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Detection and Prediction of Chipping in Wafer Grinding Based on Dicing Signal

by Bao Rong Chang, Hsiu-Fen Tsai and Hsiang-Yu Mo

Mathematics 2022, 10(24), 4631; https://doi.org/10.3390/math10244631 - 7 Dec 2022

Cited by 4 | Viewed by 3438

Simple regression cannot wholly analyze large-scale wafer backside wall chipping because the wafer grinding process encounters many problems, such as collected data missing, data showing a non-linear distribution, and correlated hidden parameters lost. The objective of this study is to propose a novel approach to solving this problem. First, this study uses time series, random forest, importance analysis, and correlation analysis to analyze the signals of wafer grinding to screen out key grinding parameters. Then, we use PCA and Barnes-Hut t-SNE to reduce the dimensionality of the key grinding parameters and compare their corresponding heat maps to find out which dimensionality reduction method is more sensitive to the chipping phenomenon. Finally, this study imported the more sensitive dimensionality reduction data into the Data Driven-Bidirectional LSTM (DD-BLSTM) model for training and predicting the wafer chipping. It can adjust the key grinding parameters in time to reduce the occurrence of large-scale wafer chipping and can effectively improve the degree of deterioration of the grinding blade. As a result, the blades can initially grind three pieces of the wafers without replacement and successfully expand to more than eight pieces of the wafer. The accuracy of wafer chipping prediction using DD-BLSTM with Barnes-Hut t-SNE dimensionality reduction can achieve 93.14%. Full article

(This article belongs to the Special Issue From Edge Devices to Cloud Computing and Datacenters: Emerging Machine Learning Applications, Algorithms, and Optimizations)

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9 pages, 2805 KB

Open AccessArticle

3200 ppi Matrix-Addressable Blue MicroLED Display

by Meng-Chyi Wu, Ming-Che Chung and Cheng-Yeu Wu

Micromachines 2022, 13(8), 1350; https://doi.org/10.3390/mi13081350 - 19 Aug 2022

Cited by 24 | Viewed by 5001

Abstract

In this article, an active matrix (AM) micro light-emitting diode (MicroLED) display with a resolution of 1920 × 1080 and a high pixel density of 3200 pixels per inch (ppi) is reported. The single pixel with a diameter of 5 μm on the MicroLED array exhibits excellent characteristics, including a forward voltage of 2.8 V at 4.4 μA, an ideality factor of 1.7 in the forward bias of 2–3 V, an extremely low leakage current of 131 fA at −10 V, an external quantum efficiency of 6.5%, and a wall-plug efficiency of 6.6% at 10.2 A/cm², a light output power of 28.3 μW and brightness of 1.6 × 10⁵ cd/m² (nits) at 1 mA. The observed blue shift in the electroluminent peak wavelength is only 6.6 nm from 441.2 nm to 434.6 nm with increasing the current from 5 μA to 1 mA (from 10 to 5 × 10³ A/cm²). Through flip-chip bonding technology, the 1920 × 1080 bottom-emitting MicroLED display through the backside of a sapphire substrate can demonstrate high-resolution graphic images. Full article

(This article belongs to the Special Issue Micro-Light Emitting Diode: From Chips to Applications)

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