Thermal Cycling Life Prediction of Sn-3.0Ag-0.5Cu Solder Joint Using Type-I Censored Data

Because solder joint interconnections are the weaknesses of microelectronic packaging, their reliability has great influence on the reliability of the entire packaging structure. Based on an accelerated life test the reliability assessment and life prediction of lead-free solder joints using Weibull distribution are investigated. The type-I interval censored lifetime data were collected from a thermal cycling test, which was implemented on microelectronic packaging with lead-free ball grid array (BGA) and fine-pitch ball grid array (FBGA) interconnection structures. The number of cycles to failure of lead-free solder joints is predicted by using a modified Engelmaier fatigue life model and a type-I censored data processing method. Then, the Pan model is employed to calculate the acceleration factor of this test. A comparison of life predictions between the proposed method and the ones calculated directly by Matlab and Minitab is conducted to demonstrate the practicability and effectiveness of the proposed method. At last, failure analysis and microstructure evolution of lead-free solders are carried out to provide useful guidance for the regular maintenance, replacement of substructure, and subsequent processing of electronic products

Diaqua­(5-methyl-1H-pyrazole-3-carboxyl­ato)(4-nitro­benzoato)copper(II)

In the title complex, [Cu(C7H4NO4)(C5H5N2O2)(H2O)2], the CuII ion is coordinated in a slightly distorted square-pyramidal enviroment. The basal plane is formed by an N atom and an O atom from a 5-methyl-1H-pyrazole-3-carboxyl­ate ligand and by two O atoms from two water ligands. The apical position is occupied by a carboxylate O atom from a 4-nitro­benzoate ligand. In the crystal structure, inter­molecular O—H⋯O and N—H⋯O hydrogen bonds link complex moleclues, forming extended chains parallel to the a axis

MetaFormer Is Actually What You Need for Vision

Transformers have shown great potential in computer vision tasks. A common belief is their attention-based token mixer module contributes most to their competence. However, recent works show the attention-based module in Transformers can be replaced by spatial MLPs and the resulted models still perform quite well. Based on this observation, we hypothesize that the general architecture of the Transformers, instead of the specific token mixer module, is more essential to the model's performance. To verify this, we deliberately replace the attention module in Transformers with an embarrassingly simple spatial pooling operator to conduct only basic token mixing. Surprisingly, we observe that the derived model, termed as PoolFormer, achieves competitive performance on multiple computer vision tasks. For example, on ImageNet-1K, PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned Vision Transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with 35%/52% fewer parameters and 50%/62% fewer MACs. The effectiveness of PoolFormer verifies our hypothesis and urges us to initiate the concept of "MetaFormer", a general architecture abstracted from Transformers without specifying the token mixer. Based on the extensive experiments, we argue that MetaFormer is the key player in achieving superior results for recent Transformer and MLP-like models on vision tasks. This work calls for more future research dedicated to improving MetaFormer instead of focusing on the token mixer modules. Additionally, our proposed PoolFormer could serve as a starting baseline for future MetaFormer architecture design. Code is available at https://github.com/sail-sg/poolformer.Comment: CVPR 2022 (Oral). Code: https://github.com/sail-sg/poolforme

MetaFormer Baselines for Vision

MetaFormer, the abstracted architecture of Transformer, has been found to play a significant role in achieving competitive performance. In this paper, we further explore the capacity of MetaFormer, again, without focusing on token mixer design: we introduce several baseline models under MetaFormer using the most basic or common mixers, and summarize our observations as follows: (1) MetaFormer ensures solid lower bound of performance. By merely adopting identity mapping as the token mixer, the MetaFormer model, termed IdentityFormer, achieves >80% accuracy on ImageNet-1K. (2) MetaFormer works well with arbitrary token mixers. When specifying the token mixer as even a random matrix to mix tokens, the resulting model RandFormer yields an accuracy of >81%, outperforming IdentityFormer. Rest assured of MetaFormer's results when new token mixers are adopted. (3) MetaFormer effortlessly offers state-of-the-art results. With just conventional token mixers dated back five years ago, the models instantiated from MetaFormer already beat state of the art. (a) ConvFormer outperforms ConvNeXt. Taking the common depthwise separable convolutions as the token mixer, the model termed ConvFormer, which can be regarded as pure CNNs, outperforms the strong CNN model ConvNeXt. (b) CAFormer sets new record on ImageNet-1K. By simply applying depthwise separable convolutions as token mixer in the bottom stages and vanilla self-attention in the top stages, the resulting model CAFormer sets a new record on ImageNet-1K: it achieves an accuracy of 85.5% at 224x224 resolution, under normal supervised training without external data or distillation. In our expedition to probe MetaFormer, we also find that a new activation, StarReLU, reduces 71% FLOPs of activation compared with GELU yet achieves better performance. We expect StarReLU to find great potential in MetaFormer-like models alongside other neural networks.Comment: Accepted to TPAMI. Code: https://github.com/sail-sg/metaforme

