nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of low leakage and high speed deep submicron PMOSFETs submitted to hole injections

We have compared the worst-case of HC degradation between Low Leakage (LL) and High Speed (HS) p-channel MOSFET's which is found under Channel Hot-Hole (CHH) injections as the electron trapping is vanishing with the reduction of the gate-oxide thickness. We focus on the distinction between HH injection and the direct tunneling of holes which is measured even at low voltage in the state-of the art 2.1 nm thick gate-oxide MOSFET's. This latter effect has been included in the Charge-Pumping (CP) analysis showing no strong effect when using appropriate measurement conditions by I/sub CP/-V/sub GL/ (V/sub GH/) curves in contrast to the standard fixed amplitude technique. LL and HS devices have shown distinct behaviors under uniform and localized CHH injections related to the different contributions of the degraded regions. CHH damage shows that for a same interface trap generation LL devices exhibit a stronger effect on the transistor performances due to the defect above the channel while HS devices have a larger degradation in the gate-drain overlap region. This is explained by the location of the peak lateral field and damage extent between both devices.
Fast Factorization-Based Inference for Bayesian Harmonic Models

Harmonic sinusoidal models are a fundamental tool for audio signal analysis. Bayesian harmonic models guarantee a good resynthesis quality and allow joint use of learnt parameter priors and auditory motivated distortion measures. However inference algorithms based on Monte Carlo sampling are rather slow for realistic data. In this paper, we investigate fast inference algorithms based on approximate factorization of the joint posterior into a product of independent distributions on small subsets of parameters. We discuss the conditions under which these approximations hold true and evaluate their performance experimentally. We suggest how they could be used together with Monte Carlo algorithms for a faster sampling-based inference.
An acoustically-motivated spatial prior for under-determined reverberant source separation

We consider the task of under-determined reverberant audio source separation. We model the contribution of each source to all mixture channels in the time-frequency domain as a zero-mean Gaussian random vector with full-rank spatial covariance matrix. We introduce an inverse Wishart prior over the covariance matrices, whose mean is given by the theory of statistical room acoustics and whose variance is learned from training data. We then derive an Expectation-Maximization (EM) algorithm to estimate the model parameters in the Maximum A Posteriori (MAP) sense given prior knowledge about the microphone spacing and the source positions. This algorithm provides a principled solution to the well-known permutation problem and achieves better separation performance than other algorithms exploiting the same prior knowledge.
Discriminative uncertainty estimation for noise robust ASR

We consider the problem of uncertainty estimation for noiserobust ASR. Existing uncertainty estimation techniques improve ASR accuracy but they still exhibit a gap compared to the use of oracle uncertainty. This comes partly from the highly non-linear feature transformation and from additional assumptions such as Gaussian distribution and independence between frequency bins in the spectral domain. In this paper, we propose a method to rescale the estimated feature-domain full uncertainty covariance matrix in a statedependent fashion according to a discriminative criterion. The state-dependent and feature index-dependent scaling factors are learned from development data. Experimental evaluation on Track 1 of the 2nd CHiME challenge data shows that discriminative rescaling leads to better results than generative rescaling. Moreover, discriminative rescaling of the Wiener uncertainty estimator leads to 12% relative word error rate reduction compared to discriminative rescaling of the alternative estimator in .
A Stress Technique Suitable For The In-Line Reliability Monitoring Of The Hot Carrier Endurance of Sub-0,5um MOSFETs

The purpose of this paper is to present a stress mode which allows the hot carrier endurance of sub-half micron MOSFETs to be evaluated within 100s of DC stress. Contrary to the classical approach which requires substantially longer test times (i.e. about 10s of DC stress at the maximum substrate current), the proposed technique is suitable for in-line reliability monitoring applications.
Spatial covariance models for under-determined reverberant audio source separation

The separation of under-determined convolutive audio mixtures is generally addressed in the time-frequency domain where the sources exhibit little overlap. Most previous approaches rely on the approximation of the mixing process by complex-valued multiplication in each frequency bin. This is equivalent to assuming that the spatial covariance matrix of each source, that is the covariance of its contribution to all mixture channels, has rank 1. In this paper, we propose to represent each source via a full-rank spatial covariance matrix instead, which better approximates reverberation. We also investigate a possible parameterization of this matrix stemming from the theory of statistical room acoustics. We illustrate the potential of the proposed approach over a stereo reverberant speech mixture.
Comparison of Second Impact Ionization Phenomena Between 0.18um N- and P-channel MOSFET's

