The inverted specific-class distance measure (ISCDM) is a popular distance metric that uses conditional probability term to calculate the distance between two nominal attribute values, but the reliability of the conditional probability term is limited by the attribute independence assumption, which leads to the suboptimal performance in applications involving sophisticated attribute dependencies. To obtain more accurate conditional probability estimation, in this study, we derive an enhanced ISCDM by leveraging structure extension to alleviate the unrealistic attribute assumption. We denominate the resulting model as the hidden inverted specific-class distance measure (HISCDM). In HISCDM, the structure extension scheme of hidden naive bayes is adopted to find the weighted dependence relationships between attributes, and then is incorporated into the conditional probability estimation. The comprehensive experimental results demonstrate that our proposed HISCDM significantly outperforms all other methods used for comparison in terms of classification accuracy.

The mesoscale eddy structure in the upper ocean is difficult to observe with altimeter satellite data due to their coarse spatiotemporal resolution. However, the world’s first Geostationary Ocean Color Imager (GOCI) makes it possible to refine the eddy structure inversion with observations of high spatiotemporal resolution (500 m and 1 h, respectively). In this study, we proposed a new flow field retrieval algorithm recurrent all-pairs field transforms in mesoscale eddy flow retrieval (RAFT-MesoEddy) based on deep current optical flow architecture in the mesoscale eddy surface flow field for GOCI data. The validation results showed that the flow field matched the particle image velocimetry (PIV) data in both quality and spatial distribution, with mean end-to-end point error (MEPE) and mean average angle error (MAAE) of less than 0.07 pixel and 2.57°, respectively. The GOCI-derived flow field showed good consistency with drifting buoy trajectory data, with MEPE and MAAE values less than 0.34 m/s and 18.78°, respectively. The GOCI-retrieved flow field showed agreement with satellite altimeter geostrophic flow data, with the MEPE and MAAE being 0.28 m/s and 10.12°, respectively. The GOCI-retrieved flow field showed an agreement with Ocean Surface Current Analyses Real-time (OSCAR) surface flow field, with the MEPE and MAAE are 0.28 m/s and 9.62°, respectively. In addition, a specific mesoscale eddy to discuss the hourly dynamics of flow field and eddy kinetic energy (EKE) is taken. The significant diurnal dynamics revealed in the hourly GOCI observations suggest caution in mapping mesoscale eddy flow using conventional altimeter satellite data, as the temporal resolution is insufficient to capture diurnal variations.

Atmospheric correction (AC) of satellite-measured polarized radiances is crucial due to the intricate radiative transfer processes within the atmospheric-ocean system and the diverse applications of polarimetric ocean color remote sensing. This study presents the intelligent polarization AC (IPAC) algorithm that efficiently handles multiangle, multispectral, and polarimetric satellite observations to derive polarized water-leaving reflectance, as well as aerosol properties (coarse-mode and fine-mode) and water inherent optical properties in open-ocean waters. To develop the IPAC algorithm, we simulated top-of-atmosphere (TOA) vector apparent reflectances over the open ocean using a vector radiative transfer simulation (VRTS) model. These simulations employed statistical results from satellite Level-3 products and precalculated lookup tables of polarization water-leaving radiance transmittances. Generating approximately 60 million TOA vector apparent reflectances and 89 inversion submodels facilitated the construction of the IPAC model. Performance assessment of IPAC included three wavelengths (490, 670, and 865 nm), three polarization states, and 13 solar-sensor geometries for each satellite pixel. Validation analysis revealed that the IPAC algorithm significantly improved the accuracy of retrieved ocean color products compared to standard AC algorithms. The mean absolute percentage error in the retrieved polarized apparent water-leaving reflectance and chlorophyll concentration relative to the measurement data was below 34.43% and 37.66%, respectively. Overall, the IPAC model proves its capability to deal with multiangle, multispectral, and polarimetric satellite data and advance the current ocean color work for many applications.

