Glistening chocolate pieces are, determinedly yet slowly, oozing down a moist brownie with a crisped-to-perfection, powdered topping. It sits there, confident, flaking lazily onto a bone-white china plate. It looks delicious—mouthwatering—and, apparently, you can make it with just a 30-minute inves... » read more

The Role Of AI And Endpoint Real-Time Data Analytics


The Internet of Things (IoT) has the capability of amassing large amounts of data which it does with the help of dispersed intelligent sensors. The organization and distribution of this enormous amount of data is posing a challenge. While conventional methods of data analysis have facilitated the operations in IoT, artificial intelligence (AI) has proven that it can do it with greater precision... » read more

Active Learning: Integrating Natural Intelligence Into Artificial Intelligence


Today, very few people would likely deny the fact that data can present major added value for companies. But analyzing data from production processes reveals the incompleteness of data collection and the associated reduced potential of the data that can be leveraged. Typical shortcomings include: Incomplete representation of processes in the dataspace, Inadequate connection of processes... » read more

Trends In Testing: New Challenges Create New Opportunities


As advancements in semiconductors and microelectronics soldier ahead into emerging, even uncharted, territory, new test challenges arise. To that end, let’s look at a few key trends and challenges that are driving opportunities for innovation in the test sector. Technology convergence has been a buzzword for some time, and this trend is only going to intensify with the heightened need to m... » read more

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