Mobility enhancement techniques for Ge and GeSn MOSFETs

The performance enhancement of conventional Si MOSFETs through device scaling is becoming increasingly difficult.The application of high mobility channel materials is one of the most promising solutions to overcome the bottleneck.The Ge and GeSn channels attract a lot of interest as the alternative channel materials,not only because of the high carrier mobility but also the superior compatibility with typical Si CMOS technology.In this paper,the recent progress of high mobility Ge and GeSn MOSFETs has been investigated,providing feasible approaches to improve the performance of Ge and GeSn devices for future CMOS technologies.

Symmetric silicon microring resonator optical crossbar array for accelerated inference and training in deep learning

Photonic integrated circuits are emerging as a promising platform for accelerating matrix multiplications in deep learning,leveraging the inherent parallel nature of light.Although various schemes have been proposed and demonstrated to realize such photonic matrix accelerators,the in situ training of artificial neural networks using photonic accelerators remains challenging due to the difficulty of direct on-chip backpropagation on a photonic chip.In this work,we propose a silicon microring resonator(MRR)optical crossbar array with a symmetric structure that allows for simple on-chip backpropagation,potentially enabling the acceleration of both the inference and training phases of deep learning.We demonstrate a 4×4 circuit on a Si-on-insulator platform and use it to perform inference tasks of a simple neural network for classifying iris flowers,achieving a classification accuracy of 93.3%.Subsequently,we train the neural network using simulated on-chip backpropagation and achieve an accuracy of 91.1%in the same inference task after training.Furthermore,we simulate a convolutional neural network for handwritten digit recognition,using a 9×9 MRR crossbar array to perform the convolution operations.This work contributes to the realization of compact and energy-efficient photonic accelerators for deep learning.

High-Order Low-Dissipation Shock-Resolving TENO-THINC Schemes for Hyperbolic Conservation Laws

While the recently proposed TENO(targeted essentially non-oscillatory)schemes[Fu et al.,Journal of Computational Physics 305(2016):333-359]exhibit better performance than the classical WENO(weighted essentially non-oscillatory)schemes with the same accuracy order,there is still a room for further improvement,e.g.,the physical discontinuities may be significantly smeared by the excessive numerical dissipation due to the enforcement of the ENO property after a long-time advection.More recently,a new fifth-order TENO5-THINC scheme is proposed by coupling the TENO5 scheme with a non-polynomial THINC(tangent of hyperbola for interface capturing)scheme based on a parameter-free discontinuity indicator.The novelty originates from the fact that the new strategy locates the discontinuities accurately and deploys the jump-like THINC reconstruction scheme for resolving the discontinuities with a sub-cell resolution,instead of enforcing the ENO property.The new scheme successfully leverages the excellent wave-resolution property of standard TENO schemes for smooth and under-resolved continuous scales and the discontinuity-resolving capability of THINC for reconstructing genuine discontinuities.In this work,we further develop the low-dissipation discontinuity-resolving very-high-order TENO-THINC reconstruction schemes for hyperbolic conservation laws by proposing tailored coupling strategies.Without loss of generality,the six-and eight-point TENO-THINC schemes are developed,and the explicit formulas are given as well as the built-in parameters.Based on a set of critical benchmark simulations,the newly proposed schemes show S.Takagi,H.Wakimura,L.Fu and F.Xiao/Commun.Comput.Phys.,34(2023),pp.1043-1078 significantly lower numerical dissipation when compared to the counterpart TENO schemes without sacrificing numerical robustness.The presented numerical results represent the state-of-the-art in the literature and can serve as references for future algorithm development.