Superradiant Emission from Coherent Excitons in van Der Waals Heterostructures

Recent advancements in isolation and stacking of layered van der Waals materials have created an unprecedented paradigm for demonstrating varieties of 2D quantum materials. Rationally designed van der Waals heterostructures composed of monolayer transition-metal dichalcogenides (TMDs) and few-layer hBN show several unique optoelectronic features driven by correlations. However, entangled superradiant excitonic species in such systems have not been observed before. In this report, it is demonstrated that strong suppression of phonon population at low temperature results in a formation of a coherent excitonic-dipoles ensemble in the heterostructure, and the collective oscillation of those dipoles stimulates a robust phase synchronized ultra-narrow band superradiant emission even at extremely low pumping intensity. Such emitters are in high demand for a multitude of applications, including fundamental research on many-body correlations and other state-of-the-art technologies. This timely demonstration paves the way for further exploration of ultralow-threshold quantum-emitting devices with unmatched design freedom and spectral tunability.     

Defocusing image to pattern contact holes using attenuated phase shift masks

The patterning of contact holes by selecting out-of-focus image plane (defocus) using attenuated phase shift masks (APSM) has been studied. Defocus is found to enhance the image modulation at low partial coherence for contact holes with negative local average of mask function. Semi-dense holes up to 130 nm in 8% APSM have been printed by 0.5 μm defocus at a partial coherence of 0.31 using KrF scanner with highest numerical aperture of 0.68. However, these holes were closed with in-focus imaging. Defocus is also found to be beneficial for patterning the pitches that have extensive side lobes with in-focus imaging.     

Adaptive motion control using neural network approximations

In this paper, we present a new adaptive technique for tracking control of mechanical systems in the presence of friction and periodic disturbances. Radial Basis Functions (RBFs) are used to compensate for the effects of nonlinearly occurring parameters in the friction and periodic disturbance model. Theoretical analysis, such as stability and transient performance, is provided. Furthermore, the performance of the adaptive RBF controller and its non-adaptive counterpart are compared.     

Fast internal dynamics in polyelectrolyte gels measured by dynamic light scattering

Summary Dynamic light scattering was used to investigate the dynamics of sodium poly(styrene sulfonate) and fully neutralized poly(acrylic acid) gels as a function of the degree of swelling and weight ratio of cross-linking agent. It was shown that the collective diffusion coefficient increases with increasing degree of swelling and that the diffusion coefficient shows stronger concentration dependence than predicted by scaling arguments. For gel samples measured at the swelling equilibrium, the diffusion coefficient increases with increasing gel concentration for both gel systems.     

Topology preservation of template-based 6-subiteration reduction operations

Generally, a reduction operation (e.g., thinning and shrinking) on 3D binary images can be represented as a set of reduction templates where every object voxel of the image satisfying any template is turned to a background voxel. Generally, it is rather difficult, error-prone and time-consuming for verifying the topological soundness of a 3D parallel reduction operation. This paper proposes sufficient conditions of time complexity O(n) for verifying the topological soundness of 3D parallel 6-subiteration reduction operations of n templates where such a kind of 3D reduction operations is performed alternatively from the six orthogonal directions to turn object voxels to background voxels. By such sufficient conditions, the topology soundness of a 3D 6-subiteration parallel reduction operation can be verified by checking each and every of its templates.     

Modeling tool wear in end-milling using enhanced GMDH learning networks

This paper presents an enhanced approach to predictive modeling for determining tool-wear in end-milling operations based on enhanced-group method of data handling (e-GMDH). Using milling input parameters (speed, feed, and depth-of-cut) and response (tool wear), the data for the model is partitioned into training and testing datasets, and the training dataset is used to realize a predictive model that is a function of the input parameters and the coefficients determined. In our approach, we first present a methodology for modeling, and then develop predictive model(s) of the problem being solved in the form of second-order equations based on the input data and coefficients realized. This approach leads to some generalization because it becomes possible to predict not only the test data obtained during experimentation, but other test data outside the experimental results can also be used. Moreover, this approach makes it easy to present the realized solution in a form that can be further optimized for the input parameters using some optimization techniques. The results realized using our e-GMDH method are promising, and the comparative study presented shows that the e-GMDH outperforms polynomial neural network (PNN); moreover, it is more flexible than the conventional GMDH, which tends to produce nonlinear solutions even for simple problems. In the investigation, the extended particle swarm optimization (PSO) technique was applied to obtain the optimal parameters. Consequently, the modeling approach is extremely useful in realizing a computer-aided process-planning system in an advanced manufacturing environment.