The As-grown-Generation (AG) model: A reliable model for reliability prediction under real use conditions

Modeling the negative bias temperature instability (NBTI) can optimize circuit design. Several models have been proposed and all of them can fit test data well. These models are extracted typically by fitting short accelerated stress data. Their capability to predict NBTI aging outside the test range has not been fully demonstrated. This predictive capability for long term aging under low operation bias is what needed by circuit designers. In this work, we test the predictive capability of the well-known reaction-diffusion (RD) based framework for samples fabricated by a variety of processes. Results show that the RD model cannot make an acceptable generic prediction. The recently proposed As-grown-Generation (AG) model is then introduced. By dividing defects into two groups, as-grown and generated defects, and measuring the as-grown defects experimentally, we demonstrate that it can make reliable prediction for the same set of data where the RD model failed

Nanoimaging of Organic Charge Retention Effects: Implications for Nonvolatile Memory, Neuromorphic Computing, and High Dielectric Breakdown Devices

While a large variety of organic and molecular materials have been found to exhibit charge memory effects, the underlying mechanism is not well-understood, which hinders rational device design. Here, we study the charge retention mechanism of a nanoscale memory system, an organic monolayer on a silicon substrate, with Au nanoparticles on top serving as the electrical contact. Combining scanning probe imaging/manipulation and density functional simulations, we observe stable charge retention effects in the system and attributed it to polaron effects at the amine functional groups. Our findings can pave the way for applications in nonvolatile memory, neuromorphic computing, and high dielectric breakdown devices

A single device based Voltage Step Stress (VSS) Technique for fast reliability screening

A new wafer-level reliability qualification methodology is proposed. Unlike conventional method which usually takes days to completion, the total test time of the new technique can be shortened within 2 hours. Besides, it only requires a single device. This new technique is easy to implement on commercial equipment and it has been successfully validated on different processes including the most advanced 28nm process with both SiON and high-k gate stacks. This new technique can be an effective tool for fast reliability screening during process development in future

Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data by including all shifted versions of a training sample. However, the underlying DCF formulation is restricted to single-resolution feature maps, significantly limiting its potential. In this paper, we go beyond the conventional DCF framework and introduce a novel formulation for training continuous convolution filters. We employ an implicit interpolation model to pose the learning problem in the continuous spatial domain. Our proposed formulation enables efficient integration of multi-resolution deep feature maps, leading to superior results on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color (+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate). Additionally, our approach is capable of sub-pixel localization, crucial for the task of accurate feature point tracking. We also demonstrate the effectiveness of our learning formulation in extensive feature point tracking experiments. Code and supplementary material are available at http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html.Comment: Accepted at ECCV 201

Author Correction: Fermiology and electron dynamics of trilayer nickelate La4Ni3O10.

The original version of this Article contained errors in Fig. 2, Fig. 3a-c and Supplementary Fig. 2. In Fig. 2g and Supplementary Fig. 2, the band structure plot calculated from density function theory (DFT) had a missing band of mainly z2 character that starts at about - 0.25 eV at the Y point and disperses downwards towards the Γ point. This band was inadvertently neglected when transferring the lines from the original band plot to the enhanced version for publication. Also in Fig. 2g, the points labelled M and Y were not exactly at (1/2 1/2 0) and (0 1/2 0), but rather (0.52 0.48 0) and (0 0.48 0) due to a bug in XCrysDen for low-symmetry structures that the authors failed to identify before publication. Thus, the bands presented were slightly off the true M-Y direction and additional splitting incorrectly appeared (in particular for the highly dispersive bands of x2-y2 character). The correct versions of Fig. 2g (cited as Fig. 1) and Supplementary Fig. 2 (cited as Fig. 2) are:which replaces the previous incorrect version, cited here as Fig. 3 and Fig. 4:Neither of these errors in Fig. 2g or Supplementary Fig. 2 affects either the discussion or any of the interpretations of the ARPES data provided in the paper. The authors discussed the multilayer band splitting along the Γ-M direction (δ band and α band as assigned in the paper), and ARPES did not see any split band. The authors did not discuss the further splitting that arises due to back folding along the M-Y direction.In Fig. 3a-c, the errors in the M and Y points in Fig. 2g cause subtle changes to the DFT dispersions. The correct version of Fig. 3a-c is cited here as Fig 5:which replaces the previous incorrect version (Fig. 6):However, the influence on the effective mass results of Fig. 3d is negligible.These errors have now been corrected in both the PDF and HTML versions of the Article. The authors acknowledge James Rondinelli and Danilo Puggioni from Northwestern University for calling our attention to these issues

