Hotspot detection using squish-net

Yang, Haoyu; Pathak, Piyush; Gennari, Frank; Lai, Yuen-Liang; Yu, Bei

doi:10.1117/12.2515172

Cited by 6 publications

(10 citation statements)

References 9 publications

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“…Unlike, other scan-lines bit map representations [9][10], GPS topological and dimensional features are directly mapped to geometrical measurements within the pattern with respect to the POI. The topological features set refers to categorical quantized data that can have finite-state values and represents topological states of the pattern, such as the corner count, edges count, corner type, etc., which is referred to here as the Topological Signature of the pattern.…”

Section: Dimensional and Topological Featuresmentioning

confidence: 99%

IC layouts patterns topological profiling using directional geometrical kernels

Hamed

El-Sewefy²,

Khalaf³

et al. 2023

DTCO and Computational Patterning II

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As the semiconductor manufacturing technology node scales down in the deep submicron domain, hotspot detection becomes more challenging and geo-contextually dependent than ever before. The need to profile IC layout patterns based on geometrical commonalities becomes a significant demand either during IC layout design or manufacturing phases. Identified hotspots during the manufacturing phase are usually correlated to specific geometrical configurations sensitive to the lithography process or other manufacturing processes. Accordingly, identifying similar geometrical configurations is an important step toward locating potential hotspots. Once these hotspots are identified, their patterns can be provided to the router to avoid using these patterns and find other valid alternatives. Furthermore, in the IC design sign-off phase or Design Rules Check (DRC), layout profiling can identify patterns with high commonalities to these problematic patterns that potentially lead to a yield loss. In this paper, we introduce automated IC layout patterns topological profiling approach using Directional Geometrical Kernels (DGKs) to capture the context of patterns around a Point-Of-Interest (POI) in an IC layout, such as a hotspot. The DGKs pattern representation provides a direct one-to-one mapping with physical geometrical measurements centered by the POI and doesn’t need further feature extraction models or maps used by other pixelized gridded imagebased or density-based representations, which are both time and computational resources-consuming. The DGKs are decomposed into topological and dimensional components. This makes the mechanism of patterns topological profiling not in need of complex models and can be precisely fine controlled to produce adequate patterns profiling granularity that is not easily approached by other patterns profiling alternatives.

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Section: Dimensional and Topological Featuresmentioning

confidence: 99%

IC layouts patterns topological profiling using directional geometrical kernels

Hamed

El-Sewefy²,

Khalaf³

et al. 2023

DTCO and Computational Patterning II

View full text Add to dashboard Cite

show abstract

“…Yang et al 18 used the adaptive squish as a layout patterns representation with a CNN architected model containing a significant portion of architecture dedicated for features extraction, and in Ref. 19 used a more tailored, deeper CNN architecture named "SquishNet." Table 2 shows a summary of ML applications that use layout extracted features during the IC manufacturing phase, mainly the detection of litho HS, OPC, SRAF placement, and litho simulation steps.…”

Section: Applications For Ic Layouts During Ic Manufacturing Phasementioning

confidence: 99%

“…Similarly, aerial image features 17 and PFT features 22 need optical simulations to be calculated, which may be suitable for ML-OPC and litho HS detection applications where features calculation is bounded by the downstream application and embedded within the overall flow. Adaptive-squish-pattern 18,19 is an adaptive-grid bitmap representation for a layout pattern that provides a compact lossless representation of layout patterns. However, it does not provide direct one-to-one mapping with geometrical features, but requires a deep-learning feature extraction through CNN models as presented in Refs.…”

Section: Ic Layouts Feature Representation For ML Applicationsmentioning

confidence: 99%

Geometrical positioning surveying-based features for BEOL line-end-pull-back modeling using regression-based machine-learning

Hamed

Hegazy²,

El-Sewefy³

et al. 2023

J. Micro/Nanopattern. Mats. Metro.

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“…This has increased challenges in IC back-end design and sign-off flows. Lithography induces defects due to phenomena such as diffraction, resulting in lithographic hotspots [38,50,51].…”

Section: Deep Learning For Hotspot Detectionmentioning

confidence: 99%

“…In contrast, machine learning solutions seek to capture the underlying physics of lithographic simulation (i.e., the relationships between IC layout features and their manufacturability) and, as such, generalize to unseen patterns (or at least that has been the hope). Recent advancements on CNN based hotspot detection [50,51] have shown that both shallow and deep CNNs are more accurate compared to legacy machine learning based and pattern matching based techniques.…”

Section: Deepmentioning

confidence: 99%

Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection

Liu,

Yang,

et al. 2019

Preprint

Self Cite

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There is substantial interest in the use of machine learning (ML) based techniques throughout the electronic computer-aided design (CAD) flow, particularly those based on deep learning. However, while deep learning methods have surpassed state-of-the-art performance in several applications, they have exhibited intrinsic susceptibility to adversarial perturbations -small but deliberate alterations to the input of a neural network, precipitating incorrect predictions. In this paper, we seek to investigate whether adversarial perturbations pose risks to ML-based CAD tools, and if so, how these risks can be mitigated. To this end, we use a motivating case study of lithographic hotspot detection, for which convolutional neural networks (CNN) have shown great promise. In this context, we show the first adversarial perturbation attacks on state-of-the-art CNN-based hotspot detectors; specifically, we show that small (on average 0.5% modified area), functionality preserving and design-constraint satisfying changes to a layout can nonetheless trick a CNN-based hotspot detector into predicting the modified layout as hotspot free (with up to 99.7% success). We propose an adversarial retraining strategy to improve the robustness of CNN-based hotspot detection and show that this strategy significantly improves robustness (by a factor of ~3) against adversarial attacks without compromising classification accuracy. CCSConcepts: • Machine learning → Neural networks; • Hardware → Physical design (EDA); Best practices for EDA; • Security and privacy;

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Hotspot detection using squish-net

Cited by 6 publications

References 9 publications

IC layouts patterns topological profiling using directional geometrical kernels

IC layouts patterns topological profiling using directional geometrical kernels

Geometrical positioning surveying-based features for BEOL line-end-pull-back modeling using regression-based machine-learning

Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection

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