分类： 物理学 >> 核物理学 提交时间： 2023-12-13
摘要： Simulating the total ionizing dose (TID) of an electrical system using transistor-level models can be difficult and expensive, particularly for digital integrated circuits (ICs). In this study, a method for modeling TID effects in complementary metal-oxide semiconductor (CMOS) digital ICs based on the input/output buffer information specification (IBIS) was proposed. The digital IC was first divided into three parts based on its internal structure: the input buffer, output buffer, and functional area. Each of these three parts was separately modeled. Using the IBIS model, the transistor V-I characteristic curves of the buffers were processed, and the physical parameters were extracted and modeled using VHDL-AMS. In the functional area, logic functions were modeled in VHDL according to the data sheet. A golden digital IC model was developed by combining the input buffer, output buffer, and functional area models. Furthermore, the golden ratio was reconstructed based on TID experimental data, enabling the assessment of TID effects on the threshold voltage, carrier mobility, and time series of the digital IC. TID experiments were conducted using a CMOS noninverting multiplexer, NC7SZ157, and the results were compared with the simulation results, which showed that the relative errors were less than 2% at each dose point. This confirms the practicality and accuracy of the proposed modeling method. The TID effect model for digital ICs developed using this modeling technique includes both the logical function of the IC and changes in electrical properties and functional degradation impacted by TID, which has potential applications in the design of radiation-hardening tolerance in digital ICs.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
Ying Mei
Shoulin Wei
Feng Wang
Chen Wu
Rodrigo Tobar
Mohsim Shaikh
Hui Deng
Wei Dai
Bo Liang
Andreas Wicenec
摘要： The Square Kilometre Array (SKA) project is an international cooperation project to build the largest radio telescope worldwide. Data processing is one of the biggest challenges of building the SKA telescope. As a distributed execution framework, the Data Activated Liu Graph Engine (DALiuGE) was proposed to be one of the candidates for addressing the massive data of the SKA. DALiuGE has many distinctive features, but its actual ability to handle scientific data is still not evident. In this paper, we perform an objective evaluation of the usability of DALiuGE concerning the execution performance, developer workload, and implementation difficulty of porting the SAGECal to DALiuGE. The evaluation results showed that the DALiuGE enables fast integration of astronomical software, but there are significant differences in the efficiency of different parallel granularities. Even with the deep optimization of the program, there is still a gap between the current DALiuGE and the traditional MPI in execution performance. Therefore, we come to a preliminary conclusion that the DALiuGE has no performance advantage in batch processing of massive data, while it may be more suitable for application scenarios with more customized computational tasks, such as SKA science regional centers.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Galaxy morphology reflects structural properties which contribute to understand the formation and evolution of galaxies. Deep convolutional networks have proven to be very successful in learning hidden features that allow for unprecedented performance on galaxy morphological classification. Such networks mostly follow the supervised learning paradigm which requires sufficient labelled data for training. However, it is an expensive and complicated process of labeling for million galaxies, particularly for the forthcoming survey projects. In this paper, we present an approach based on contrastive learning with aim for learning galaxy morphological visual representation using only unlabeled data. Considering the properties of low semantic information and contour dominated of galaxy image, the feature extraction layer of the proposed method incorporates vision transformers and convolutional network to provide rich semantic representation via the fusion of the multi-hierarchy features. We train and test our method on 3 classifications of datasets from Galaxy Zoo 2 and SDSS-DR17, and 4 classifications from Galaxy Zoo DECaLS. The testing accuracy achieves 94.7%, 96.5% and 89.9% respectively. The experiment of cross validation demonstrates our model possesses transfer and generalization ability when applied to the new datasets. The code that reveals our proposed method and pretrained models are publicly available and can be easily adapted to new surveys.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Galaxy morphology reflects structural properties which contribute to understand the formation and evolution of galaxies. Deep convolutional networks have proven to be very successful in learning hidden features that allow for unprecedented performance on galaxy morphological classification. Such networks mostly follow the supervised learning paradigm which requires sufficient labelled data for training. However, it is an expensive and complicated process of labeling for million galaxies, particularly for the forthcoming survey projects. In this paper, we present an approach based on contrastive learning with aim for learning galaxy morphological visual representation using only unlabeled data. Considering the properties of low semantic information and contour dominated of galaxy image, the feature extraction layer of the proposed method incorporates vision transformers and convolutional network to provide rich semantic representation via the fusion of the multi-hierarchy features. We train and test our method on 3 classifications of datasets from Galaxy Zoo 2 and SDSS-DR17, and 4 classifications from Galaxy Zoo DECaLS. The testing accuracy achieves 94.7%, 96.5% and 89.9% respectively. The experiment of cross validation demonstrates our model possesses transfer and generalization ability when applied to the new datasets. The code that reveals our proposed method and pretrained models are publicly available and can be easily adapted to new surveys.
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