Poisson shot noise parameter estimation from a single scanning electron microscopy image

Kockentiedt, Stephen; Tönnies, Klaus D.; Gierke, Erhardt; Dziurowitz, Nico; Thim, Carmen; Plitzko, Sabine

doi:10.1117/12.2008374

Cited by 5 publications

(4 citation statements)

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“…A similar issue happens with the secondary emissions as well. This leads to noise in the beam interaction which can be modelled using either a compound Poisson process or a Gaussian-Poisson process (by approximation) [26]- [30]. In addition, the electronic components of the SEM (e.g., amplifiers and scan generators) induce an Additive White Gaussian Noise (AWGN) into the response but its influence was found to be negligible as compared to that of beam interaction noise [31], [32].…”

Section: Beam Interactionmentioning

confidence: 99%

“…There are approaches in literature that can address these issues. The shot noise parameter can be directly estimated from a single SEM image [26]. LASRE, an approach developed for segmenting SEM images, captures the pixel intensity distribution for the individual materials without the ground truth labels or layout images [63].…”

Section: B Impact Of Ad-hoc Processes On Hardware Assurancementioning

confidence: 99%

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REFICS: Assimilating Data-Driven Paradigms Into Reverse Engineering and Hardware Assurance on Integrated Circuits

Wilson¹,

Zhu

et al. 2021

IEEE Access

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Comprehensive hardware assurance approaches guaranteeing trust on Integrated Circuits (ICs) typically require the verification of the IC design layout and functionality through destructive Reverse Engineering (RE). It is a resource intensive process that will benefit greatly from the extensive integration of data-driven paradigms, especially in the imaging and image analysis phase. Although obvious, this uptake of data-driven approaches into RE-assisted hardware assurance is lagging due to the lack of massive amounts of high-quality labelled data. In this paper, a large-scale synthetic Scanning Electron Microscopy (SEM) dataset, REFICS, is introduced to address this issue. The dataset, the first open-source dataset in the RE community, consists of 800,000 SEM images over two node technologies, 32nm and 90nm, and four cardinal layers of the IC, namely, doping, polysilicon, contact and metal layers. Furthermore, a framework, based on uncertainty and risk, is introduced to compare the efficacy and benefits of existing RE workflows utilizing ad-hoc steps in its execution. These developments are critical in developing RE-assisted hardware assurance into a scalable, automated and fault-tolerant approach. Finally, the work is concluded with the performance analysis of existing machine learning and deep learning approaches for image analysis in RE and hardware assurance.

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Section: Beam Interactionmentioning

confidence: 99%

Section: B Impact Of Ad-hoc Processes On Hardware Assurancementioning

confidence: 99%

REFICS: Assimilating Data-Driven Paradigms Into Reverse Engineering and Hardware Assurance on Integrated Circuits

Wilson¹,

Zhu

et al. 2021

IEEE Access

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“…It means that parts of an image with higher intensity have a higher noise level than parts with low intensity. A low signal-tonoise ratio leads to a high level of noise, which affects detected edges and accuracy in measurement [9,10]. With the electron tomography technique, data acquisition is the detection of scattered electron particles.…”

Section: Introductionmentioning

confidence: 99%

Combined total variation of first and fractional orders for Poisson noise removal in digital images

Pham

Tran

Pham

et al. 2021

ICS

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Introduction: Many methods have been proposed to handle the image restoration problem with Poisson noise. A popular approach to Poissonian image reconstruction is the one based on Total Variation. This method can provide significantly sharp edges and visually fine images, but it results in piecewise-constant regions in the resulting images. Purpose: Developing an adaptive total variation-based model for the reconstruction of images contaminated by Poisson noise, and an algorithm for solving the optimization problem. Results: We proposed an effective way to restore images degraded by Poisson noise. Using the Bayesian framework, we proposed an adaptive model based on a combination of first-order total variation and fractional order total variation. The first-order total variation model is efficient for suppressing the noise and preserving the keen edges simultaneously. However, the first-order total variation method usually causes artifact problems in the obtained results. To avoid this drawback, we can use high-order total variation models, one of which is the fractional-order total variation-based model for image restoration. In the fractional-order total variation model, the derivatives have an order greater than or equal to one. It leads to the convenience of computation with a compact discrete form. However, methods based on the fractional-order total variation may cause image blurring. Thus, the proposed model incorporates the advantages of two total variation regularization models, having a significant effect on the edge-preserving image restoration. In order to solve the considered optimization problem, the Split Bregman method is used. Experimental results are provided, demonstrating the effectiveness of the proposed method. Practical relevance: The proposed method allows you to restore Poissonian images preserving their edges. The presented numerical simulation demonstrates the competitive performance of the model proposed for image reconstruction. Discussion: From the experimental results, we can see that the proposed algorithm is effective in suppressing noise and preserving the image edges. However, the weighted parameters in the proposed model were not automatically selected at each iteration of the proposed algorithm. This requires additional research.

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“…The fundamental a few ideas main FL are described in Foundations of Unclear Reasoning. FL is that of a linguistic variable, that's, a variable whose prices are phrases [6] as opposed to numbers. In influence, a lot of FL might be considered as a technique for research with phrases as opposed to numbers.…”

Section: Non Local Patch Regressionmentioning

confidence: 99%

Performance Analysis of Mixed Noise Removal from Images Using Data Mining

Kaur

Bawa²

2017

IJARCSSE

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I. INTRODUCTIONData mining programs analyze relationships and patterns in data based on which user's request. For instance, data mining software can be utilized to produce classes of information. To illustrate, imagine a cafe wants to make use of data mining to find out when they need to offer certain specials. It discusses the info it's collected and creates classes centered on when customers visit and what they order.In other cases, data miners find clusters of information centered on logical relationships, or they look at associations and sequential patterns to draw conclusions about trends in consumer behavior.The information mining method pauses into five steps. First, companies acquire information and fill it to their information warehouses. Next, they keep and control the info, both on in-house hosts or the cloud. Organization analysts, administration groups and data engineering specialists entry the info and establish how they wish to arrange it. Then, program application kinds the info on the basis of the user's benefits, and ultimately, the finish individual gifts the info within an easy-to-share structure, like a chart or table.

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Poisson shot noise parameter estimation from a single scanning electron microscopy image

Cited by 5 publications

References 0 publications

REFICS: Assimilating Data-Driven Paradigms Into Reverse Engineering and Hardware Assurance on Integrated Circuits

REFICS: Assimilating Data-Driven Paradigms Into Reverse Engineering and Hardware Assurance on Integrated Circuits

Combined total variation of first and fractional orders for Poisson noise removal in digital images

Performance Analysis of Mixed Noise Removal from Images Using Data Mining

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