GAN-OPC: Mask optimization with lithography-guided generative adversarial nets
Mask optimization has been a critical problem in the VLSI design flow due to the mismatch
between the lithography system and the continuously shrinking feature sizes. Optical …
between the lithography system and the continuously shrinking feature sizes. Optical …
AI/ML algorithms and applications in VLSI design and technology
An evident challenge ahead for the integrated circuit (IC) industry is the investigation and
development of methods to reduce the design complexity ensuing from growing process …
development of methods to reduce the design complexity ensuing from growing process …
Computational lithography using machine learning models
Y Shin - IPSJ Transactions on System and LSI Design …, 2021 - jstage.jst.go.jp
Machine learning models have been applied to a wide range of computational lithography
applications since around 2010. They provide higher modeling capability, so their …
applications since around 2010. They provide higher modeling capability, so their …
Fast inverse lithography based on dual-channel model-driven deep learning
X Ma, X Zheng, GR Arce - Optics Express, 2020 - opg.optica.org
Inverse lithography technology (ILT) is extensively used to compensate image distortion in
optical lithography systems by pre-warping the photomask at the pixel scale. However …
optical lithography systems by pre-warping the photomask at the pixel scale. However …
Establishing fast, practical, full-chip ILT flows using machine learning
T Cecil, K Braam, A Omran… - Optical …, 2020 - spiedigitallibrary.org
Since its introduction at Luminescent Technologies and continued development at
Synopsys, Inverse Lithography Technology (ILT) has delivered industry leading quality of …
Synopsys, Inverse Lithography Technology (ILT) has delivered industry leading quality of …
Machine learning-based edge placement error analysis and optimization: a systematic review
As the semiconductor manufacturing process is moving towards the 3 nm node, there is a
crucial need to reduce the edge placement error (EPE) to ensure proper functioning of the …
crucial need to reduce the edge placement error (EPE) to ensure proper functioning of the …
Neural network classifier-based OPC with imbalanced training data
Machine learning-guided optical proximity correction, called ML-OPC in this paper, has
recently been proposed to alleviate long runtime of model-based OPC. ML-OPC using …
recently been proposed to alleviate long runtime of model-based OPC. ML-OPC using …
Model-informed deep learning for computational lithography with partially coherent illumination
X Zheng, X Ma, Q Zhao, Y Pan, GR Arce - Optics Express, 2020 - opg.optica.org
Computational lithography is a key technique to optimize the imaging performance of optical
lithography systems. However, the large amount of calculation involved in computational …
lithography systems. However, the large amount of calculation involved in computational …
Machine learning (ML)-based lithography optimizations
Recent lithography optimizations demand higher accuracy and cause longer runtime.
Optical proximity correction (OPC) and sub-resolution assist feature (SRAF) insertion, for …
Optical proximity correction (OPC) and sub-resolution assist feature (SRAF) insertion, for …