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The assessment of embryo viability for in vitro fertilization (IVF) is mainly based on subjective visual analysis, with the limitation of intra- and inter-observer variation and a time-consuming task. In this study, we used deep learning with large dataset of microscopic embryo images to develop an automated grading system for embryo assessment. This study included a total of 171,239 images from 16,201 embryos of 4,146 IVF cycles at Stork Fertility Center (https://www.e-stork.com.tw) from March 6, 2014 to April 13, 2018. The images were captured by inverted microscope (Zeiss Axio Observer Z1) at 112 to 116 hours (Day 5) or 136 to 140 hours (Day 6) after fertilization. Using a pre-trained network trained on the ImageNet dataset as convolution base, we applied Convolutional Neural Network (CNN) on embryo images, using ResNet50 architecture to fine-tune ImageNet parameters. The predicted grading results was compared with the grading results from trained embryologists to evaluate the model performance. The images were labeled by trained embryologists, based on Gardner’s grading system: blastocyst development ranking from 3–6, ICM quality as A, B, or C; and TE quality as a, b, or c. After pre-processing, the images were divided into training, validation, and test groups, in which 60% were allocated to the training group, 20% to the validation group, and 20% to the test group. The ResNet50 algorithm was trained on the 60% images allocated to the training group, and the algorithm’s performance was evaluated using the 20% images allocated to the test group. The results showed an average predictive accuracy of 75.36% for the all three grading categories: 96.24% for blastocyst development, 91.07% for ICM quality, and 84.42% for TE quality. To the best of our knowledge, this is the first study of an automatic embryo grading system using large dataset from Asian population. Combing the promising results obtained in this study with time-lapse microscope system integrated with IVF Electronic Medical Record platform, a fully automated and non-invasive pipeline for embryo assessment will be achieved.

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We reported amorphous Indium-Gallium-Zinc-Oxide (a-IGZO) TFT OLED TV panel development in AUO. The Generation 6 (G6) threshold voltage uniformity can be lower than 0.8V. The RGB side-by-side OLED patterning was realized by fine metal mask. By the application of a source follower connection pixel compensation circuit, the panels driven by AUO engine show excellent characteristics. For future product performance requirement, amorphous Indium-Tin-Zinc-Oxide (a-ITZO) TFT was developing, high mobility as 33.2 cm2/Vs was achieved. Metal-organic chemical vapor deposition (MOCVD) system was also used to improve the mobility of IGZO TFT. To improve manufacturing efficiency of OLED process, inkjet printing technology is also under development in AUO.

Augmented reality (AR) has started to become popular in our daily life, such as head up display, gaming, house decoration, and many other applications under exploration. Transparent AMOLED is regarded as one of best display technologies for AR applications. In this paper, a highly transparent 13 inch 100 PPI AMOLED was developed for this purpose. Through optimization of TFT array layer stack, OLED cathode pattern and encapsulation structure, 68% transmittance, low reflectance and low yellow index were achieved. With this highly transparent AMOLED, many AR applications can be realized in the future.

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Water‐filled, drop impact tests on full‐scale, blow‐molding polyester bottles showed that impact properties change significantly after 30 days of aging at 50°C. To quantify this deleterious effect of aging, we did small‐scale laboratory experiments using the essential work of fracture method (EWFM). We measured the essential work of fracture of thin specimens cut from the sides of blow‐molded bottles and aged under various conditions. Similar to the impact results, significant drops in essential work occurred after relatively short amounts of aging. Additional experiments confirmed that the decreases in essential work were due to physical aging and not to chemical degradation. A comparison of the essential work results to the full‐scale, bottle‐drop impact tests showed some inconsistencies that need to be resolved before EWFM can be recommended as a quantitative method for studying physical aging of blow‐molding polyester resins.

A new 5T2C pixel circuit is proposed in this study to compensate TFT Vth variation, power line IR-drop, and OLED degradation in LTPS PMOS process and it also has The Close Loop Negative Feedback Method (CL-NFM) to compensate the gate voltage distortion of the driving TFT during the emission phase. The simulation showed that the emission current error are 4.85%, 9.02% and 39.25% separated for high, middle and low grey level compared between the current value in 1ms and 16ms, which is 12.51%, 38.14% and 97.8% for the conventional pixel structure, respectively.