Multiangle polarization measurements in the near-infrared band from space were illustrated as suitable for the inversion of aerosol vertical distribution (AVD) information. In this study, we reported the interference of the AVD to linearly polarized radiances (${\rho }_{Q}$and${\rho }_{U}$) and scalar radiance (${\rho }_{I}$) under different simulation configurations, and the sensitivity discrepancies of${\rho }_{I}$,${\rho }_{Q}$, and${\rho }_{U}$to the aerosol scale height were analyzed by theoretical calculation of Mie scattering theory. Furthermore, the high correlation between polarization measurements in the near-infrared band and AVD inspired to perform polarization measurement inversion of the aerosol layer height (ALH). The constructed model was based on nonlinear optimization and the total-absorption ocean assumption. By fitting the linearly polarized radiances obtained by polarization observations in the near-infrared band, the AVD information were retrieved. The evaluation indicators showed that the root mean square error (RMSE) of the retrievals is less than 1 km for three typical sea areas, which demonstrates that polarization inversion is a valuable addition to light dectection and ranging (Lidar) data for AVD measurement.

The adjacency effect (AE) caused by the surrounding land cannot be ignored for satellite remote sensing of coastal and inland waters, especially for rivers with a narrow width. However, there is currently a lack of comprehensive understanding of AE for rivers, much less the precise correction methods for these effects. Here, a 3-D vector radiative transfer model for a 3-D radiative transfer model for a nonuniform underlying surface (NUS-MC) was developed. The ability of the NUS-MC to accurately simulate AEs in the case of Rayleigh scattering was validated by comparing its results with those from existing models for both uniform and nonuniform underlying surface cases. The mean absolute percentage deviations (MAPDs) for the simulated radiance are within 0.14% for a uniform water underlying and within 0.34% for a uniform land underlying surface. Based on the NUS-MC, AEs on Rayleigh scattering radiance for river waters under various conditions were systematically quantified. The results show that$\rho _{\mathrm {AE}}$decreases with increasing solar zenith angle (SZA) but increases with increasing view zenith angle (VZA). Even for a large river, 10 km across, at a wavelength of 443 nm, the mean${\rho }_{\mathrm {AE}}$at the river center is 3.97% at an SZA of 0° when the land albedo is 0.1, increasing to 18.19% and 33.37% at land albedos of 0.3 and 0.5, respectively. Overall, AEs significantly affect Rayleigh scattering even in rivers with widths of up to 10 km. In addition, the angle between the river channel and the observation plane as well as the distance from the riverbank to the point of view in the river can also affect the AEs. These findings suggest that the impact of land AEs on Rayleigh scattering must be thoroughly considered to retrieve water-leaving radiance in rivers accurately, and it is essential to develop atmospheric correction methods capable of removing AEs.

Mariculture is an important offshore economic activity, and excessive farming can lead to the deterioration of sea ecology. The concentration of nutrients (mainly DIN (dissolved inorganic nitrogen) and PO4 (orthophosphate-phosphorous)) is the main factor characterizing the health condition of farmed seas. Conventional field monitoring methods are spatiotemporally limited, and remote sensing technology has the advantages of high spatial coverage and long time series monitoring. Thus, the Sentinel-3 reflectance data and the in situ measured data for the offshore waters of Wenzhou were matched simultaneously. Then, the matched dataset between the Sentinel-2 band and the in situ measured data were obtained through spectral correspondence conversion between Sentinel-2 and Sentinel-3, and a machine learning algorithm was used to build the inversion model with an independent validation process. The correlations between the concentration of nutrients, area of rafts and precipitation were assessed, and a strong positive correlation was found between the concentration of nutrients and the area of rafts, and a weak negative correlation was found between the former and precipitation.

