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Deep Learning To Classify And Establish Structure Property Predictions With PeakForce QNM Atomic Force Microscopy


Machine learning and specifically, deep learning, is a powerful tool to establish the presence (or absence) of microstructure correlations to bulk properties with its ability to flesh out relationships and trends that are difficult to establish otherwise. This application note discusses the use of deep learning tools, to explore AFM phase and PeakForce Quantitative Nanomechanics (QNM) im... » read more

Challenges Mount In New Autos


Electronics are becoming the primary differentiator for carmakers, adding an array of options that can alter everything from how a vehicle's occupants interact with their surroundings to how the vehicle drives. But the infrastructure needed to support these features also raises a slew of technology and business questions for which there are no simple answers today. For example, how will new ... » read more

Chipmakers Model AI For Radio Access Networks


The chips that power and connect smartphones are now foundational to a disparate portfolio of daily tasks we take for granted, from accessing the internet to snapping a photo or asking Siri or Google if rain is in the forecast. Most people don’t think twice about the conflicting demands these tasks can place on semiconductors, but for engineers at leading chip manufacturers, this balancing ac... » read more

AI Power Consumption Exploding


Machine learning is on track to consume all the energy being supplied, a model that is costly, inefficient, and unsustainable. To a large extent, this is because the field is new, exciting, and rapidly growing. It is being designed to break new ground in terms of accuracy or capability. Today, that means bigger models and larger training sets, which require exponential increases in processin... » read more

ML And UVM Share Same Flaws


A number of people must be scratching their heads over what UVM and machine learning (ML) have in common, such that they can be described as having the same flaws. In both cases, it is a flaw of omission in some sense. Let's start with ML, and in particular, object recognition. A decade ago, Alexnet, coupled with GPUs, managed to beat all of the object detection systems that relied on tradit... » read more

Bespoke Silicon Redefines Custom ASICs


Semiconductor Engineering sat down to discuss bespoke silicon and what's driving that customization with Kam Kittrell, vice president of product management in the Digital & Signoff group at Cadence; Rupert Baines, chief marketing officer at Codasip; Kevin McDermott, vice president of marketing at Imperas; Mo Faisal, CEO of Movellus; Ankur Gupta, vice president and general manager of Siemens... » read more

Customization, Heterogenous Integration, And Brute Force Verification


Semiconductor Engineering sat down to discuss why new approaches are required for heterogeneous designs, with Bari Biswas, senior vice president for the Silicon Realization Group at Synopsys; John Lee, general manager and vice president of the Ansys Semiconductor business unit; Michael Jackson, corporate vice president for R&D at Cadence; Prashant Varshney, head of product for Microsoft Azu... » read more

Improving Yield With Machine Learning


Machine learning is becoming increasingly valuable in semiconductor manufacturing, where it is being used to improve yield and throughput. This is especially important in process control, where data sets are noisy. Neural networks can identify patterns that exceed human capability, or perform classification faster. Consequently, they are being deployed across a variety of manufacturing proce... » read more

Who Does Processor Validation?


Defining what a processor is, and what it is supposed to do, is not always as easy as it sounds. In fact, companies are struggling with the implications of hundreds of heterogenous processing elements crammed into a single chip or package. Companies have extensive verification methodologies, but not for validation. Verification is a process of ensuring that an implementation matches a specif... » 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

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