J. Janssen

Analysis of Cu/low-k bond pad delamination by using a novel failure index

For the development of state-of-the-art Cu/low-k CMOS technologies, the integration and introduction of new low-k materials are one of the major bottlenecks due to the bad thermal and mechanical integrity of these materials and the inherited weak interfacial adhesion. Especially the forces resulting from packaging related processes such as dicing, wire bonding, bumping and molding are critical and can easily result in cracking, delamination and chipping of the IC back-end structure if no appropriate measures are taken. This paper presents a methodology for optimizing the thermo-mechanical reliability of bond pads by using a 3D multi-scale finite element approach. An important characteristic of this methodology is the use of a novel energy-based failure index, which allows a fast qualitative comparison of different back-end structures. The usability of the methodology will be illustrated by the comparison of three different bond pad structures.

Age at First Episode Modulates Diagnosis-Related Structural Brain Abnormalities in Psychosis

Brain volume and thickness abnormalities have been reported in first-episode psychosis (FEP). However, it is unclear if and how they are modulated by brain developmental stage (and, therefore, by age at FEP as a proxy). This is a multicenter cross-sectional case-control brain magnetic resonance imaging (MRI) study. Patients with FEP (n = 196), 65.3% males, with a wide age at FEP span (12-35 y), and healthy controls (HC) (n = 157), matched for age, sex, and handedness, were scanned at 6 sites. Gray matter volume and thickness measurements were generated for several brain regions using FreeSurfer software. The nonlinear relationship between age at scan (a proxy for age at FEP in patients) and volume and thickness measurements was explored in patients with schizophrenia spectrum disorders (SSD), affective psychoses (AFP), and HC. Earlier SSD cases (ie, FEP before 15-20 y) showed significant volume and thickness deficits in frontal lobe, volume deficits in temporal lobe, and volume enlargements in ventricular system and basal ganglia. First-episode AFP patients had smaller cingulate cortex volume and thicker temporal cortex only at early age at FEP (before 18-20 y). The AFP group also had age-constant (12-35-y age span) volume enlargements in the frontal and parietal lobe. Our study suggests that age at first episode modulates the structural brain abnormalities found in FEP patients in a nonlinear and diagnosis-dependent manner. Future MRI studies should take these results into account when interpreting samples with different ages at onset and diagnosis.

Reproducibility of brain‐cognition relationships using three cortical surface‐based protocols: An exhaustive analysis based on cortical thickness

People differ in their cognitive functioning. This variability has been exhaustively examined at the behavioral, neural and genetic level to uncover the mechanisms by which some individuals are more cognitively efficient than others. Studies investigating the neural underpinnings of interindividual differences in cognition aim to establish a reliable nexus between functional/structural properties of a given brain network and higher order cognitive performance. However, these studies have produced inconsistent results, which might be partly attributed to methodological variations. In the current study, 82 healthy young participants underwent MRI scanning and completed a comprehensive cognitive battery including measurements of fluid, crystallized, and spatial intelligence, along with working memory capacity/executive updating, controlled attention, and processing speed. The cognitive scores were obtained by confirmatory factor analyses. T1‐weighted images were processed using three different surface‐based morphometry (SBM) pipelines, varying in their degree of user intervention, for obtaining measures of cortical thickness (CT) across the brain surface. Distribution and variability of CT and CT‐cognition relationships were systematically compared across pipelines and between two cognitively/demographically matched samples to overcome potential sources of variability affecting the reproducibility of findings. We demonstrated that estimation of CT was not consistent across methods. In addition, among SBM methods, there was considerable variation in the spatial pattern of CT‐cognition relationships. Finally, within each SBM method, results did not replicate in matched subsamples. Hum Brain Mapp 36:3227–3245, 2015.