L-band passive microwave remote sensing of ocean surfaces is hampered by uncertainties due to the contribution of precipitation. However, modeling and correcting rain effect are complicated because all the precipitation contributions from the atmosphere and sea surface, including the rain effect in the atmosphere, water refreshing, rain-perturbed sea surface, and rain-induced local wind, are coupled. These rain effects significantly alter L-band microwave satellite measurements. This study investigates the impact of precipitation on the satellite measured brightness temperature (TB) and proposes a correction method. The results show that the rain-induced TB increase is approximately 0.5–1.4 K in the atmosphere, depending on the incidence angle and polarization. Moreover, the rain effect on sea surface emissions is more significant than that in the atmosphere. The result shows that rain effects on sea surface emissions are higher than 3 K for both polarizations when the rain rate is higher than 20 mm/h. We validate the rain effect correction model based on Soil Moisture Active Passive (SMAP) observations. The results show that the TBs at the top of the atmosphere (TOA) simulated by the model are in a good agreement with the SMAP observations, with root-mean-square errors (RMSEs) of 1.137 and 1.519 K for the horizontal and vertical polarizations, respectively, indicating a relatively high accuracy of the established model. Then, a correction model is applied to sea surface salinity (SSS) retrieval for analysis, and the results show that the developed model corrects the underestimation in SSS retrieval. Finally, the rain effect correction model is validated with Aquarius observations in three regions, and it is found that the RMSEs of the corrected TOA TBs range from 1.013 to 1.608 K, which is higher than those without rain effect correction (RMSEs range from 2.078 to 3.894 K). Overall, the model developed in this study provides relatively a good accuracy for rain effect correction.

In this letter, we propose a downscaling approach to improve the spatial resolution of sea surface salinity (SSS) in estuarine areas using combined microwave and ocean color data. The model established a relationship between SSS and normalized sea surface emissivity and colored dissolved organic matter (CDOM). The model was validated byin situmeasurements conducted in the East China Sea (ECS) and Mississippi River Estuary (MRE). The model showed relatively good agreement within situSSS measurements and illustrated enhanced SSS at high (4 km) resolution compared with low (40 km) resolution, with root mean square errors (RMSEs) of 1.55 versus 2.58 psu in the ECS and 0.39 versus 1.49 psu in the MRE. Overall, the proposed downscaling approach enhances the spatial resolution and accuracy of satellite SSS observations over estuarine areas, which should be helpful for ocean dynamic and biogeochemical studies in these regions.

Satellite ocean color products have been widely used to monitor spatiotemporal variations in marine ecological environments from regional to global oceans. However, current satellite ocean color products fail to provide effective records during the winter in high–latitude oceans, limiting understanding of the marine ecological environment during the winter season. In this study, we proposed an atmospheric correction model, namely, the Neural Network Atmospheric Correction algorithm for the Southern Hemisphere (NN–AC–SH), and recover winter satellite ocean color products for the high–latitude oceans of the Southern Hemisphere from 2003 to 2020. The accuracy of the NN–AC–SH model was verified based on the in situ data from the Aerosol Robotic Network–Ocean Color (AERONET–OC). The results indicate that the NN–AC–SH model performed better than the traditional near–infrared (NIR) iterative atmospheric correction algorithm, e.g., in the 443, 488 and 531 nm bands, the relative deviations of the NN–AC–SH model were 23.11%, 20.96% and 23.14%, respectively, while the values of the NIR model were 30.72%, 22.85%, and 24.81%, respectively. Under the observation condition of a high solar zenith angle (SZA), the NN–AC–SH model performed better than the NIR model in terms of the amount of effective data (94 vs. 78 data points) and model inversion accuracy (a relative deviation 32.83% vs. 43.60%). Moreover, in situ chlorophyll concentration data from the NASA bio–Optical Marine Algorithm Dataset (NOMAD) and Chinese Antarctic Research Expedition (CHINARE) were used to verify the accuracy of the restored chlorophyll concentration products, and the results reveal that the NN–AC–SH model resulted in more effective records of higher accuracy than those obtained with the NASA–distributed chlorophyll concentration products. Overall, for the first time, this study recovered long time–series (2003–2020) ocean color products for the high–latitude oceans of the Southern Hemisphere (≥50°S) in the winter, which could provide unique satellite products for investigating the marine ecological environment of the Antarctic and sub–Antarctic oceans in the winter.

