Boosting EUV Conversion Efficiency With 2-Micron Dual-Beam Laser Irradiation


Researchers from Utsunomiya University, RIKEN, The University of Tokyo, and Tohoku University, et al. have published “40% boost in extreme ultraviolet conversion efficiency via simultaneous dual-beam 2-µm laser irradiation”. Abstract “Scaling extreme ultraviolet (EUV) source power for next-generation lithography demands higher conversion efficiency (CE) at reduced per-pulse ene... » read more

Read-Centric DTCO for IGZO FeFETs 3D Heterogeneous AI memories (imec, KU Leuven)


Researchers from imec and KU Leuven have published “DTCO of NOR-Type IGZO FeFETs for 3D Heterogeneous AI Memories: A Read-Centric Perspective”. Abstract excerpt “This work evaluates the viability of NOR-type IGZO FeFETs for 3D heterogeneous AI memories from a read-centric design-technology co-optimization (DTCO) perspective, spanning on-chip back-end-of-line (BEOL) RAMs an... » read more

Accelerating Zero-Knowledge Proof Generation With Reconfigurable Hardware (KAIST)


Researchers from Korea Advanced Institute of Science and Technology (KAIST) have published “ZK-Flex: A Flexible and Scalable Framework for Accelerating Zero-Knowledge Proofs”. Abstract “Zero-knowledge proofs (ZKP) allows a prover to convince a verifier of computational correctness without revealing private data, ensuring both privacy and verifiability. However, proof generation i... » read more

Analyzing Rowhammer Vulnerability in Monolithic 3D IWO eDRAM for Edge (ASU, Georgia Tech)


Researchers from Arizona State University and Georgia Institute of Technology published “Thermal- and Aging-Aware Rowhammer Vulnerability Analysis of Monolithically-Integrated IWO eDRAM for Edge Platforms”. "This work presents the first comprehensive temperature- and aging-aware vulnerability analysis of amorphous Indium Tungsten Oxide (IWO) embedded DRAM (eDRAM), a promising next-... » read more

Scaling Nanoribbon Transistors with Monolayer TMDs (Stanford, Chalmers, Horiba, SLAC)


Researchers from Stanford University, Chalmers University of Technology, HORIBA Scientific, and SLAC National Accelerator Laboratory have published “Scaling nanoribbon transistors with monolayer transition metal dichalcogenides”. Abstract “Nanoscale transistors demand aggressive scaling of all channel dimensions—length, width and thickness. Two-dimensional semiconductors (2DS... » read more

Using Graph Attention for Virtual Metrology in Semiconductor Manufacturing (Intel Foundry, ASU)


Researchers from Arizona State University and Intel Foundry have published “Graph Attention-Based Virtual Metrology for Film Deposition Processes in Semiconductor Manufacturing”. Abstract “Artificial intelligence-driven semiconductor manufacturing increasingly operates at nanometer and angstrom scales, where precise process control depends on accurate and timely metrology. Howeve... » read more

Surface Modification for III-V Selective Area MBE of Non-Selective Mask Materials (UT Austin, Harvard)


Researchers from University of Texas at Austin and Harvard University published “Surface Modification for III-V Selective Area Molecular Beam Epitaxy of Non-Selective Mask Materials”. Abstract Excerpt “Selective-area embedded regrowth of III-V semiconductors by molecular beam epitaxy enables the seamless integration of metals and dielectrics into crystalline material for novel... » read more

Scaling Open-Source HW Accelerator for Deep NN Inference (UDE, Fraunhofer IMS)


Researchers from University of Duisburg-Essen and Fraunhofer Institute for Microelectronic Circuits and Systems have published “OpenEye: A Scalable Open-Source Hardware Accelerator for DNNs”. Abstract “The increasing computational complexity of deep neural network inference poses significant challenges for efficient hardware acceleration on embedded platforms, particularly with respect ... » read more

Moving Intelligence Closer to the Sensor Edge (IBM Research)


A researcher from IBM Research - Europe published “Emerging Trends in Intelligent Sensing”. Abstract “The rapid proliferation of artificial intelligence, connected devices, and high speed mobile networks is driving unprecedented computational demands that challenge traditional sensor architectures. This article explores the shift toward edge computing, where computation is perfor... » read more

Flexible AI-MCU For Fast Inference of Transformer Models At The Ultra-Low-Power Edge (ETH Zurich, U. Bologna)


Researchers from ETH Zurich and University of Bologna have released “CHIMERA: A Flexible and Scalable 3.1 TOPS/W AI-MCU with Transformer Accelerator and 563 Gb/s Shared-L2 Memory Subsystem with QoS Guarantees”. Abstract “We present Chimera, a flexible and scalable Microcontroller Unit (MCU) designed to accelerate real-time inference of rapidly evolving transformer-based models a... » read more

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