tag: deep learning

Enabling Production-Ready AI For Semiconductor Manufacturing
By Tareq Aljaber - 21 May, 2026 - Comments: 0

Semiconductor inspection has always been a scalability problem. Inspection teams are buried in manual reviews because the machines on the line throw false rejects, miss real defects, and can't learn from the data they're already producing. The job hasn't really changed in decades. Find defects faster. Find them with higher sensitivity. Keep cost down. And whatever you do, don't bury the review ... » read more

The Smart Advantage: How Artificial Intelligence Is Transforming Inspection And Metrology In Semiconductor Manufacturing
By Charlie Zhu - 14 Apr, 2026 - Comments: 0

There is no doubt that the semiconductor industry is in an era of rapid and profound transformation, driven by an increasing demand for smaller, faster, and more powerful chips. As the speed of innovation continues to advance, so does the pressure on semiconductor manufacturers to detect and address defects and inconsistencies with near-perfect accuracy to keep pace with this demand. Manual ... » read more

Survey of DL-Based LiDAR Super-Resolution For Autonomous Driving (University College London)
By Technical Paper Link - 24 Feb, 2026 - Comments: 0

University College London researchers published "A Comprehensive Survey on Deep Learning-Based LiDAR Super-Resolution for Autonomous Driving." Abstract "LiDAR sensors are often considered essential for autonomous driving, but high-resolution sensors remain expensive while affordable low-resolution sensors produce sparse point clouds that miss critical details. LiDAR super-resolution addre... » read more

A Review Of Acoustic Side-Channel Attacks: An AI View (Penn State Univ.)
By Technical Paper Link - 06 Jan, 2026 - Comments: 0

A new technical paper titled "A Survey on Acoustic Side-Channel Attacks: An Artificial Intelligence Perspective" was published by researchers at Penn State University. Abstract "Acoustic Side-Channel Attacks (ASCAs) exploit the sound produced by keyboards and other devices to infer sensitive information without breaching software or network defenses. Recent advances in deep learning, large ... » read more

DL Atomistic Semi-Empirical Pseudopotential Model For Nanomaterials (UC Berkeley, LBNL et al.)
By Technical Paper Link - 03 Jan, 2026 - Comments: 0

A new technical paper titled "Deep-learning atomistic semi-empirical pseudopotential model for nanomaterials" was published by researchers at UC Berkeley, Lawrence Berkeley National Laboratory et al. Abstract "The semi-empirical pseudopotential method (SEPM) has been widely applied to provide computational insights into the electronic structure, photophysics, and charge carrier dynamics of ... » read more

Co-Simulation Framework for Parallel DNN Execution on Chiplet-Based Systems (UW–Madison, Washington State)
By Technical Paper Link - 03 Nov, 2025 - Comments: 0

A new technical paper titled "CHIPSIM: A Co-Simulation Framework for Deep Learning on Chiplet-Based Systems" was published by researchers at University of Wisconsin–Madison and Washington State University. Abstract "Due to reduced manufacturing yields, traditional monolithic chips cannot keep up with the compute, memory, and communication demands of data-intensive applications, such as ra... » read more

Thermally-Aware, Multi-Objective Scheduling Framework for DL Workloads on Heterogeneous Multi-Chiplet PIM Architectures (UW–Madison, Washington State)
By Technical Paper Link - 19 Aug, 2025 - Comments: 0

A new technical paper titled "THERMOS: Thermally-Aware Multi-Objective Scheduling of AI Workloads on Heterogeneous Multi-Chiplet PIM Architectures" was published by researchers at the University of Wisconsin–Madison and Washington State University. Abstract "Chiplet-based integration enables large-scale systems that combine diverse technologies, enabling higher yield, lower costs, and sca... » read more

DL Compiler Framework For More Efficient Inter-Core Connected AI Chips (UIUC, Microsoft)
By Technical Paper Link - 21 Jul, 2025 - Comments: 0

A new technical paper titled "Elk: Exploring the Efficiency of Inter-Core Connected AI Chips with Deep Learning Compiler Techniques" was published by researchers at the University of Illinois Urbana-Champaign (UIUC) and Microsoft Research. Abstract "To meet the increasing demand of deep learning (DL) models, AI chips are employing both off-chip memory (e.g., HBM) and highbandwidth low-laten... » read more

Accelerator Architecture For In-Memory Computation of CNN Inferences Using Racetrack Memory
By Technical Paper Link - 03 Jul, 2025 - Comments: 0

A new technical paper titled "Hardware-software co-exploration with racetrack memory based in-memory computing for CNN inference in embedded systems" was published by researchers at National University of Singapore, A*STAR, Chinese Academy of Sciences, and Hong Kong University of Science and Technology. Abstract "Deep neural networks generate and process large volumes of data, posing challe... » read more

Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing (Infineon, U. Padova et al.)
By Technical Paper Link - 21 Jun, 2025 - Comments: 0

A new technical paper titled "Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing" was published by researchers at Infineon Technologies, University of Padova and University of Bologna. Abstract "In the semiconductor sector, due to high demand but also strong and increasing competition, time to market and quality are key factors in securing significant marke... » read more

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