The vertical distribution of absorbing aerosols has nonnegligible impact on the atmospheric correction of satellite ocean color remote sensing, especially for the water-leaving radiance retrieval at blue and ultraviolet bands. In this study, we investigated the impact of the vertical distribution of absorbing aerosols on the satellite-measured radiance at the top of the atmosphere (TOA) and the retrieved water-leaving radiance. First, the global occurrence frequency of absorbing aerosols was mapped, and it was found that the annual averaged occurrence frequencies of absorbing aerosols were >30% over the coasts of the Sahara and Arabian Desert, China, South-Central Africa, and the Indian Peninsula. Second, a new aerosol classification algorithm was developed to establish absorbing aerosol optical models based on AERosol RObotic NETwork (AERONET) site observations. Finally, the effects of the vertical distribution of absorbing aerosols on the upward radiance at the TOA at 412 nm and the retrieved water-leaving radiance were evaluated. The results showed that the influence of the vertical distribution on the TOA radiance could be up to 8% for dust and 10% for fine-dominated absorbing aerosols (FDAs), which was comparable with the influence of aerosol optical depth. Not considering absorbing aerosols during atmospheric correction might produce$\sim 4$%–10% errors in the water-leaving radiance retrieval at 412 nm over turbid waters under the assumption of a Gaussian distribution. Simplified exponential and two-layer atmospheric vertical distribution models can lead to errors of water-leaving radiance retrieval up to$\sim 12$%–15% and$\sim 30$%–40%, respectively. Overall, the imperfect vertical distribution assumption and aerosol model selection might induce uncertainty in water-leaving radiance retrieval from 10% to 80% under absorbing aerosol conditions.

Sea surface salinity (SSS) retrieval from satellite-based microwave radiometer is hampered by uncertainties due to atmospheric and surface scattering contributions. This study presents a comprehensive vector radiative transfer model (VRTM) for estimation of brightness temperature (TB) (for SSS retrieval) from L-band radiometry based on a matrix-operator method. It includes an efficient two-scale model (TSM) that combines a geometrical optics (GO) model and a small-perturbation model (SPM) for computing both the small- and large-scale scattering components of the sea surface. Moreover, it considers the influence of rain effects on TB by including the radiation extinction term (scattering and attenuation). The simulation results using the VRTM were validated with those obtained from the RT4 model for flat sea surface conditions and the RTTOV model for rough sea surface conditions. The relative difference in the estimated TB among the models was small (<; 1%) for low wind speeds (<; 1 m/s) and increased up to 3% for high wind speeds and observation angles. Simulations on the influence of wind speed on TB with various parameterizations were further examined. Compared with SMOS-MIRAS and Aquarius measurements, the VRTM simulations agreed well with satellite measurements for both vertically and horizontally polarized TBs with biases of less than 2.2 K for observation angles from 20° to 65°. The binned TBs showed even better results, with a standard deviation of less than 1.45 K and an absolute mean error of less than 1.2 K.

Scores of machine learning algorithms depend on a good distance measure to achieve high performance. The inverted specific-class distance measure, simply ISCDM, is proposed to find reasonable distance measure between each pair of instances with nominal attributes only. ISCDM does not depend on the attribute value of the training instance, which makes it less sensitive to missing values in the training set and more robust to non-class attribute noise. However, in ISCDM, all attributes are assumed to be fully independent. It is obvious that the attribute independence assumption in ISCDM is rarely true in reality, which would harm its performance in the applications with complex attribute dependencies. In this paper, we single out an improved inverted specific-class distance measure by relaxing its unrealistic attribute independence assumption. We call it averaged one-dependence inverted specific-class distance measure, simply AODISCDM. We experimentally tested it on 29 classification problems from the University of California at Irvine (UCI) repository and found that it significantly outperforms ISCDM in terms of the negative conditional log likelihood (-CLL) and the root relative squared error (RRSE). Besides, the proposed AODISCDM maintains the computational simplicity (no search involved) and robustness that characterise ISCDM.