Effect of metal layout design on passivation crack occurrence using both experimental and simulation techniques

Thermo-mechanical reliability is one of the concerns for semiconductor developments due to miniaturization, introduction of new materials, and higher application temperatures. FE modeling techniques are developed to predict the effect of IC interconnect metal designs on the thermo-mechanically-induced cracking of passivation layers. Experimental techniques on specially designed IC packages are developed to verify the predicted passivation cracks. With the verified 2D and 3D models, various simulations are performed and it is established that delamination of the IC/compound interface is a key trigger for passivation cracking. When delamination is present, crack occurrence is found to depend on the metal layout and location on the IC. Optimizing the metal layout design can even prevent passivation cracks. By combining efficient and accurate simulations with a limited number of experiments, passivation cracks can be quantitatively predicted prior to physical prototyping.

Novel Damage Model for Delamination in Cu/low-k IC Backend Structures

For the development of state-of-the-art Cu/low-k CMOS technologies, the integration and introduction of new low-k materials are one of the major bottlenecks due to the bad thermal and mechanical integrity of these materials and the inherited weak interfacial adhesion. Especially the forces resulting from packaging related processes such as dicing, wire bonding, bumping and molding are critical and can easily result in cracking, delamination and chipping of the IC back-end structure if no appropriate measures are taken. This paper presents a methodology for optimizing the thermomechanical reliability of bond pads by using a 3D multi-level Finite Element approach. An important characteristic of this methodology is the use of a novel energy-based damage model, which allows a fast qualitative comparison of different back-end structures. The usability of the methodology will be illustrated by the comparison of three different bond pad

Platform for systems medicine research and diagnostic applications in psychotic disorders—The METSY project

Psychotic disorders are associated with metabolic abnormalities including alterations in glucose and lipid metabolism. A major challenge in the treatment of psychosis is to identify patients with vulnerable metabolic profiles who may be at risk of developing cardiometabolic co-morbidities. It is established that both central and peripheral metabolic organs use lipids to control energy balance and regulate peripheral insulin sensitivity. The endocannabinoid system, implicated in the regulation of glucose and lipid metabolism, has been shown to be dysregulated in psychosis. It is currently unclear how these endocannabinoid abnormalities relate to metabolic changes in psychosis. Here we review recent research in the field of metabolic co-morbidities in psychotic disorders as well as the methods to study them and potential links to the endocannabinoid system. We also describe the bioinformatics platforms developed in the EU project METSY for the investigations of the biological etiology in patients at risk of psychosis and in first episode psychosis patients. The METSY project was established with the aim to identify and evaluate multi-modal peripheral and neuroimaging markers that may be able to predict the onset and prognosis of psychiatric and metabolic symptoms in patients at risk of developing psychosis and first episode psychosis patients. Given the intrinsic complexity and widespread role of lipid metabolism, a systems biology approach which combines molecular, structural and functional neuroimaging methods with detailed metabolic characterisation and multi-variate network analysis is essential in order to identify how lipid dysregulation may contribute to psychotic disorders. A decision support system, integrating clinical, neuropsychological and neuroimaging data, was also developed in order to aid clinical decision making in psychosis. Knowledge of common and specific mechanisms may aid the etiopathogenic understanding of psychotic and metabolic disorders, facilitate early disease detection, aid treatment selection and elucidate new targets for pharmacological treatments.

The association between gene variants and longitudinal structural brain changes in psychosis: a systematic review of longitudinal neuroimaging genetics studies

Evidence suggests that genetic variation might influence structural brain alterations in psychotic disorders. Longitudinal genetic neuroimaging (G-NI) studies are designed to assess the association between genetic variants, disease progression and brain changes. There is a paucity of reviews of longitudinal G-NI studies in psychotic disorders. A systematic search of PubMed from inception until November 2016 was conducted to identify longitudinal G-NI studies examining the link between Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI)-based brain measurements and specific gene variants (SNPs, microsatellites, haplotypes) in patients with psychosis. Eleven studies examined seven genes: BDNF, COMT, NRG1, DISC1, CNR1, GAD1, and G72. Eight of these studies reported at least one association between a specific gene variant and longitudinal structural brain changes. Genetic variants associated with longitudinal brain volume or cortical thickness loss included a 4-marker haplotype in G72, a microsatellite and a SNP in NRG1, and individual SNPs in DISC1, CNR1, BDNF, COMT and GAD1. Associations between genotype and progressive brain changes were most frequently observed in frontal regions, with five studies reporting significant interactions. Effect sizes for significant associations were generally of small or intermediate magnitude (Cohen’s d < 0.8). Only two genes (BDNF and NRG1) were assessed in more than one study, with great heterogeneity of the results. Replication studies and studies exploring additional genetic variants identified by large-scale genetic analysis are warranted to further ascertain the role of genetic variants in longitudinal brain changes in psychosis.

