KAN: Kolmogorov Arnold Networks: An Alternative To MLPs (MIT, CalTech, et al.)


A new technical paper titled "KAN: Kolmogorov-Arnold Networks" was published by researchers at MIT, CalTech, Northeastern University and The NSF Institute for Artificial Intelligence and Fundamental Interactions. Abstract: "Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While... » read more

Using Deep Learning ADC For Defect Classification For Automatic Defect Inspection


In traditional semiconductor packaging, manual defect review after automated optical inspection (AOI) is an arduous task for operators and engineers, involving review of both good and bad die. It is hard to avoid human errors when reviewing millions of defect images every day, and as a result, underkill or overkill of die can occur. Automatic defect classification (ADC) can reduce the number of... » read more

New Ways To Optimize GEMM-Based Applications Targeting Two Leading AI-Optimized FPGA Architectures


A technical paper titled “Efficient Approaches for GEMM Acceleration on Leading AI-Optimized FPGAs” was published by researchers at The University of Texas at Austin and Arizona State University. Abstract: "FPGAs are a promising platform for accelerating Deep Learning (DL) applications, due to their high performance, low power consumption, and reconfigurability. Recently, the leading FPGA... » read more

Deep Learning (DL) Applications In Photomask To Wafer Semiconductor Manufacturing


How Advantest Corporation, ASML, Fraunhofer, imec, Siemens EDA and others are using deep learning in semiconductor manufacturing. Click here to read more. » read more

High-NA EUVL: Automated Defect Inspection Based on SEMI-SuperYOLO-NAS


A new technical paper titled "Towards Improved Semiconductor Defect Inspection for high-NA EUVL based on SEMI-SuperYOLO-NAS" was published by researchers at KU Leuven, imec, Ghent University, and SCREEN SPE. Abstract "Due to potential pitch reduction, the semiconductor industry is adopting High-NA EUVL technology. However, its low depth of focus presents challenges for High Volume Manufac... » read more

A Precision-Optimized Fixed-Point Near-Memory Digital Processing Unit for Analog IMC (IBM and ETH Zurich)


A technical paper titled “A Precision-Optimized Fixed-Point Near-Memory Digital Processing Unit for Analog In-Memory Computing” was published by researchers at IBM Research Europe and IIS-ETH Zurich. Abstract: "Analog In-Memory Computing (AIMC) is an emerging technology for fast and energy-efficient Deep Learning (DL) inference. However, a certain amount of digital post-processing is requ... » read more

Fabs Begin Ramping Up Machine Learning


Fabs are beginning to deploy machine learning models to drill deep into complex processes, leveraging both vast compute power and significant advances in ML. All of this is necessary as dimensions shrink and complexity increases with new materials and structures, processes, and packaging options, and as demand for reliability increases. Building robust models requires training the algorithms... » read more

Deep Learning Discovers Millions Of New Materials (Google)


A technical paper titled “Scaling deep learning for materials discovery” was published by researchers at Google DeepMind and Google Research. Abstract: "Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing. From microchips to batteries and photovoltaics, discovery of inorganic crystals has been bottleneck... » read more

Center For Deep Learning In Electronics Manufacturing: Bringing Deep Learning To Production For Photomask Manufacturing


The Center for Deep Learning in Electronics Manufacturing (CDLe) was formed as an alliance between D2S, Mycronic and NuFlare Technology in autumn 2018. Assignees from each alliance partner worked with deep learning (DL) experts under the leadership of Ajay Baranwal, director of CDLe. The CDLe’s mission was to 1) turn DL into a core competency inside each of the companies and 2) do DL projects... » read more

Analog In-Memory Cores With Multi-Memristive Unit-Cells (IBM)


A technical paper titled “Exploiting the State Dependency of Conductance Variations in Memristive Devices for Accurate In-Memory Computing” was published by researchers at IBM Research-Europe, IBM Research-Albany, and IBM Research-Yorktown Heights. Abstract: "Analog in-memory computing (AIMC) using memristive devices is considered a promising Non-von Neumann approach for deep learning (DL... » read more

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