A nonlithographic second-order self-assembly process for synthesizing uniform and ordered arrays of nanorods and nanodots is presented and applied to the fabrication of ZnO nanorod arrays. Nucleation sites were defined by patterning Au nanodot catalysts with a self-organized array of nanopores formed in anodized aluminum oxide (AAO). The self-assembled vertically aligned ZnO nanorods grown on GaN exhibit hexagonal facets, and have a uniform diameter of 60 nm and a mean length of 400 nm. The growth technique is simple, robust, and offers a direct control over array and single nanorod configurations. The growth temperature is significantly lower than normal, and yet, the resultant defect level is much lower than normal.

We report a complete deep-learning framework using a single-step object detection model in order to quickly and accurately detect and classify the types of manufacturing defects present on Printed Circuit Board (PCBs). We describe the complete model architecture and compare with the current state-of-the-art using the same PCB defect dataset. These benchmark methods include the Faster Region Based Convolutional Neural Network (FRCNN) with ResNet50, RetinaNet, and You-Only-Look-Once (YOLO) for defect detection and identification. Results show that our method achieves a 98.1% mean average precision(mAP[IoU = 0.5]) on the test samples using low-resolution images. This is 3.2% better than the state-of-the-art using low-resolution images (YOLO V5m) and 1.4% better than the state-of-the-art using high-resolution images (FRCNN-ResNet FPN). While achieving better accuracies, our model also requires roughly 3× fewer model parameters (7.02M) compared with the state-of-the-art FRCNN-ResNet FPN (23.59M) and YOLO V5m (20.08M). In most cases, the major bottleneck of the PCB manufacturing chain is quality control, reliability testing and manual rework of defective PCBs. Based on the initial results, we firmly believe that implementing this model on a PCB manufacturing line could significantly increase the production yield and throughput, while dramatically reducing manufacturing costs.

The engineering of quantum dot solids with low defect concentrations and efficient carrier transport through a ligand strategy is crucial to achieve efficient quantum dot (QD) optoelectronic devices. Here, we study the consequences of various surface ligand treatments on the light emission properties of PbS quantum dot films using 1,3-benzenedithiol (1,3-BDT), 1,2-ethanedithiol (EDT), mercaptocarboxylic acids (MPA) and ammonium sulfide ((NH4)2S). We first investigate the influence of different ligand treatments on the inter-dot separation, which mainly determines the conductivity of the QD films. Then, through a combination of photoluminescence and transient photoluminescence characterization, we demonstrate that the radiative and non-radiative recombination mechanisms in the quantum dot films depend critically on the length and chemical structure of the surface ligands.