Monitor Etch Defects on Dies in the Outer Regions Of The Wafer Using ISR


A technical paper titled "Detection of defective chips from nanostructures with a high-aspect ratio using hyperspectral imaging and deep learning" was published by researchers at Samsung Electronics. Abstract: "We have developed an imaging spectroscopic reflectometry (ISR) method based on hyperspectral imaging and deep learning to detect defects in the bottom region of high-aspect-ratio nan... » read more

Analog In-Memory Computing: Fast Deep NN Training (IBM Research)


A new technical paper titled "Fast and robust analog in-memory deep neural network training" was published by researchers at IBM Research. Abstract "Analog in-memory computing is a promising future technology for efficiently accelerating deep learning networks. While using in-memory computing to accelerate the inference phase has been studied extensively, accelerating the training phase has... » read more

DL Compiler for Efficiently Utilizing Inter-Core Connected AI Chips (UIUC, Microsoft)


A new technical paper titled "Scaling Deep Learning Computation over the Inter-Core Connected Intelligence Processor" was published by researchers at UIUC and Microsoft Research. Abstract "As AI chips incorporate numerous parallelized cores to scale deep learning (DL) computing, inter-core communication is enabled recently by employing high-bandwidth and low-latency interconnect links on th... » read more

Co-optimizing HW Architecture, Memory Footprint, Device Placement And Per-Chip Operator Scheduling (Georgia Tech, Microsoft)


A technical paper titled “Integrated Hardware Architecture and Device Placement Search” was published by researchers at Georgia Institute of Technology and Microsoft Research. Abstract: "Distributed execution of deep learning training involves a dynamic interplay between hardware accelerator architecture and device placement strategy. This is the first work to explore the co-optimization ... » read more

6G And Beyond: Overall Vision And Survey of Research


A new 92 page technical paper titled "6G: The Intelligent Network of Everything -- A Comprehensive Vision, Survey, and Tutorial" was published by IEEE researchers at Finland's University of Oulu. Abstract "The global 6G vision has taken its shape after years of international research and development efforts. This work culminated in ITU-R's Recommendation on "IMT-2030 Framework". While the d... » read more

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

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