Engineered quantum dots for infrared photodetectors

Quantum Dot Infrared Photodetector (QDIP) Focal Plane Arrays (FPAs) have been proposed as an alternative technology for the 3rd generation FPAs. QDIPs are emerging as a competitive technology for infrared detection and imaging especially in the midwave infrared (MWIR) and longwave infrared (LWIR) regime. These detectors are based on intersubband transitions in self-assembled InAs quantum dots (QDs) and offer several advantages such as normal incidence detection, low dark currents and high operating temperatures, while enjoying all the benefits of a mature GaAs fabrication technology. However, due to Stranski-Krastanov (SK) growth mode and the subsequent capping growth, the conventional SK QDs are pancake shaped\u27 with small height to base ratio due to interface diffusion. Thus they cannot fully exploit the 3D \u27artificial atom\u27 properties. This dissertation work investigates two approaches for shape engineered QDs: (1) Selective capping techniques of Stranski-Krastanov QDs, and (2) Growth of Sub-Monolayer (SML) QDs. Using Molecular Beam Epitaxy (MBE) growth, engineered QDs have been demonstrated with improved dot geometry and 3D quantum confinement to more closely resemble the 3D \u27artificial atom\u27. In SK-QDs, the results have demonstrated an increased dot height to base aspect ratio of 0.67 compared with 0.23 for conventional SK-QD using Transmission Electron Microscope (TEM) images, enhanced s-to-p polarized spectral response ratio of 37% compared with 10% for conventional SK-QD, and improved SK-QDIP characterization such as: high operating temperature of 150K under background-limited infrared photodetection (BLIP) condition, photodetectivity of 1x109 cmHz1/2/W at 77K for a peak wavelength of 4.8 μm, and photoconductive gain of 100 (Vb=12V) at 77 K. In SML-QDs, we have demonstrated dots with a small base width of 4~6 nm, height of 8 nm, absence of wetting layer and advantage optical property than the SK-QDs. SML-QD shows adjustable dot height to base aspect ratio of 8nm/6nm, increased s-to-p polarized spectral response ratio of 33%, and a narrower full width at half maximum (FWHM), long wavelength 10.5 μm bound-to-bound intersubband transition, and higher responsivity of 1.2 A/W at -2.2 V at 77K and detectivity of 4x109 cmHz1/2/W at 0.4 V 77K.\u2

ReLER@ZJU Submission to the Ego4D Moment Queries Challenge 2022

In this report, we present the ReLER@ZJU1 submission to the Ego4D Moment Queries Challenge in ECCV 2022. In this task, the goal is to retrieve and localize all instances of possible activities in egocentric videos. Ego4D dataset is challenging for the temporal action localization task as the temporal duration of the videos is quite long and each video contains multiple action instances with fine-grained action classes. To address these problems, we utilize a multi-scale transformer to classify different action categories and predict the boundary of each instance. Moreover, in order to better capture the long-term temporal dependencies in the long videos, we propose a segment-level recurrence mechanism. Compared with directly feeding all video features to the transformer encoder, the proposed segment-level recurrence mechanism alleviates the optimization difficulties and achieves better performance. The final submission achieved Recall@1,tIoU=0.5 score of 37.24, average mAP score of 17.67 and took 3-rd place on the leaderboard.Comment: Accepted to ECCV 2022 Ego4D Workshop; 3rd place in Ego4D Moment Query Challeng

Action Sensitivity Learning for the Ego4D Episodic Memory Challenge 2023

This report presents ReLER submission to two tracks in the Ego4D Episodic Memory Benchmark in CVPR 2023, including Natural Language Queries and Moment Queries. This solution inherits from our proposed Action Sensitivity Learning framework (ASL) to better capture discrepant information of frames. Further, we incorporate a series of stronger video features and fusion strategies. Our method achieves an average mAP of 29.34, ranking 1st in Moment Queries Challenge, and garners 19.79 mean R1, ranking 2nd in Natural Language Queries Challenge. Our code will be released.Comment: Accepted to CVPR 2023 Ego4D Workshop; 1st in Ego4D Moment Queries Challenge; 2nd in Ego4D Natural Language Queries Challeng

Midwave Infrared Quantum Dot Avalanche Photodiode

We report the first demonstration of a GaAs based avalanche photodiode (APD) operating in the midwave infrared region . In the device, called the quantum dot avalanche photodiode, an intersubband quantum dots-in-a-well detector is coupled with an APD through a tunnel barrier. Using this approach, we have increased the photocurrent and reached a conversion efficiency of 12%, which is one of the highest reported conversion efficiencies for any quantum dot detector

Increased normal incidence photocurrent in quantum dot infrared photodetectors

We have increased the ratio of s-polarization (normal incidence) to p-polarization photocurrent to 50% in a quantum dot-in-a-well based infrared photodetector form the typical s-p polarization ratio about 20%. This improvement was achieved by engineering the dot geometry and the quantum confinement via post growth capping materials of the Stranski Krastanov growth mode quantum dots (QDs). The TEM images show that the height to base ratio of shape engineered QDs was increased to 8 nm/12 nm from the control sample\u27s ratio 4 nm/17 nm. The dot geometry correlates with the polarized photocurrent measurements of the detector. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4764905

