Application of the hybrid Hopkins–Abbe method in full-chip OPC

The hybrid Hopkins–Abbe method is presented and shown to resolve the problem of the traditional Hopkins theory, namely the requirement for constant mask diffraction efficiencies. Simulation of electromagnetic scattering from the mask that takes into account the oblique angles of incidence from the illumination is performed by application of the domain decomposition method that is extended for offaxis illumination. Examples of 45 nm and 32 nm lines and spaces through pitch and through focus are presented to demonstrate the validity and accuracy of the hybrid Hopkins–Abbe method. The results obtained are in excellent agreement with a rigorous and independent (third party) simulator.     

A Memristive Nanoparticle/Organic Hybrid Synapstor for Neuroinspired Computing

A large effort is devoted to the research of new computing paradigms associated with innovative nanotechnologies that should complement and/or propose alternative solutions to the classical Von Neumann/CMOS (complementary metal oxide semiconductor) association. Among various propositions, spiking neural network (SNN) seems a valid candidate. i) In terms of functions, SNN using relative spike timing for information coding are deemed to be the most effective at taking inspiration from the brain to allow fast and efficient processing of information for complex tasks in recognition or classification. ii) In terms of technology, SNN may be able to benefit the most from nanodevices because SNN architectures are intrinsically tolerant to defective devices and performance variability. Here, spike‐timing‐dependent plasticity (STDP), a basic and primordial learning function in the brain, is demonstrated with a new class of synapstor (synapse‐transistor), called nanoparticle organic memory field‐effect transistor (NOMFET). This learning function is obtained with a simple hybrid material made of the self‐assembly of gold nanoparticles and organic semiconductor thin films. Beyond mimicking biological synapses, it is also demonstrated how the shape of the applied spikes can tailor the STDP learning function. Moreover, the experiments and modeling show that this synapstor is a memristive device. Finally, these synapstors are successfully coupled with a CMOS platform emulating the pre‐ and postsynaptic neurons, and a behavioral macromodel is developed on usual device simulator.     

A Photoaddressable Liquid Crystalline Phase Transition in Graphene Oxide Nanocomposites

The multifunctionality of graphene has the potential to unlock important developments in nanocomposite science. However, the manipulation of graphene without interfering with its unique properties and while controlling its spatial organization remains challenging. Here, the formation of a photoaddressable liquid crystalline (LC) solution through the stabilization of graphene oxide (GO) with photocleavable brushes is described. The LC behavior leads to the thermodynamic entrapment of GO into low aspect ratio domains that fail to display the properties typically predicted for graphene nanocomposites. The morphology and structural and electronic performance of these nanocomposites are regenerated through the brush cleavage, which controls the phase transition of the LC phase. These results show that kinetic control of graphene assembly can be an attractive tool toward the dynamic regulation of processable sol states and structured percolated networks for rational composite manufacturing.     

Physical investigation of the impact of electrolessly deposited self-aligned caps on insulation of copper interconnects

With the miniaturization of ULSI circuits and the associated increase of current density up to several MA/cm², copper interconnects are facing electromigration issues at the top interface with the dielectric capping layer SiC(N). A promising solution is to insert selectively on top of copper lines a CoWP metallic self-aligned encapsulation layer, deposited using a wet electroless process. We study the impact of this process on electrical line insulation as a function of cap thickness at the 65 nm technology node and we investigate the physical origin of leakage currents. Below a critical thickness, only a slight leakage current increase of less than one decade is observed, remaining within the specification for self-aligned capping layer processes. Above this critical thickness, large leakage currents are generated due to the combined effect of lateral growth and the presence of parasitic redeposited nodules. We show that a simple phenomenological model allows to reproduce the experimental data, to assess quantitatively the contribution of parasitic defects, and to predict that the self-aligned barrier technology should be extendible up to the 32 nm node, provided that a thin cap layer of less than 8 nm is used.     

