Chip-level mass detection for micro-LED displays based on regression analysis and deep learning.

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.

Permutationally Invariant Deep Learning Approach to Molecular Fingerprinting with Application to Compound Mixtures.

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.