Differentiable Edge-based OPC
- URL: http://arxiv.org/abs/2408.08969v3
- Date: Fri, 30 Aug 2024 02:35:32 GMT
- Title: Differentiable Edge-based OPC
- Authors: Guojin Chen, Haoyu Yang, Haoxing Ren, Bei Yu, David Z. Pan,
- Abstract summary: DiffOPC is a differentiable OPC framework that enjoys the virtue of both edge-based OPC and ILT.
Our approach achieves lower edge placement error while reducing manufacturing cost by half compared to state-of-the-art OPC techniques.
- Score: 13.779806155253043
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Optical proximity correction (OPC) is crucial for pushing the boundaries of semiconductor manufacturing and enabling the continued scaling of integrated circuits. While pixel-based OPC, termed as inverse lithography technology (ILT), has gained research interest due to its flexibility and precision. Its complexity and intricate features can lead to challenges in mask writing, increased defects, and higher costs, hence hindering widespread industrial adoption. In this paper, we propose DiffOPC, a differentiable OPC framework that enjoys the virtue of both edge-based OPC and ILT. By employing a mask rule-aware gradient-based optimization approach, DiffOPC efficiently guides mask edge segment movement during mask optimization, minimizing wafer error by propagating true gradients from the cost function back to the mask edges. Our approach achieves lower edge placement error while reducing manufacturing cost by half compared to state-of-the-art OPC techniques, bridging the gap between the high accuracy of pixel-based OPC and the practicality required for industrial adoption, thus offering a promising solution for advanced semiconductor manufacturing.
Related papers
- Joint Transmit and Pinching Beamforming for PASS: Optimization-Based or Learning-Based? [89.05848771674773]
A novel antenna system ()-enabled downlink multi-user multiple-input single-output (MISO) framework is proposed.
It consists of multiple waveguides, which equip numerous low-cost antennas, named (PAs)
The positions of PAs can be reconfigured to both spanning large-scale path and space.
arXiv Detail & Related papers (2025-02-12T18:54:10Z) - Fast inverse lithography based on a model-driven block stacking convolutional neural network [10.170465557304897]
This paper presents a novel inverse lithographic approach to Optical Proximity Correction (OPC)
It employs a model-driven, block stacking deep learning framework that expedites the generation of masks conducive to manufacturing.
Numerical experiments have substantiated the efficacy of the proposed end-to-end approach.
arXiv Detail & Related papers (2024-12-19T07:42:07Z) - Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing [53.77822620185878]
We propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs.
We develop "BayesMulti", a training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections.
Our integrated approach enables use of analog computing in much deeper and wider networks, achieving up to 100-fold improvements.
arXiv Detail & Related papers (2024-12-03T19:20:08Z) - GPU-Accelerated Inverse Lithography Towards High Quality Curvy Mask Generation [5.373749225521622]
Inverse Lithography Technology (ILT) has emerged as a promising solution for photo mask design and optimization.
We introduce a GPU-accelerated ILT algorithm that improves contour quality and process window.
arXiv Detail & Related papers (2024-11-11T19:10:58Z) - ILILT: Implicit Learning of Inverse Lithography Technologies [5.373749225521622]
We propose an implicit learning ILT: ILILT, which leverages the implicit learning inputs to ground-conditioned ILT solutions, significantly improving efficiency and quality.
arXiv Detail & Related papers (2024-05-06T15:49:46Z) - Inverse Lithography Physics-informed Deep Neural Level Set for Mask
Optimization [0.8547032097715571]
Level set-based inverse lithography technology (ILT) has drawn considerable attention as a promising OPC solution.
Deep learning (DL) methods have shown great potential in accelerating ILT.
We propose an inverse lithography physics-informed deep neural level set (ILDLS) approach for mask optimization.
arXiv Detail & Related papers (2023-08-15T01:56:22Z) - Adaptive Window Pruning for Efficient Local Motion Deblurring [81.35217764881048]
Local motion blur commonly occurs in real-world photography due to the mixing between moving objects and stationary backgrounds during exposure.
Existing image deblurring methods predominantly focus on global deblurring.
This paper aims to adaptively and efficiently restore high-resolution locally blurred images.
arXiv Detail & Related papers (2023-06-25T15:24:00Z) - DevelSet: Deep Neural Level Set for Instant Mask Optimization [11.847061281805463]
inverse lithography technique (ILT) has drawn significant attention and is becoming prevalent in emerging OPC solutions.
In this paper, we present DevelSet, a GPU and deep neural network (DNN) accelerated level set OPC framework for metal layer.
arXiv Detail & Related papers (2023-03-18T13:48:53Z) - Generating Superpixels for High-resolution Images with Decoupled Patch
Calibration [82.21559299694555]
Patch Networks (PCNet) is designed to efficiently and accurately implement high-resolution superpixel segmentation.
DPC takes a local patch from the high-resolution images and dynamically generates a binary mask to impose the network to focus on region boundaries.
In particular, DPC takes a local patch from the high-resolution images and dynamically generates a binary mask to impose the network to focus on region boundaries.
arXiv Detail & Related papers (2021-08-19T10:33:05Z) - Universal and Flexible Optical Aberration Correction Using Deep-Prior
Based Deconvolution [51.274657266928315]
We propose a PSF aware plug-and-play deep network, which takes the aberrant image and PSF map as input and produces the latent high quality version via incorporating lens-specific deep priors.
Specifically, we pre-train a base model from a set of diverse lenses and then adapt it to a given lens by quickly refining the parameters.
arXiv Detail & Related papers (2021-04-07T12:00:38Z) - Face Anti-Spoofing by Learning Polarization Cues in a Real-World
Scenario [50.36920272392624]
Face anti-spoofing is the key to preventing security breaches in biometric recognition applications.
Deep learning method using RGB and infrared images demands a large amount of training data for new attacks.
We present a face anti-spoofing method in a real-world scenario by automatic learning the physical characteristics in polarization images of a real face.
arXiv Detail & Related papers (2020-03-18T03:04:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.