In this article, we propose an efficient statistical parameter extraction method to accurately model the random device mismatch of MOSFETs. The key idea is to approximate the performance variations ...as mathematical functions of device mismatch. Based on these approximated functions and the electrical test data, we solve the unknown statistical parameters by nonlinear optimization. Our numerical experiments demonstrate that the proposed method can remarkably improve the modeling accuracy with affordable computational cost, compared against the state-of-the-art techniques.
The reliability of circuits is significantly affected by process variations in manufacturing and environmental variation during operation. Current yield optimization algorithms take process ...variations into consideration to improve circuit reliability. However, the influence of environmental variations (e.g., voltage and temperature variations) is often ignored in current methods because of the high computational cost. In this article, a novel and efficient approach named BNN-BYO is proposed to optimize the yield of analog circuits in multiple environmental corners. First, we use a Bayesian Neural Network (BNN) to simultaneously model the yields and performances of interest in multiple corners efficiently. Next, the multi-corner yield optimization can be performed by embedding BNN into a Bayesian optimization framework. Since the correlation among yields and performances of interest in different corners is implicitly encoded in the BNN model, it provides great modeling capabilities for yields and their uncertainties to improve the efficiency of yield optimization. Our experimental results demonstrate that the proposed method can save up to 45.3% of simulation cost compared to other baseline methods to achieve the same target yield. In addition, for the same simulation cost, our proposed method can find better design points with 3.2% yield improvement.
Due to the significant process variations, designers have to optimize the statistical performance distribution of nano-scale IC design in most cases. This problem has been investigated for decades ...under the formulation of stochastic optimization, which minimizes the expected value of a performance metric while assuming that the distribution of process variation is exactly given. This paper rethinks the variation-aware circuit design optimization from a new perspective. First, we discuss the variation shift problem, which means that the actual density function of process variations almost always differs from the given model and is often unknown. Consequently, we propose to formulate the variation-aware circuit design optimization as a distributionally robust optimization problem, which does not require the exact distribution of process variations. By selecting an appropriate uncertainty set for the probability density function of process variations, we solve the shift-aware circuit optimization problem using distributionally robust Bayesian optimization. This method is validated with both a photonic IC and an electronics IC. Our optimized circuits show excellent robustness against variation shifts: the optimized circuit has excellent performance under many possible distributions of process variations that differ from the given statistical model. This work has the potential to enable a new research direction and inspire subsequent research at different levels of the EDA flow under the setting of variation shift.
Accurate prediction of battery lifetime is critical for ensuring timely maintenance and safety of batteries. Although data-driven methods have made significant progress, their model accuracy is often ...hampered by a scarcity of labeled data. To address this challenge, we developed a semi-supervised learning technique named partial Bayesian co-training (PBCT), enhancing the modeling of battery lifetime prediction. Leveraging the low-cost unlabeled data, our model extracts hidden information to improve the understanding of the underlying data patterns and achieve higher lifetime prediction accuracy. PBCT outperforms existing approaches by up to 21.9% on lifetime prediction accuracy, with negligible overhead for data acquisition. Moreover, our research suggests that incorporating unlabeled data into the training process can help to uncover critical factors that impact battery lifetime, which may be overlooked with a limited number of labeled data alone. The proposed semi-supervised approach sheds light on the future direction for efficient and explainable data-driven battery status estimation.
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•Battery lifetime is predicted using limited data through semi-supervised learning•The proposed method performs superior in both accuracy and interpretability•Economic costs are significantly reduced by lessening the need for labeled data
Data-driven methods have been extensively utilized for battery lifetime prediction to achieve high accuracy using only early-cycle battery testing data. Despite the offered advances, they are usually limited by the shortage of data, as labeled data are often expensive to obtain owing to the high costs of testing a battery to its end of life. To tackle this challenge, this work proposes an innovative semi-supervised learning-based solution, which incorporates external information from massive unlabeled data to facilitate the training of a prediction model. While semi-supervised learning can substantially improve prediction accuracy, we found that it can also help with understanding the electrochemical principles of battery degradation by precisely recognizing the features with physical importance. Most importantly, the proposed method can significantly reduce the economic costs by achieving equivalent accuracy using less labeled data compared with fully supervised methods.
An innovative semi-supervised machine learning method is proposed in this work to tackle the challenge of data shortage for battery lifetime prediction. By leveraging low-cost unlabeled data, the proposed method reveals the underlying data patterns of battery capacity degradation, thereby achieving both superior prediction accuracy and interpretability. Furthermore, the proposed method achieves grand economic value by substantially reducing the costs of battery testing, leading to significant importance in the R&D of rechargeable batteries.
Due to the significant process variations, designers have to optimize the statistical performance distribution of nano-scale IC design in most cases. This problem has been investigated for decades ...under the formulation of stochastic optimization, which minimizes the expected value of a performance metric while assuming that the distribution of process variation is exactly given. This paper rethinks the variation-aware circuit design optimization from a new perspective. First, we discuss the variation shift problem, which means that the actual density function of process variations almost always differs from the given model and is often unknown. Consequently, we propose to formulate the variation-aware circuit design optimization as a distributionally robust optimization problem, which does not require the exact distribution of process variations. By selecting an appropriate uncertainty set for the probability density function of process variations, we solve the shift-aware circuit optimization problem using distributionally robust Bayesian optimization. This method is validated with both a photonic IC and an electronics IC. Our optimized circuits show excellent robustness against variation shifts: the optimized circuit has excellent performance under many possible distributions of process variations that differ from the given statistical model. This work has the potential to enable a new research direction and inspire subsequent research at different levels of the EDA flow under the setting of variation shift.
