Scalable Photomask Optimization With Morphological Learning (SUNY Buffalo, VU, IBM)


A new technical paper, "MorphOPC: Advancing Mask Optimization with Multi-scale Hierarchical Morphological Learning," was published by researchers at University at Buffalo, Villanova University, and IBM T. J. Watson Research Center. Abstract "As feature sizes shrink to the nanometer scale, accurately transferring circuit patterns from photomasks to silicon wafers becomes increasingly chall... » read more

Faster And Better Floorplanning With ML-Based Macro Placement


The chips contained in today’s consumer and commercial electronic products are staggering in size and complexity. The largest devices include central processing units (CPUs), graphics processing units (GPUs), and system-on-chip (SoC) devices that integrate many functions on a single die. Additionally, chips are expanding beyond their traditional borders with multi-die approaches such as 2.5DI... » read more

Improving ML-Based Device Modeling Using Variational Autoencoder Techniques


A technical paper titled “Improving Semiconductor Device Modeling for Electronic Design Automation by Machine Learning Techniques” was published by researchers at Commonwealth Scientific and Industrial Research Organisation (CSIRO), Peking University, National University of Singapore, and University of New South Wales. Abstract: "The semiconductors industry benefits greatly from the integ... » read more