New generation ocean color imagers on geostationary orbits are designed to provide a much higher temporal resolution along with enhanced spatial and spectral resolutions that will open up obvious opportunities for improving the sampling frequency and resolving diurnal variability of phytoplankton and other biogeochemical properties in dynamic coastal waters. Despite the capabilities of such new generation sensors to detect the diurnal cycles of various ocean phenomena, there is a lack of knowledge on their radiometric sensitivity and signal detectability for observing the ocean color at morning or evening hours. This paper aims to explore the capability of geostationary satellite ocean color sensor for detecting ocean biogeochemical properties [chlorophyll (CHL); total suspended matter (TSM); colored dissolved organic matter (CDOM)] under high solar zenith angles (SZAs). The analysis is based upon simulations from the vector radiative transfer model for the coupled ocean-atmosphere system (PCOART-SA), which considers the earth curvature effects. The unitless differential signal-to-noise ratio (ASNR) is used as a discriminant parameter to indicate the radiometric sensitivity to variation of different biogeochemical properties. The results showed that the SZAs have a significant impact on the signal detectability for CHL variation. For typical shelf water (CHL = 1 μg/L, TSM = 1 mg/L, CDOM = 0.15 m-1), with the typical observation zenith angle (OZA) = 30°, changes on theorder of ΔCHL = 0.024 μg/L (2.4% to background CHL) were detectable when SZA = 30°; when SZA > 75°, the detectable minimal ΔCHL increased to 0.77 μg/L (77%), indicating the difficulty of detecting CHL under high SZA. For CDOM, the detectability of changes (ΔCDOM) was also found to be closely related to the SZAs, i.e., changes on the order of ten times depending on the SZA conditions. However, even under extremely high SZA conditions (SZA = 80°, OZA = 30°), ACDOM = 0.007 m-1which is about 4.7% of the background CDOM was still detectable at 412 nm. On the other hand, under high SZA conditions (SZA = 80°, OZA = 30°), ΔTSM = 0.211 mg/L (2.1% to the background TSM) was also detectable. Overall, our results indicate that under high SZAs conditions, the geostationary satellite ocean color sensor may experience difficulty in detecting a slight change in CHL variation in productive waters, but it still can detect small changes in TSM and CDOM contents despite a reduced sensitivity at the steeper SZAs.

High-precision measurement of sea surface temperature (SST) requires an accurate knowledge of sea surface emissivity (SSE). Many studies have found that the SST estimations in the coastal areas are less accurate than that in open seas, where water turbidity is negligible. Previous works regard SSE as a function of surface roughness and observation angle; however, works have rarely focused on the internal characteristic of water, such as its turbidity. Thus, this paper presents thermal infrared measurements of the emissivity of turbid water carried out under controlled conditions with a multichannel radiometer working in the 8-14-μm region. The results showed that measured emissivity values decreased with the increase of water turbidity. The decrease was tiny for lower suspended particulate matter (SPM) concentrations but a significance emissivity decrease with higher concentrations, especially at large observation angles. For instance, the difference between concentrations of 0 and 5000 mg/L manifested an average emissivity variation of 2.93% with 5000 mg/L at a viewing angle of 55°, depending on the radiometer spectral channels. And, a parametric relationship of emissivity in terms of SPM concentration was established in this paper. The impact of ignoring water turbidity on SST, using SST algorithms and a case of satellite retrievals, was analyzed. It was indicated that there would be an SST error lower than 0.2 °C at SPM concentrations less than 1000 mg/L and that SST deviations would reach values up to almost 0.5 °C-0.6 °C at 5000 mg/L, even higher than 1 °C at large angles for a given atmospheric water vapor content less than 4 g/cm2.