Hot carrier aging of nano-scale devices: characterization method, statistical variation, and their impact on use voltage

Hot carrier aging (HCA) has attracted a lot of attentions recently, as it can be a lifetime limiting mechanism for both I/O and core devices. The applicability of the conventional characterization method developed for large devices to nano-scale devices is questionable, as nano-scale devices suffers from within-a-device-fluctuation (WDF). This work shows that the inclusion of WDF measured by the commercial quasi-DC SMU gives erroneous results. A method is proposed to separate the WDF from the real HCA for reliable parameter extraction of the HCA model. The lifetime and use voltage become yield dependent and the impact of statistical variations on SRAM is assessed

DEFECTS AND LIFETIME PREDICTION FOR GE PMOSFETS UNDER AC NBTI STRESSES

Germanium has higher hole mobility and is a candidate for replacing silicon for pMOSFETs. This work reviews the recent progresses in understanding the negative bias temperature instability (NBTI) of Ge pMOSFETs and compares it with SiON/Si devices. Both Ge and SiON/Si devices have two groups of defects: as-grown hole traps (AHT) and generated defects (GDs). The generation process, however, is different: GDs are interface-controlled for SiON/Si and dielectric-controlled for Ge devices. This leads to substantially higher GDs under DC stress than under AC stress for Ge, although they are similar for SiON/Si devices. Moreover, GDs alter their energy levels with charge status and can be reset to original precursor states after neutralization for Ge, but these processes are insignificant for SiON/Si. The impact of these differences on lifetime prediction will be presented and the defects and physical mechanism will be explored

NBTI prediction and its induced time dependent variation

Negative bias temperature instability (NBTI) prediction relies on a reliable extraction of power exponents from its kinetics. When measured by fast pulse technique, however, the kinetics does not follow a power law. This paper reviews the recent progresses on how to restore the power law, based on the As-grown-Generation (AG) model. For nanometer sized devices, NBTI is different for different devices, inducing a time-dependent variation. The new technique proposed for characterizing this Time-dependent Variation accounting for within-a-device-Fluctuation (TVF) will be reviewed

DEFECTS FOR RANDOM TELEGRAPH NOISE AND NEGATIVE BIAS TEMPERATURE INSTABILITY

Random Telegraphy Noise (RTN) and Negative Bias Temperature Instability (NBTI) are two important sources of device instability. Their relation is not fully understood and is investigated in this work. We examine the similarity and differences of the defects responsible for them. By following the As-grown-Generation (AG) model proposed by our group, we present clear evidences that the As-grown hole traps (AHTs) are responsible for the RTN of pMOSFETs. AHTs also dominate NBTI initially, but the generated defects (GDs) become increasingly important for NBTI as stress time increases. The GDs, however, do not cause RTN

Assessing the Accuracy of Statistical Properties Extracted from a Limited Number of Device Under Test for Time Dependent Variations

As device sizes scale down, device variations scale up. There are two types of device-to-device variations (DDV): as-fabricated or time-zero DDV and the time dependent variations (TDV). Even if two nano-scaled devices were identical at time-zero, they would be different after stresses and result in TDV, since the defect generation and charging-discharging are stochastic. To characterize TDV, statistical properties, such as the mean value and standard deviation, are extracted from tests. Their accuracy improves as the number of device under tests (DUTs) increases. Ageing is time consuming and the typical DUTs used are in the range of tens to hundreds. There is little information on the accuracy of the statistical properties extracted from such a limited DUTs and the objective of this paper is to propose a methodology to assess it. Based on the defect-centric model, the accuracy with a specific confidence level is evaluated for a given number of DUTs and a stress level