IMPORTANCE MEASURE OF PROBABILISTIC COMMON CAUSE FAILURES UNDER SYSTEM HYBRID UNCERTAINTY BASED ON BAYESIAN NETWORK

When dealing with modern complex systems, the relationship existing between components can lead to the appearance of various dependencies between component failures, where multiple items of the system fail simultaneously in unpredictable fashions. These probabilistic common cause failures affect greatly the performance of these critical systems. In this paper a novel methodology is developed to quantify the importance of common cause failures when hybrid uncertainties are presented in systems. First, the probabilistic common cause failures are modeled with Bayesian networks and are incorporated into the system exploiting the α factor model. Then, probability-boxes (bound analysis method) are introduced to model the hybrid uncertainties and quantify the effect of uncertainties on system reliability. Furthermore, an extended Birnbaum importance measure is defined to identify the critical common cause failure events and coupling impact factors when uncertainties are expressed by probability-boxes. Finally, the effectiveness of the method is demonstrated through a numerical example.W przypadku nowoczesnych systemów złożonych, relacje zachodzące między komponentami mogą prowadzić do pojawienia się różnych zależności między ich uszkodzeniami, a tym samym do sytuacji w których kilka składowych systemu ulega uszkodzeniu jednocześnie w nieprzewidywalny sposób. Tego typu probabilistyczne uszkodzenia wywołane wspólną przyczyną (PCCF) mają ogromny wpływ na wydajność tych kluczowych systemów. W przedstawionym artykule opracowano nową metodę szacowania ważności PCFF w sytuacjach, gdy w systemie występują niepewności hybrydowe. W pierwszej kolejności, PCFF zamodelowano za pomocą sieci bayesowskich i włączono do systemu wykorzystującego model współczynnika α. Następnie, wprowadzono przedziały prawdopodobieństwa, tzw. probability boxes (bound analysis method), w celu zamodelowania niepewności hybrydowych i kwantyfikacji wpływu tych niepewności na niezawodność systemu. Ponadto zdefiniowano rozszerzoną miarę ważności Birnbauma, która pozwala zidentyfikować krytyczne zdarzenia PCCF oraz czynniki, które je wywołały, w przypadkach, gdy niepewności wyrażone są za pomocą probability boxes. Skuteczność metody wykazano na przykładzie numerycznym

Bone Mineral Density Reference Standards for Chinese Children Aged 3-18: Cross-Sectional Results of the 2013-2015 China Child and Adolescent Cardiovascular Health (CCACH) Study

Objectives: No nationwide paediatric reference standards for bone mineral density (BMD) are available in China. We aimed to provide sex-specific BMD reference values for Chinese children and adolescents (3-18 years). Methods: Data (10 818 participants aged 3-18 years) were obtained from cross-sectional surveys of the China Child and Adolescent Cardiovascular Health in 2015, which included four municipality cities and three provinces. BMD was measured using Hologic Discovery Dual Energy X-ray Absorptiometry (DXA) scanner. The DXA measures were modelled against age, with height as an independent variable. The LMS statistical method using a curve fitting procedure was used to construct reference smooth cross-sectional centile curves for dependent versus independent variables. Results: Children residing in Northeast China had the highest total body less head (TBLH) BMD while children residing in Shandong Province had the lowest values. Among children, TBLH BMD was higher for boys as compared with girls; but, it increased with age and height in both sexes. Furthermore, TBLH BMD was higher among US children as compared with Chinese children. There was a large difference in BMD for height among children from these two countries. US children had a much higher BMD at each percentile (P) than Chinese children; the largest observed difference was at P50 and P3 and the smallest difference was at P97. Conclusions: This is the first study to present a sex-specific reference dataset for Chinese children aged 3-18 years. The data can help clinicians improve interpretation, assessment and monitoring of densitometry results

Phage display mediated immuno-PCR

Immuno-PCR (IPCR) is a powerful detection technology in immunological study and clinical diagnosis due to its ultrasensitivity. Here we introduce a new strategy termed phage display mediated immuno-PCR (PD-IPCR). Instead of utilization of monoclonal antibody (mAb) and chemically bond DNA that required in the conventional IPCR, a recombinant phage particle is applied as a ready reagent for IPCR experiment. The surface displayed single chain variable fragment (scFv) and phage DNA themselves can directly serve as detection antibody and PCR template, respectively. The aim of the design is to overcome shortcoming of low detection sensitivity of scFv so as to largely facilitate the real application of scFv in immunoassay. The idea has been demonstrated by applying hantaan virus nucleocapsid protein (NP) and prion protein (PrP) as detection targets in three experimental protocols (indirect, sandwich and real-time PD-IPCR assays). The detection sensitivity was increased 1000- to 10 000-folds compared with conventional enzyme-linked immunosorbent assays (ELISAs). This proof-of-concept study may serve as a new model to develop an easy to operate, low cost and ultrasensitive immunoassay method for broad applications