The consequence of the second impact ionization phenomena (2II) with the back-bias VB differs significantly between Nand PMOSFET’s where electron or hole emissions are favored in the ionization processes. In PMOS a direct hole gate-current is now observed like the electronic one, as a function of the lateral field due to the first ionization event (II) which are both strongly reduced with VB during 2II effects. This behavior is opposite to the NMOS one where the II and 2II electronic gate-current is easily enhanced at lower field towards the NMOS gate by the conjonction of the thermionicand tunneling emissions. These results are explained by the localization of the emission point under the gate and by the hole energy loss which reduces the hole gate-current.
Beyond the Narrowband Approximation: Wideband Convex Methods for Under-Determined Reverberant Audio Source Separation

We consider the problem of extracting the source signals from an under-determined convolutive mixture assuming known mixing filters. State-of-the-art methods operate in the time-frequency domain and rely on narrowband approximation of the convolutive mixing process by complex-valued multiplication in each frequency bin. The source signals are then estimated by minimizing either a mixture fitting cost or a ℓ1 source sparsity cost, under possible constraints on the number of active sources. In this paper, we define a wideband ℓ2 mixture fitting cost circumventing the above approximation and investigate the use of a ℓ1,2 mixed-norm cost promoting disjointness of the source time-frequency representations. We design a family of convex functionals combining these costs and derive suitable optimization algorithms. Experiments indicate that the proposed wideband methods result in a signal-to-distortion ratio improvement of 2 to 5 dB compared to the state-of-the-art on reverberant speech mixtures.
Robust modeling of musical chord sequences using probabilistic N-grams

The modeling of music as a language is a core issue for a wide range of applications such as polyphonic music retrieval, automatic style identification, audio to symbolic music transcription and computer-assisted composition. In this paper, we focus on the modeling of chord sequences by probabilistic N-grams. Previous studies using these models have achieved limited success, due to overfitting and to the use of a single chord labeling scheme. We investigate these issues using model smoothing and selection techniques initially designed for spoken language modeling. This approach is evaluated over a set of songs by The Beatles, considering several chord labeling schemes. Initial results show that the accuracy of N-grams is increased but that additional improvements may still be achieved in the future using more advanced, possibly music-specific, smoothing techniques.
Design-in-Reliability Approach for NBTI and Hot-Carrier Degradations in Advanced Nodes

A practical and accurate design-in-reliability methodology has been developed for designs on 90-65-nm technology nodes to quantitatively assess the degradation due to hot carrier and negative bias temperature instability. Simulation capability has been built on top of an existing analog simulator ELDO. Circuits are analyzed using this methodology, illustrating the capabilities of the methodology as well as highlighting the impacts of the two degradation modes.
A fundamental pitfall in blind deconvolution with sparse and shift-invariant priors

We consider the problem of blind sparse deconvolution, which is common in both image and signal processing. To counter-balance the ill-posedness of the problem, many approaches are based on the minimization of a cost function. A well-known issue is a tendency to converge to an undesirable trivial solution. Besides domain specific explanations (such as the nature of the spectrum of the blurring filter in image processing) a widespread intuition behind this phenomenon is related to scaling issues and the nonconvexity of the optimized cost function. We prove that a fundamental issue lies in fact in the intrinsic properties of the cost function itself: for a large family of shift-invariant cost functions promoting the sparsity of either the filter or the source, the only global minima are trivial. We complete the analysis with an empirical method to verify the existence of more useful local minima.
Musical source separation using time-frequency source priors

This article deals with the source separation problem for stereo musical mixtures using prior information about the sources (instrument names and localization). After a brief review of existing methods, we design a family of probabilistic mixture generative models combining modified positive independent subspace analysis (ISA), localization models, and segmental models (SM). We express source separation as a Bayesian estimation problem and we propose efficient resolution algorithms. The resulting separation methods rely on a variable number of cues including harmonicity, spectral envelope, azimuth, note duration, and monophony. We compare these methods on two synthetic mixtures with long reverberation. We show that they outperform methods exploiting spatial diversity only and that they are robust against approximate localization of the sources.
Convex Regularizations for the Simultaneous Recording of Room Impulse Responses

We propose to acquire large sets of room impulse responses (RIRs) by simultaneously playing known source signals on multiple loudspeakers. We then estimate the RIRs via a convex optimization algorithm using convex penalties promoting sparsity and/or exponential amplitude envelope. We validate this approach on real-world recordings. The proposed algorithm makes it possible to estimate the RIRs to a reasonable accuracy even when the number of recorded samples is smaller than the number of RIR samples to be estimated, thereby leading to a speedup of the recording process compared to state-of-the-art RIR acquisition techniques. Moreover, the penalty promoting both sparsity and exponential amplitude envelope provides the best results in terms of robustness to the choice of its parameters, thereby consolidating the evidence in favor of sparse regularization for RIR estimation. Finally, the impact of the choice of the emitted signals is analyzed and evaluated.
Stability analysis of multiplicative update algorithms for non-negative matrix factorization

