Thin-film quantum dot photodiode for monolithic infrared image sensors

Imaging in the infrared wavelength range has been fundamental in scientific, military and surveillance applications. Currently, it is a crucial enabler of new industries such as autonomous mobility (for obstacle detection), augmented reality (for eye tracking) and biometrics. Ubiquitous deployment of infrared cameras (on a scale similar to visible cameras) is however prevented by high manufacturing cost and low resolution related to the need of using image sensors based on flip-chip hybridization. One way to enable monolithic integration is by replacing expensive, small-scale III-V-based detector chips with narrow bandgap thin-films compatible with 8- and 12-inch full-wafer processing. This work describes a CMOS-compatible pixel stack based on lead sulfide quantum dots (PbS QD) with tunable absorption peak. Photodiode with a 150-nm thick absorber in an inverted architecture shows dark current of 10(-6) A/cm(2) at 2 V reverse bias and EQE above 20% at 1440 nm wavelength. Optical modeling for top illumination architecture can improve the contact transparency to 70%. Additional cooling (193 K) can improve the sensitivity to 60 dB. This stack can be integrated on a CMOS ROIC, enabling order-of-magnitude cost reduction for infrared sensors

Distributed-Memory Breadth-First Search on Massive Graphs

This chapter studies the problem of traversing large graphs using the breadth-first search order on distributed-memory supercomputers. We consider both the traditional level-synchronous top-down algorithm as well as the recently discovered direction optimizing algorithm. We analyze the performance and scalability trade-offs in using different local data structures such as CSR and DCSC, enabling in-node multithreading, and graph decompositions such as 1D and 2D decomposition.Comment: arXiv admin note: text overlap with arXiv:1104.451

Nanosecond spin lifetimes in bottom-up fabricated bilayer graphene spin-valves with atomic layer deposited Al2_2O3_3 spin injection and detection barriers

We present spin transport studies on bi- and trilayer graphene non-local spin-valves which have been fabricated by a bottom-up fabrication method. By this technique, spin injection electrodes are first deposited onto Si$^{++}$/SiO$_2$ substrates with subsequent mechanical transfer of a graphene/hBN heterostructure. We showed previously that this technique allows for nanosecond spin lifetimes at room temperature combined with carrier mobilities which exceed 20,000 cm$^2$/(Vs). Despite strongly enhanced spin and charge transport properties, the MgO injection barriers in these devices exhibit conducting pinholes which still limit the measured spin lifetimes. We demonstrate that these pinholes can be partially diminished by an oxygen treatment of a trilayer graphene device which is seen by a strong increase of the contact resistance area products of the Co/MgO electrodes. At the same time, the spin lifetime increases from 1 ns to 2 ns. We believe that the pinholes partially result from the directional growth in molecular beam epitaxy. For a second set of devices, we therefore used atomic layer deposition of Al$_2$O$_3$ which offers the possibility to isotropically deposit more homogeneous barriers. While the contacts of the as-fabricated bilayer graphene devices are non-conductive, we can partially break the oxide barriers by voltage pulses. Thereafter, the devices also exhibit nanosecond spin lifetimes.Comment: 6 pages, 4 figure

Human Attention in Image Captioning: Dataset and Analysis

In this work, we present a novel dataset consisting of eye movements and verbal descriptions recorded synchronously over images. Using this data, we study the differences in human attention during free-viewing and image captioning tasks. We look into the relationship between human attention and language constructs during perception and sentence articulation. We also analyse attention deployment mechanisms in the top-down soft attention approach that is argued to mimic human attention in captioning tasks, and investigate whether visual saliency can help image captioning. Our study reveals that (1) human attention behaviour differs in free-viewing and image description tasks. Humans tend to fixate on a greater variety of regions under the latter task, (2) there is a strong relationship between described objects and attended objects ($97\%$ of the described objects are being attended), (3) a convolutional neural network as feature encoder accounts for human-attended regions during image captioning to a great extent (around $78\%$), (4) soft-attention mechanism differs from human attention, both spatially and temporally, and there is low correlation between caption scores and attention consistency scores. These indicate a large gap between humans and machines in regards to top-down attention, and (5) by integrating the soft attention model with image saliency, we can significantly improve the model's performance on Flickr30k and MSCOCO benchmarks. The dataset can be found at: https://github.com/SenHe/Human-Attention-in-Image-Captioning.Comment: To appear at ICCV 201