Deep Learning Applications For Material Sciences: Methods, Recent Developments


New technical paper titled "Recent advances and applications of deep learning methods in materials science" from researchers at NIST, UCSD, Lawrence Berkeley National Laboratory, Carnegie Mellon University, Northwestern University, and Columbia University. Abstract "Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning... » read more

Hybrid Method For More Reliable Virtual Sensors Within Vehicle Dynamics Control Systems


New technical paper titled "Ensuring the Reliability of Virtual Sensors Based on Artificial Intelligence within Vehicle Dynamics Control Systems" from University of Duisburg-Essen. Abstract "The use of virtual sensors in vehicles represents a cost-effective alternative to the installation of physical hardware. In addition to physical models resulting from theoretical modeling, artificial in... » read more

Deep Learning In Industrial Inspection


Deep learning is at the upper end of AI complexity, sifting through more data to achieve more accurate results. Charlie Zhu, vice president of R&D at CyberOptics, talks about how DL can be utilized with inspection to identify defects in chips that are not discernible by traditional computer vision algorithms, classifying multiple objects simultaneously from multiple angles and taking into accou... » read more

New End Markets, More Demand For Complex Chips


Experts at the Table: Semiconductor Engineering sat down to discuss economic conditions and how that affects chip design with Anirudh Devgan, president and CEO of Cadence; Joseph Sawicki, executive vice president of Siemens EDA; Niels Faché, vice president and general manager at Keysight; Simon Segars, advisor at Arm; and Aki Fujimura, chairman and CEO of D2S. This discussion was held in front... » read more

Improving PPA With AI


AI/ML/DL is starting to show up in EDA tools for a variety of steps in the semiconductor design flow, many of them aimed at improving performance, reducing power, and speeding time to market by catching errors that humans might overlook. It's unlikely that complex SoCs, or heterogeneous integration in advanced packages, ever will be perfect at first silicon. Still, the number of common error... » read more

Artificial intelligence deep learning for 3D IC reliability prediction


New research from National Yang Ming Chiao Tung University, National Center for High-Performance Computing (Taiwan), Tunghai University, MA-Tek Inc, and UCLA. Abstract "Three-dimensional integrated circuit (3D IC) technologies have been receiving much attention recently due to the near-ending of Moore’s law of minimization in 2D IC. However, the reliability of 3D IC, which is greatly infl... » read more

Expedera: Custom Deep Learning Accelerators Through Soft-IP


Internet of Things (IoT) and Artificial Intelligence (AI) have caused a massive increase in data generation — and along with it, a need to process data faster and more efficiently. Dubbed a “tsunami of data,” data centers are expected to consume about one-fifth of worldwide energy before 2030. This data explosion is driving a wave of startups looking to gain a foothold in custom accele... » read more

Survey: 2022 Deep Learning Applications


The 2022 member list of deep learning projects and products that eBeam members are working on in photomask to wafer semiconductor manufacturing. Participating companies include Advantest, ASML, Canon, CEA-LETI, D2S, Fraunhofer IPMS, Hitachi High-Tech Corporation, imec, NuFlare Technology, Siemens Industries Software, Inc.; Siemens EDA, STMicroelectronics, and TASMIT. Click here to see the su... » read more

The Right Project Is Key For Photomask Adoption Of Deep Learning


Deep learning (DL) has become an integral part of the success of many companies. There have been many papers and some reported successes in semiconductor manufacturing, yet only 22% of the luminaries participating in the 2021 eBeam Initiative Luminaries survey see DL as a competitive advantage for photomask making by next year, as shown in figure 1. Looking at that chart, the luminaries believe... » read more

Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS


Abstract: "Camera-based Deep Learning algorithms are increasingly needed for perception in Automated Driving systems. However, constraints from the automotive industry challenge the deployment of CNNs by imposing embedded systems with limited computational resources. In this paper, we propose an approach to embed a multi- task CNN network under such conditions on a commercial prototy... » read more

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