Multiplicative update algorithms have encountered a great success to solve optimization problems with non-negativity constraints, such as the famous non-negative matrix factorization (NMF) and its many variants. However, despite several years of research on the topic, the understanding of their convergence properties is still to be improved. In this paper, we show that Lyapunov's stability theory provides a very enlightening viewpoint on the problem. We prove the stability of supervised NMF and study the more difficult case of unsupervised NMF. Numerical simulations illustrate those theoretical results, and the convergence speed of NMF multiplicative updates is analyzed.
Consistent Wiener Filtering for Audio Source Separation

Wiener filtering is one of the most ubiquitous tools in signal processing, in particular for signal denoising and source separation. In the context of audio, it is typically applied in the time-frequency domain by means of the short-time Fourier transform (STFT). Such processing does generally not take into account the relationship between STFT coefficients in different time-frequency bins due to the redundancy of the STFT, which we refer to as consistency. We propose to enforce this relationship in the design of the Wiener filter, either as a hard constraint or as a soft penalty. We derive two conjugate gradient algorithms for the computation of the filter coefficients and show improved audio source separation performance compared to the classical Wiener filter both in oracle and in blind conditions.
Instrument-Specific Harmonic Atoms for Mid-Level Music Representation

Several studies have pointed out the need for accurate mid-level representations of music signals for information retrieval and signal processing purposes. In this paper, we propose a new mid-level representation based on the decomposition of a signal into a small number of sound atoms or molecules bearing explicit musical instrument labels. Each atom is a sum of windowed harmonic sinusoidal partials whose relative amplitudes are specific to one instrument, and each molecule consists of several atoms from the same instrument spanning successive time windows. We design efficient algorithms to extract the most prominent atoms or molecules and investigate several applications of this representation, including polyphonic instrument recognition and music visualization.
Electric field and temperature acceleration of quasi-breakdown phenomena in ultrathin oxides

In this paper the quasi-breakdown phenomenon is studied for 3.5 nm ultrathin gate oxides. The appearance of this failure mode is investigated and the associated leakage current is characterized. Moreover the electric field and temperature dependence of the quasi-breakdown occurrence is analyzed: a novel procedure is proposed in order to determine the associated acceleration and to be able to predict the reliability with respect to this failure mode at nominal use conditions for deep-submicron technologies. It is shown that quasi-breakdown phenomenon is a major limiting factor for the ultrathin oxide reliability.
An extended experimental investigation of DNN uncertainty propagation for noise robust ASR

Automatic speech recognition (ASR) in noisy environments remains a challenging goal. Recently, the idea of estimating the uncertainty about the features obtained after speech enhancement and propagating it to dynamically adapt deep neural network (DNN) based acoustic models has raised some interest. However, the results in the literature were reported on simulated noisy datasets for a limited variety of uncertainty estimators. We found that they vary significantly in different conditions. Hence, the main contribution of this work is to assess DNN uncertainty decoding performance for different data conditions and different uncertainty estimation/propagation techniques. In addition, we propose a neural network based uncertainty estimator and compare it with other uncertainty estimators. We report detailed ASR results on the CHiME-2 and CHiME-3 datasets. We find that, on average, uncertainty propagation provides similar relative improvement on real and simulated data and that the proposed uncertainty estimator performs significantly better than the one in . We also find that the improvement is consistent, but it depends on the signal-to-noise ratio (SNR) and the noise environment.
Impact of the gate-stack change from 40nm node SiON to 28nm High-K Metal Gate on the Hot-Carrier and Bias Temperature damage

High-K Metal-Gate 28nm node (C28) with equivalent gate-oxide thickness EOT= 1.35nm has been compared to low power 40nm CMOS node (1.7nm) on silicon bulk. Hot-Carrier damage in C28 originates from the same permanent ΔNIT mechanism under current driven Multiple Particle (MP) interactions, relative to the SiON interface layer while border to bulk oxide traps make the larger difference between NMOS and PMOS transistors. This has been obtained by their respective temperature activation and AC response behaviors at Room Temperature and High Temperature due to the distinct proportion of accessible shallow/deep defects in the HK-MG structures.
Multichannel harmonic and percussive component separation by joint modeling of spatial and spectral continuity

This paper considers the blind separation of the harmonic and percussive components of multichannel music signals. We model the contribution of each source to all mixture channels in the time-frequency domain via a spatial covariance matrix, which encodes its spatial characteristics, and a scalar spectral variance, which represents its spectral structure. We then exploit the spatial continuity and the different spectral continuity structures of harmonic and percussive components as prior information to derive maximum a posteriori (MAP) estimates of the parameters using the expectation-maximization (EM) algorithm. Experimental results over professional musical mixtures show the effectiveness of the proposed approach.
On the oxide thickness extraction in deep-submicron technologies