Adverse drug reaction (ADR) is a common clinical problem, sometimes accompanying with high risk of mortality and morbidity. It is also one of the major factors that lead to failure in new drug development. Unfortunately, most of current experimental and computational methods are unable to evaluate clinical safety of drug candidates in early drug discovery stage due to the very limited knowledge of molecular mechanisms underlying ADRs. Therefore, in this study, we proposed a novel na€ıve Bayesian model for rapid assessment of clinical ADRs with frequency estimation. This model was constructed on a gene-ADR association network, which covered 611 US FDA approved drugs, 14,251 genes, and 1,254 distinct ADR terms. An average detection rate of 99.86 and 99.73 percent were achieved eventually in identification of known ADRs in internal test data set and external case analyses respectively. Moreover, a comparative analysis between the estimated frequencies of ADRs and their observed frequencies was undertaken. It is observed that these two frequencies have the similar distribution trend. These results suggest that the naıve Bayesian model based on gene-ADR association network can serve as an efficient and economic tool in rapid ADRs assessment.

Memory circuits have become important components in today's IC designs which demands extremely high integration density and reliability under process variations. The most challenging task is how to accurately estimate the extremely small failure probability of memory circuits where the circuit failure is a “rare event”. Classic importance sampling has been widely recognized to be inaccurate and unreliable in high dimensions. To address this issue, we propose a fast statistical analysis to estimate the probability of rare events in high dimensions and prove that the estimation is always bounded. This methodology has been successfully applied to the failure analysis of memory circuits with hundreds of variables, which was considered to be very intractable before. To the best of our knowledge, this is the first work that successfully solves high dimensional “rare event” problems without using expensive Monte Carlo and classic importance sampling methods. Experiments on a 54-dimensional SRAM cell circuit show that the proposed approach achieves 1150x speedup over Monte Carlo without compromising any accuracy. It also outperforms the classification based method (e.g., Statistical Blockade) by 204x and existing importance sampling method (e.g., Spherical Sampling) by 5x. On another 117-dimension circuit, the proposed approach yields 364x speedup over Monte Carlo while existing importance sampling methods completely fail to provide reasonable accuracy.

It has become increasingly challenging to model the stochastic behavior of analog/mixed-signal (AMS) circuits under large-scale process variations. In this paper, a novel moment-matching-based method has been proposed to accurately extract the probabilistic behavioral distributions of AMS circuits. This method first utilizes Latin hypercube sampling coupling with a correlation control technique to generate a few samples (e.g., sample size is linear with number of variable parameters) and further analytically evaluate the high-order moments of the circuit behavior with high accuracy. In this way, the arbitrary probabilistic distributions of the circuit behavior can be extracted using moment-matching method. More importantly, the proposed method has been successfully applied to high-dimensional problems with linear complexity. The experiments demonstrate that the proposed method can provide up to 1666X speedup over crude Monte Carlo method for the same accuracy.

Importance sampling is a popular approach to estimate rare event failures of SRAM cells. We propose to improve importance sampling by probability collectives. First, we use "Kullback-Leibler (KL) distance" to measure the distance between the optimal sampling distribution and the original sampling distribution of variable process parameters. Further, the probability collectives (PC) technique using immediate sampling is adapted to analytically minimize the KL distance and to obtain a sampling distribution as close to the optimal as possible. The proposed algorithm significantly accelerates the convergence of importance sampling. Experiments demonstrate that proposed algorithm is 5200X faster than the Monte Carlo approach and achieves more than $40X$ speedup over other existing state-of-the-art techniques without compromising estimation accuracy.