Homogeneous AlGaN/GaN superlattices grown on free-standing (1(1)over-bar00) GaN substrates by plasma-assisted molecular beam epitaxy

Two-dimensional and homogeneous growth of m-plane AlGaN by plasma-assisted molecular beam epitaxy has been realized on free-standing (1 (1) over bar 00) GaN substrates by implementing high metal-to-nitrogen (III/N) flux ratio. AlN island nucleation, often reported for m-plane AlGaN under nitrogen-rich growth conditions, is suppressed at high III/N flux ratio, highlighting the important role of growth kinetics for adatom incorporation. The homogeneity and microstructure of m-plane AlGaN/GaN superlattices are assessed via a combination of scanning transmission electron microscopy and high resolution transmission electron microscopy (TEM). The predominant defects identified in dark field TEM characterization are short basal plane stacking faults (SFs) bounded by either Frank-Shockley or Frank partial dislocations. In particular, the linear density of SFs is approximately 5 x 10(-5) cm(-1), and the length of SFs is less than 15 nm. (C) 2013 AIP Publishing LLC

Action Sensitivity Learning for Temporal Action Localization

Temporal action localization (TAL), which involves recognizing and locating action instances, is a challenging task in video understanding. Most existing approaches directly predict action classes and regress offsets to boundaries, while overlooking the discrepant importance of each frame. In this paper, we propose an Action Sensitivity Learning framework (ASL) to tackle this task, which aims to assess the value of each frame and then leverage the generated action sensitivity to recalibrate the training procedure. We first introduce a lightweight Action Sensitivity Evaluator to learn the action sensitivity at the class level and instance level, respectively. The outputs of the two branches are combined to reweight the gradient of the two sub-tasks. Moreover, based on the action sensitivity of each frame, we design an Action Sensitive Contrastive Loss to enhance features, where the action-aware frames are sampled as positive pairs to push away the action-irrelevant frames. The extensive studies on various action localization benchmarks (i.e., MultiThumos, Charades, Ego4D-Moment Queries v1.0, Epic-Kitchens 100, Thumos14 and ActivityNet1.3) show that ASL surpasses the state-of-the-art in terms of average-mAP under multiple types of scenarios, e.g., single-labeled, densely-labeled and egocentric.Comment: Accepted to ICCV 202

A Bit More Bayesian: Domain-Invariant Learning with Uncertainty

Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data. In this paper, we address both challenges with a probabilistic framework based on variational Bayesian inference, by incorporating uncertainty into neural network weights. We couple domain invariance in a probabilistic formula with the variational Bayesian inference. This enables us to explore domain-invariant learning in a principled way. Specifically, we derive domain-invariant representations and classifiers, which are jointly established in a two-layer Bayesian neural network. We empirically demonstrate the effectiveness of our proposal on four widely used cross-domain visual recognition benchmarks. Ablation studies validate the synergistic benefits of our Bayesian treatment when jointly learning domain-invariant representations and classifiers for domain generalization. Further, our method consistently delivers state-of-the-art mean accuracy on all benchmarks.Comment: accepted to ICML 202

Surface morphology evolution of m-plane (1(1)over-bar00) GaN during molecular beam epitaxy growth: Impact of Ga/N ratio, miscut direction, and growth temperature

We present a systematic study of morphology evolution of [1 (1) over bar 00] m-plane GaN grown by plasma-assisted molecular beam epitaxy on free-standing m-plane substrates with small miscut angles towards the -c [000 (1) over bar] and +c [0001] directions under various gallium to nitrogen (Ga/N) ratios at substrate temperatures T = 720 degrees C and T = 740 degrees C. The miscut direction, Ga/N ratio, and growth temperature are all shown to have a dramatic impact on morphology. The observed dependence on miscut direction supports the notion of strong anisotropy in the gallium adatom diffusion barrier and growth kinetics. We demonstrate that precise control of Ga/N ratio and substrate temperature yields atomically smooth morphology on substrates oriented towards _c [0001] as well as the more commonly studied -c [000 (1) over bar] miscut substrates. (C) 2013 AIP Publishing LLC

Bile acid signalling and its role in anxiety disorders

Anxiety disorder is a prevalent neuropsychiatric disorder that afflicts 7.3%~28.0% of the world’s population. Bile acids are synthesized by hepatocytes and modulate metabolism via farnesoid X receptor (FXR), G protein-coupled receptor (TGR5), etc. These effects are not limited to the gastrointestinal tract but also extend to tissues and organs such as the brain, where they regulate emotional centers and nerves. A rise in serum bile acid levels can promote the interaction between central FXR and TGR5 across the blood-brain barrier or activate intestinal FXR and TGR5 to release fibroblast growth factor 19 (FGF19) and glucagon-like peptide-1 (GLP-1), respectively, which in turn, transmit signals to the brain via these indirect pathways. This review aimed to summarize advancements in the metabolism of bile acids and the physiological functions of their receptors in various tissues, with a specific focus on their regulatory roles in brain function. The contribution of bile acids to anxiety via sending signals to the brain via direct or indirect pathways was also discussed. Different bile acid ligands trigger distinct bile acid signaling cascades, producing diverse downstream effects, and these pathways may be involved in anxiety regulation. Future investigations from the perspective of bile acids are anticipated to lead to novel mechanistic insights and potential therapeutic targets for anxiety disorders