Thickness measurement of ultrathin oxides is more and more difficult due to the oxide thickness scale down following the supply voltage decrease in deep-submicron technologies. This paper discusses the drawbacks of the already existing extraction methods and proposes an original approach which has various advantages. This extraction procedure, based on the plot 1/C vs 1/(V/sub G/-V/sub FB/) obtained by classical C-V measurements, can be easily implemented and provides more accurate oxide thickness results.
Hot carrier degradation in nanowire transistors: Physical mechanisms, width dependence and impact of Self-Heating

We present an extensive study of Hot Carrier reliability in N-Ωfet nanowires. For the first time 3 HC degradation modes were clearly evidenced as in planar technology and accurately modeled. Moreover HC reliability was proved to be width-independent. Finally it is shown that, although SH is important in nanowires, it has almost no implication on its HC reliability because of the weak temperature dependence of the HC mechanisms.
Long-term robot motion planning for active sound source localization with Monte Carlo tree search

We consider the problem of controlling a mobile robot in order to localize a sound source. A microphone array can provide the robot with information on source localization. By combining this information with the movements of the robot, the localization accuracy can be improved. However, random robot motion or short-term planning may not result in optimal localization. In this paper, we propose an optimal long-term robot motion planning algorithm for active source localization. We introduce a Monte Carlo tree search (MCTS) method to find a sequence of robot actions that minimize the entropy of the belief on the source location. A tree of possible robot movements which balances between exploration and exploitation is first constructed. Then, the movement that leads to minimum uncertainty is selected and executed. Experiments and statistical results show the effectiveness of our proposed method on improving sound source localization in the long term compared to other motion planning methods.
Improved method for the oxide thickness extraction in MOS structures with ultrathin gate dielectrics

An improved method for the assessment of the oxide thickness applicable to advanced CMOS technologies is proposed. To this end, a proper combination of Maserjian's technique and of Vincent's method is used to alleviate the unknown parameter inherent to both extraction procedures and which depends on the employed carrier statistics. The new method has been successfully applied to various technologies with gate oxide thickness ranging from 7 nm to 1.8 nm.
Novel hot-carrier AC-DC design guidelines for advanced CMOS nodes

The understanding of the relationship between circuit lifetime and device DC hot carrier (HC) stress lifetime is becoming increasingly important for advanced nodes since supply voltage (Vdd) and channel length (L) do not scale anymore in similar proportions. This paper proposes a novel approach to tackle HC risk assessment through a combination of refined transistor HC modeling, Wafer Level Reliability (WLR) & High Temperature Operating Lifetest (HTOL) experimental results and simulations.
Multipitch estimation by joint modeling of harmonic and transient sounds

Multipitch estimation techniques are widely used for music transcription and acquisition of musical data from digital signals. In this paper, we propose a flexible harmonic temporal timbre model to decompose the spectral energy of the signal in the time-frequency domain into individual pitched notes. Each note is modeled with a 2-dimensional Gaussian mixture. Unlike previous approaches, the proposed model is able to represent not only the harmonic partials but also the inharmonic attack of each note. We derive an Expectation-Maximization (EM) algorithm to estimate the parameters of this model and illustrate the higher performance of the proposed algorithm than NMF algorithm  and HTC algorithm  for the task of multipitch estimation over synthetic and real-world data.
PBTI mechanisms in La containing Hf-based oxides assessed by very Fast IV measurements

PBTI in La-doped HfSiON/TiN stacks is investigated using Ultra Fast IV measurements. Excellent PBTI lifetime of these oxides is demonstrated. We also show that PBTI is explained only by trapping in stress induced defects and not by trapping in pre-existing ones. Dependence on oxide field, temperature activation and recovery of PBTI are also investigated and modeled.
Statistically Robust Approach to Lens Distortion Calibration with Model Selection

This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. While almost all existing nonmetric distortion calibration methods need user involvement in one form or another,we present an approach to distortion calibration based on the robust the-least-median-of-squares (LMedS) estimator. Our approach is thus able to proceed in a ful ly-automatic manner while being less sensitive to erroneous input data such as image curves that are mistakenly considered as projections of 3D linear segments. Our approach uniquely uses fast, closed-form solutions to the distortion coefficients, which serve as an initial point for a non-linear optimization algorithm to straighten imaged lines. Moreover we propose a method for distortion model selection based on geometrical inference.Successful experiments to evaluate the performance of this approach on synthetic and real data are reported.