3-D vision technologies are extensively used in a wide variety of applications. Particularly glasses-based 3-D technology facilities are increasingly becoming affordable to our daily lives. Considering health issues raised by 3-D video technologies, to the best of our knowledge, most of the pilot studies are practiced in a highly-controlled laboratory environment only. In this paper, we present NeuroGlasses, a nonintrusive wearable physiological signal monitoring system to facilitate health analysis and diagnosis of 3-D video watchers. The NeuroGlasses system acquires health-related signals by physiological sensors and provides feedbacks of health-related features. Moreover, the NeuroGlasses system employs signal-specific reconstruction and feature extraction to compensate the distortion of signals caused by variation of the placement of the sensors. We also propose a server-based NeuroGlasses infrastructure where physiological features can be extracted for real-time response or collected on the server side for long term analysis and diagnosis. Through an on-campus pilot study, the experimental results show that NeuroGlasses system can effectively provide physiological information for healthcare purpose. Furthermore, it approves that 3-D vision technology has a significant impact on the physiological signals, such as EEG, which potentially leads to neural diseases.

Performance failure has become a significant threat to the reliability and robustness of analog circuits. In this article, we first develop an efficient non-Monte-Carlo (NMC) transient mismatch analysis, where transient response is represented by stochastic orthogonal polynomial (SOP) expansion under PVT variations and probabilistic distribution of transient response is solved. We further define performance yield and derive stochastic sensitivity for yield within the framework of SOP, and finally develop a gradient-based multiobjective optimization to improve yield while satisfying other performance constraints. Extensive experiments show that compared to Monte Carlo-based yield estimation, our NMC method achieves up to 700 X speedup and maintains 98% accuracy. Furthermore, multiobjective optimization not only improves yield by up to 95.3% with performance constraints, it also provides better efficiency than other existing methods.

Process variation in nanometer technology is becoming an important issue for cutting-edge FPGAs with a multimillion gate capacity. Considering both die-to-die and within-die variations in effective channel length, threshold voltage, and gate oxide thickness, we first develop closed-form models of chip-level FPGA leakage and timing variations. Experiments show that the mean and standard deviation computed by our models are within 3% from those computed by Monte Carlo simulation. We also observe that the leakage and timing variations can be up to 3X and 1.9X, respectively. We then derive analytical yield models considering both leakage and timing variations, and use such models to evaluate the performance of FPGA device and architecture considering process variations. Compared to the baseline, which uses the VPR architecture and device setup based on the ITRS roadmap, device and architecture tuning improves leakage yield by 10.4%, timing yield by 5.7%, and leakage and timing combined yield by 9.4%. We also observe that LUT size of 4 gives the highest leakage yield, LUT size of 7 gives the highest timing yield, but LUT size of 5 achieves the maximum leakage and timing combined yield. To the best of our knowledge, this is the first in-depth study on FPGA architecture and device coevaluation considering process variation.

The most challenging problem in the current block-based statistical static timing analysis (SSTA) is how to handle the max operation efficiently and accurately. Existing SSTA techniques suffer from limited modeling capability by using a linear delay model with Gaussian distribution, or have scalability problems due to expensive operations involved to handle non-Gaussian variation sources or nonlinear delays. To overcome these limitations, we propose efficient algorithms to handle the max operation in SSTA with both quadratic delay dependency and non-Gaussian variation sources simultaneously. Based on such algorithms, we develop an SSTA flow with quadratic delay model and non-Gaussian variation sources. All the atomic operations, max and add, are calculated efficiently via either closed-form formulas or low dimension (at most 2-D) lookup tables. We prove that the complexity of our algorithm is linear in both variation sources and circuit sizes, hence our algorithm scales well for large designs. Compared to Monte Carlo simulation for non-Gaussian variation sources and nonlinear delay models, our approach predicts the mean, standard deviation and 95% percentile point with less than 2% error, and the skewness with less than 10% error.

This paper presents a parallel and incremental solver for stochastic capacitance extraction. The random geometrical variation is described by stochastic geometrical moments, which lead to a densely augmented system equation. To efficiently extract the capacitance and solve the system equation, a parallel fast-multipole-method (FMM) is developed in the framework of stochastic geometrical moments. This can efficiently estimate the stochastic potential interaction and its matrix-vector product (MVP) with charge. Moreover, a generalized minimal residual (GMRES) method with incremental update is developed to calculate both the nominal value and the variance. Our overall extraction show is called piCAP. A number of experiments show that piCAP efficiently handles a large-scale on-chip capacitance extraction with variations. Specifically, a parallel MVP in piCAP is up 3 × to faster than a serial MVP, and an incremental GMRES in piCAP is up to 15× faster than non-incremental GMRES methods.