Engineering Transition Metal Layers for Long Lasting Anionic Redox in Layered Sodium Manganese Oxide

Oxygen-redox-based-layered cathode materials are of great importance in realizing high-energy-density sodium-ion batteries (SIBs) that can satisfy the demands of next-generation energy storage technologies. However, Mn-based-layered materials (P2-type Na-poor Na_y[A_xMn_1−x]O₂, where A = alkali ions) still suffer from poor reversibility during oxygen-redox reactions and low conductivity. In this work, the dual Li and Co replacement is investigated in P2-type-layered Na_xMnO₂. Experimentally and theoretically, it is demonstrated that the efficacy of the dual Li and Co replacement in Na_0.6[Li_0.15Co_0.15Mn_0.7]O₂ is that it improves the structural and cycling stability despite the reversible Li migration from the transition metal layer during de-/sodiation. Operando X-ray diffraction and ex situ neutron diffraction analysis prove that the material maintains a P2-type structure during the entire range of Na⁺ extraction and insertion with a small volume change of ≈4.3%. In Na_0.6[Li_0.15Co_0.15Mn_0.7]O₂, the reversible electrochemical activity of Co³⁺/Co⁴⁺, Mn³⁺/Mn⁴⁺, and O^2-/(O₂)^n- redox is identified as a reliable mechanism for the remarkable stable electrochemical performance. From a broader perspective, this study highlights a possible design roadmap for developing cathode materials with optimized cationic and anionic activities and excellent structural stabilities for SIBs.     

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.     

Modelling of through silicon via and devices electromagnetic coupling

This paper is essentially composed of two parts for future synthesis. We developed 2D and 3D simulations, starting from a 0.35 μm standard CMOS technology, focusing on through silicon via or redistribution layer induced coupling; nMOSFET, pMOSFET, and the sensitive regions of the CMOS inverter are investigated. We also study stacked devices in 3D circuits, in the radiofrequency range, and propagation of electromagnetic waves along some interconnections with discontinuities. This study is performed in the time domain—a finite-difference time-domain method is applied to the analysis of some vias flanked by two striplines, all embedded in silicon. Electric and magnetic field distributions, transmission and reflexion parameters, and pulse propagations along a transverse via are presented.     

Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge

EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intrapatient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the configuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed.     

Spirobifluorene Dimers: Understanding How The Molecular Assemblies Drive The Electronic Properties

Spirobifluorene (SBF) is one of the most important scaffolds used in the design of organic semi-conductors (OSCs) for electronics. In recent years, among all the structures developed for these applications, SBF dimers have been highlighted due to their great potential in thermally activated delayed fluorescence and in phosphorescent organic light-emitting diodes. Attaching two SBF units generate 10 dimers, each possessing its own structural specificity, which in turn drives its electronic properties. These ten SBF dimers are gathered herein. Understanding how the molecular assembly determines the electronic properties has been one of the pillars of organic electronics. This is the goal of this article. As positional isomerism is a key tool to design OSCs, defining the design guidelines for the SBF scaffold appears of interest for the future of this building block. Herein, the importance of the two main parameters involved in the electrochemical and photophysical properties, namely the nature of the phenyl linkages and the steric congestion between the two SBF units is discussed. The combination of these two parameters drives the electronic properties but their respective weight is different as a function of the regioisomer involved or of the property considered (frontier orbitals energy level, absorption, fluorescence, phosphorescence).     

Machine learning to design full-reference image quality assessment algorithm

A crucial step in image compression is the evaluation of its performance, and more precisely, available ways to measure the quality of compressed images. In this paper, a machine learning expert, providing a quality score is proposed. This quality measure is based on a learned classification process in order to respect human observers. The proposed method namely Machine Learning-based Image Quality Measure (MLIQM) first classifies the quality using multi-Support Vector Machine (SVM) classification according to the quality scale recommended by the ITU. This quality scale contains 5 ranks ordered from 1 (the worst quality) to 5 (the best quality). To evaluate the quality of images, a feature vector containing visual attributes describing images content is constructed. Then, a classification process is performed to provide the final quality class of the considered image. Finally, once a quality class is associated to the considered image, a specific SVM regression is performed to score its quality. Obtained results are compared to the one obtained applying classical Full-Reference Image Quality Assessment (FR-IQA) algorithms to judge the efficiency of the proposed method.