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•China’s urban green space loss in 2003–2015 was equivalent to losing an area of 46.4 soccer fields per day.•Every 1 million growth of urban residents accompanied a decline of ...50.9 ± 3.33 m2 green space per capita.•The cooling of green space increased while the area of cooling space decreased.•Urban residents locating outside the cooling space increased by 4.23 × 106 people per year.
Urban green spaces (UGSs) reduce the surrounding temperature and create cooling areas as a buffer between people and high temperatures, thus helping residents adapt to the warming climate. However, the accessibility of UGS cooling services to the residents of cities remains largely unknown, which hinders decision-making regarding the formulation of climate adaptation and urban greening schemes. In the present study, we estimated the number of residents who accessed UGSs for cooling by analyzing the annual changes in such cooling areas during summer across 315 Chinese cities from 2003 to 2015. Approximately 93.3% of the cities showed significant decreasing trends (p < 0.05) of the total UGS area; as such the UGS coverage dropped from 12.23 ± 0.32% in 2003 to 7.69 ± 0.22% in 2015. Consequently, with the prevalent loss of UGS, the coverage of cooling spaces decreased from 32.55 ± 0.76% in 2003 to 24.39 ± 0.60% in 2015. This has formed a spatial mismatch between the growing urban population and the remaining UGSs. Accordingly, the number of residents of areas outside these cooling spaces increased by 4.23 million per year. In particular, the shortage of cooling services was more significant in cities with < 20,000 USD gross domestic product per capita and < 5 million residents than in the rest of the cities. To minimize the adverse impacts of increasing temperatures, focused greening plans are warranted, specifically in underdeveloped cities.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Metals cannot exhibit ferroelectricity because static internal electric fields are screened by conduction electrons, but in 1965, Anderson and Blount predicted the possibility of a ferroelectric ...metal, in which a ferroelectric-like structural transition occurs in the metallic state. Up to now, no clear example of such a material has been identified. Here we report on a centrosymmetric (R3c) to non-centrosymmetric (R3c) transition in metallic LiOsO3 that is structurally equivalent to the ferroelectric transition of LiNbO3 (ref. 3). The transition involves a continuous shift in the mean position of Li(+) ions on cooling below 140 K. Its discovery realizes the scenario described in ref. 2, and establishes a new class of materials whose properties may differ from those of normal metals.
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IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Urban parks are an important part of urban ecosystems and play an important role in biodiversity conservation. However, it is still unclear how park characteristics affect plant diversity which could ...hinder the conservation of urban biodiversity due to the ineffective design of urban parks. The diversity of woody plants in 33 parks of Changchun, China, was measured with plot measurements and linked with urban park characteristics (e.g., size and age of the park) to uncover the relationship between them. The results show that urban woody plant species were abundant, with 98 species belonging to 51 genera and 26 families in the snow climate city of Changchun. The variation in woody plant diversity was largely explained by internal patch characteristics (e.g., size, age, shape), and external factors surrounding the park (e.g., land use type and socioeconomic level) accounted for only 16.0% in our study. For internal patch characteristics, older urban parks with larger areas demonstrated a richer level of plant diversity and increased nonlinearly with increasing park area. The threshold size significantly affecting plant diversity variation was approximately 30 ha. Plant diversity had positive linear relationships with the ages of urban parks, supporting the legacy effect. In addition, woody plant diversity nonlinearly decreased with increasing park shape index, which suggested that the plant diversity could also be increased by optimizing the park shape. Regarding the external factors surrounding parks, the spatial distribution of woody plant diversity varied greatly from the urban center to the suburbs. House prices around the park had positive linear relationships with woody plant diversity in parks, supporting the luxury effect. However, building and road proportions, and green space proportion had no relationship with plant diversity in parks. This study can provide a robust reference for enriching plant diversity in urban parks, thus improving the development of urban sustainable cities.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Surface urban heat islands (SUHIs) have received worldwide attention owing to their significant adverse impacts on socio-economic and environmental conditions. However, how the SUHIs vary across ...cities of different sizes and whether there is a threshold for city size that affect SUHIs were not explicit due to the limited selection of large cities in previous studies. Our study attempts to support comprehensive decision-making for national sustainable spatial management of SUHI in urbanized areas of different sizes. We used impervious surface area data to define the urbanized boundary for obtaining the SUHI in China. Then we analyzed the spatio-temporal patterns of SUHIs under China's rapid urbanization. We furtherly constructed spatial autoregressive and structural equation models to verify the drivers' direct and indirect contributions to SUHI. The SUHI effects existed extensively in most urbanized areas, not only in large or mega urbanized areas (average value: 2.08 ± 0.68°C) but also in petty urbanized areas (average value: 0.64 ± 0.19°C). We also found that the size of urbanized areas contributed the most to the increase in SUHI intensity (SUHII). Natural drivers are mediators indirectly contributing to variations in SUHII, influenced by anthropogenic drivers. In addition, we also found that the size of urbanized areas contributed the most to the increase in SUHI intensity (SUHII). The size of the urbanized areas had a positive non-linear relationship with SUHIIs. When the size was larger than 400 km
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, the growth of SUHIIs maintained an equilibrium state. This study highlights the importance of impervious surface expansion for increasing SUHII. To confront SUHIs, it is necessary to perform proper urban planning.
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