Stochastic device noise has become a significant challenge for high-precision analog/RF circuits, and it is particularly difficult to correctly include both white noise and flicker noise in the traditional transient verification with an efficient numerical solution. In this paper, a Non-Monte-Carlo transient noise analysis is developed. Both white noise and flicker noise are considered in Itô integral based stochastic differential algebraic equation (SDAE), which is solved by one-time calculation of variance using stochastic orthogonal polynomials (SoPs). Our work is the first in literature to provide the SoP-based SDAE solution with application for transient noise analysis. Experiments on a number of different analog circuits demonstrate that the proposed method is up to 488X faster than Monte Carlo method with similar accuracy, and achieves on average 6.8X speedup over the existing non-Monte-Carlo approaches.

Stochastic device parameter variations have dramatically increased beyond the scale of 65nm and can significantly lead to large mismatch for analog circuits. To estimate unknown analog circuit behavior in performance space under the given stochastic variations in parameter space, many state-of-art approaches have been developed recently. However, either Gaussian distribution or response surface model (RSM) with analytical formulae has to be assumed when connecting performance space and parameter space. A novel point-estimation based approach has been proposed in this paper to capture arbitrary stochastic distributions for analog circuit behaviors in performance space. First, to evaluate high-order moments of circuit behavior in an accurate fashion, the point-estimation method has been applied with only a few number of simulations. Then, probability density function (PDF) of circuit behavior can be efficiently extracted by the obtained high-order moments. This method is further extended for multiple parameters under linear complexity. Extensive numerical experiments on a number of different circuits have demonstrated that the proposed point-estimation method can provide up to 181X runtime speedup with the same accuracy, when compared with Monte Carlo method. Moreover, it can further achieve up to 15X speedup over the RSM-based method such as APEX with the similar accuracy.

With technology scaling down to 90nm and below, many yield-driven design and optimization methodologies have been proposed to cope with the prominent process variation and to increase the yield. A critical issue that affects the efficiency of those methods is to estimate the yield when given design parameters under variations. Existing methods either use Monte Carlo method in performance domain where thousands of simulations are required, or use local search in parameter domain where a number of simulations are required to characterize the point on the yield boundary defined by performance constraints. To improve efficiency, in this paper we propose QuickYield, a yield surface boundary determination by surface-point finding and global-search. Experiments on a number of different circuits show that for the same accuracy, QuickYield is up to 519X faster compared with the Monte Carlo approach, and up to 4.7X faster compared with YENSS, the fastest approach reported in literature.

It is unknown how to include stochastic process variation into fast-multipole-method (FMM) for a full chip capacitance extraction. This paper presents a parallel FMM extraction using stochastic polynomial expanded geometrical moments. It utilizes multi-processors to evaluate in parallel for the stochastic potential interaction and its matrix-vector product (MVP) with charge. Moreover, a generalized minimal residual (GMRES) method with deflation is modified to incrementally consider the nominal value and the variance. The overall extraction flow is called piCAP. Experiments show that the parallel MVP in piCAP is up to 3X faster than the serial MVP, and the incremental GMRES in pi-CAP is up to 15X faster than non-incremental GMRES methods.

In this paper, we describe the algorithms implemented in MBEM, a program for wideband impedance extraction of complicated 3-D structures. MBEM is based on a mixed boundary element method (BEM), which reduces the number of unknowns from about 7N in FastImp to 4N, for MQS analysis. Efficient techniques are proposed to handle the extra matrix multiplication, form post-process matrices, and solve the final linear equation system. The inaccuracy of calculation using FastImp at low frequency is also analyzed, which shows the mixed BEM eliminates it completely. Experiments on several typical 3-D structures validate the advantage of MBEM over FastImp, on both accuracy and efficiency.