Virtual Design and Qualification of IC Backend Structures

For Integrated Circuit (IC) wafer backend development, process developers have to design robust backend structures that guarantee both functionality and reliability during waferfab processes, packaging, qualification tests and lifetime. Figure 1 shows a simplified diagram for the design (and redesign) cycle forevelopment. Subsequently, package development IC development. Subsequently, package develop t . inherited runs a similar cycle. By using reliability modell relate it to the interaction of IC and package assembly, such as IC/compound delamination, we aim at integrating IC and packge prototyping in order to develop reliable IC packages faster. This paper presents parts of our research to approach thermo-mechanical IC reliability by virtually designing and quaifying IC backend structures in both IC processing, packaging and testing processes. By combining experimental and numerical results, targeted failure modes and mechanisms as well as their interactions are understood. It is found that delamination is the key trigger for passivation cracking and metal shift. Even more, the layout of interconnect metals in the backend of ICs has a major effect on under bond-pad wir delamination observed after wafer probing an wire ing. Reliable predictive modelling approaches enable IC package development towards a first-time-right practice.

Virtual qualification of moisture induced failures of advanced packages [IC packages]

This paper presents a combined numerical and experimental methodology for predicting and preventing moisture induced failures in encapsulated packages. Prevention of such failures can enable efficient and optimal pre-selection of materials, their interfaces and geometric design with respect to the desired resistance to moisture. This virtual qualification methodology is illustrated for a specific BGA package which showed 50% failures (broken stitch-bonds) during HAST testing due to excessive warpage and/or delamination of different interfaces. For three different material combinations the moisture diffusion during the HAST test is predicted and subsequently thermo-mechanical-moisture simulations are performed where the effects of hygro-swelling, vapor pressure, thermal expansion and delamination on the failure mechanisms are predicted. The comparison of the simulation results of the different molding compounds with the observations of HAST testing indicates that the developed methods and models can predict the observed trends.

EPA-0882 - Prediction of diagnosis of early-onset schizophrenia spectrum disorders using support vector machines

Diagnosis of schizophrenia spectrum disorders (SSD) may be difficult in clinical practice, particularly during the first episodes of early-onset psychosis (FE-EOP). Aims To develop a Support Vector Machine (SVM) algorithm as a predictive tool for diagnostic outcome in patients with FE-EOP, based on clinical and biomedical data at the emergence of the illness. Methods Two-year, prospective longitudinal study, where 81 patients (9-17 years of age) with a FE-EOP and stable diagnosis at follow-up and 41 age and sex-matched healthy controls (HC) were included. Structured diagnostic interviews, clinical and cognitive scales, a MRI scan and biochemical tests were conducted at baseline. Three SVM classification algorithms were developed (SSD vs HC group, non-SSD vs HC group, and SSD vs non-SSD group). Jackknifing was used to validate the algorithms and to calculate performance estimates. Enhanced-Recursive Feature Elimination was performed in order to gain information about the predictive weight for diagnosis of each variable. Results The SSD-versus-non-SSD classifier achieved an overall accuracy of 83.1%, sensitivity of 86.6% and specificity of 77.8%. The variables during a FE-EOP with higher predictive value for a diagnosis of SSD were clinical variables such as negative symptoms preceding or during the psychotic onset, poor insight and duration of illness until first psychiatric contact. Biochemical, neuroimaging, and cognitive variables at baseline did not provide any additional predictive value. Conclusions SVM may serve as a predictive tool for early diagnosis of SSD during a FE-EOP. The most discriminative variables during a FE-EOP for a future diagnosis of SSD are clinical variables.