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Lateral multiheterostructures with spatially modulated bandgaps have great potential for applications in high-performance electronic, optoelectronic and thermoelectric devices. Multiheterostructures based on transition metal tellurides are especially promising due to their tunable bandgap in a wide range and the rich variety of structural phases. However, the synthesis of telluride-based multiheterostructures remains a challenge due to the low activity of tellurium and the poor thermal stability of tellurium alloys. In this work, we synthesized monolayer WSe 2−2 x Te 2 x /WSe 2−2 y Te 2 y ( x > y ) multiheterostructures in situ using chemical vapor deposition (CVD). Photoluminescence analysis and Raman mapping confirm the spatial modulation of the bandgap in the radial direction. Furthermore, field-effect transistors with the channels parallel (type I) and perpendicular (type II) to the multiheterostructure rings were fabricated. Type I transistors exhibit enhanced ambipolar transport, due to the low energy bridges between the source and drain. Remarkably, the photocurrents in type I transistors are two orders of magnitude higher than those in type II transistors, which can be attributed to the fact that the photovoltaic photocurrents generated at the two heterojunctions are summed together in type I transistors, but they cancel each other in typemore »II transistors. These multiheterostructures will provide a new platform for novel electronic/photonic devices with potential applications in broadband light sensing, highly sensitive imaging and ultrafast optoelectronic integrated circuits.« less

Indium Selenide (In 2 Se 3 ) is a newly emerged van der Waals (vdW) ferroelectric material, which unlike traditional insulating ferroelectric materials, is a semiconductor with a bandgap of about 1.36 eV. Ferroelectric diodes and transistors based on In 2 Se 3 have been demonstrated. However, the interplay between light and electric polarization in In 2 Se 3 has not been explored. In this paper, we found that the polarization in In 2 Se 3 can be programmed by optical stimuli, due to its semiconducting nature, where the photo generated carriers in In 2 Se 3 can alter the screening field and lead to polarization reversal. Utilizing these unique properties of In 2 Se 3 , we demonstrated a new type of multifunctional device based on 2D heterostructures, which can concurrently serve as a logic gate, photodetector, electronic memory and photonic memory. This dual electrical and optical operation of the memories can simplify the device architecture and offer additional functionalities, such as ultrafast optical erase of large memory arrays. In addition, we show that dual-gate structure can address the partial switching problem commonly observed in In 2 Se 3 ferroelectric transistors, as the two gates can enhance the verticalmore »electric field and facilitate the polarization switching in the semiconducting In 2 Se 3 . These discovered effects are of general nature and should be observable in any ferroelectric semiconductor. These findings deepen the understanding of polarization switching and light-polarization interaction in semiconducting ferroelectric materials and open up their applications in multifunctional electronic and photonic devices.« less

End-to-end data-driven image compressive sensing reconstruction (EDCSR) frameworks achieve state-of-the-art reconstruction performance in terms of reconstruction speed and accuracy. However, due to their end-to-end nature, existing EDCSR frameworks can not adapt to a variable compression ratio (CR). For applications that desire a variable CR, existing EDCSR frameworks must be trained from scratch at each CR, which is computationally costly and time-consuming. This paper presents a generic compression ratio adapter (CRA) framework that addresses the variable compression ratio (CR) problem for existing EDCSR frameworks with no modification to given reconstruction models nor enormous rounds of training needed. CRA exploits an initial reconstruction network to generate an initial estimate of reconstruction results based on a small portion of the acquired measurements. Subsequently, CRA approximates full measurements for the main reconstruction network by complementing the sensed measurements with resensed initial estimate. Our experiments based on two public image datasets (CIFAR10 and Set5) show that CRA provides an average of 13.02 dB and 5.38 dB PSNR improvement across the CRs from 5 to 30 over a naive zero-padding approach and the AdaptiveNN approach(a prior work), respectively. CRA addresses the fixed-CR limitation of existing EDCSR frameworks and makes them suitable for resource-constrained compressive sensing applications.

3D models of objects and scenes are critical to many academic disciplines and industrial applications. Of particular interest is the emerging opportunity for 3D graphics to serve artificial intelligence: computer vision systems can benefit from synthetically-generated training data rendered from virtual 3D scenes, and robots can be trained to navigate in and interact with real-world environments by first acquiring skills in simulated ones. One of the most promising ways to achieve this is by learning and applying generative models of 3D content: computer programs that can synthesize new 3D shapes and scenes. To allow users to edit and manipulate the synthesized 3D content to achieve their goals, the generative model should also be structure-aware: it should express 3D shapes and scenes using abstractions that allow manipulation of their high-level structure. This state-of-the- art report surveys historical work and recent progress on learning structure-aware generative models of 3D shapes and scenes. We present fundamental representations of 3D shape and scene geometry and structures, describe prominent methodologies including probabilistic models, deep generative models, program synthesis, and neural networks for structured data, and cover many recent methods for structure-aware synthesis of 3D shapes and indoor scenes.