RESUMEN
Though micro-light-emitting diode (micro-LED) displays are regarded as the next-generation emerging display technology, challenges such as defects in LED's light output power and radiation patterns are critical to the commercialization success. Here we propose an electroluminescence mass detection method to examine the light output quality from the on-wafer LED arrays before they are transferred to the display substrate. The mass detection method consists of two stages. In the first stage, the luminescent image is captured by a camera by mounting an ITO (indium-tin oxide) transparent conducting glass on the LED wafer. Due to the resistance of the ITO contact pads and on-wafer n-type electrodes, we develop a calibration method based on the circuit model to predict the current flow on each LED. The light output power of each device is thus calibrated back by multi-variable regression analysis. The analysis results in an average variation as low as 6.89% for devices predicted from luminescent image capturing and actual optical power measurement. We also examine the defective or non-uniform micro-LED radiation profiles by constructing a 2-D convolutional neural network (CNN) model. The optimized model is determined among three different approaches. The CNN model can recognize 99.45% functioning LEDs, and show a precision of 96.29% for correctly predicting good devices.
RESUMEN
The decrease of light output efficiency with the reduction of LED (light-emitting diode) die size is one of the challenges of micro-LED displays. Here we propose a digital etching technology that employs multi-step etching and treatment to mitigate sidewall defects exposed after mesa dry etching. In this study, by two-step etching and N2 treatment, the electrical properties of the diodes show an increase of forward current and a decrease in reverse leakage due to suppressed sidewall defects. An increase of light output power by 92.6% is observed for 10 × 10-µm2 mesa size with digital etching, as compared with that with only one step etching and no treatment. We also demonstrated only 1.1% decrease in output power density for a 10 × 10-µm2 LED as compared with a 100 × 100-µm2 device without performing digital etching.
RESUMEN
Recent advancements in deep learning have led to widespread applications of its algorithms to synthetic planning and reaction predictions in the field of chemistry. One major area, known as supervised learning, is being explored for predicting certain properties such as reaction yields and types. Many chemical descriptors known as fingerprints are being explored as potential candidates for reaction properties prediction. However, there are few studies that describe the permutational invariance of chemical fingerprints, which are concatenated at some stage before being fed to deep learning architecture. In this work, we show that by utilizing permutational invariance, we consistently see improved results in terms of accuracy relative to previously published studies. Furthermore, we are able to accurately predict hydrogen peroxide loss with our own dataset, which consists of more than 20 ingredients in each chemical formulation.
