Reliability of Cu-to-Cu direct bonging: In order to realize the reliability of Cu-to-Cu direct bonding interconnects, we adopted nanotwinned Cu (nt-Cu) electroplating process to fabricate nt-Cu microbump for Cu-to-Cu direct bonding. Figure (a) is the photo of chip after bonding. Underfill (UF) dispensing was used to protect Cu microbumps. The cross-section SEM image of Cu microbump is shown in figure (b). Some chips underwent the reliability tests, including temperature cycling test (TCT) and electromigration (EM) test. To further study the failure mechanism, finite element analysis (FEA) was done to know the stress and current distribution during reliability test. This research could provide fundamental understanding for Cu-Cu joints with organic dielectric hybrid bonding.

1.2 kV SiC Super Junction MOSFET: We have developed the first 1.2 kV SJ MOSFET in Taiwan in 2020. Its threshold voltage is 3.5 V, saturation current is about 12 A, and the specific on resistance is 8mΩ*cm2.

Tool wear and health condition monitoring during the processing: The tool wear monitoring technology developed by our researching team is specifically designed to analyze whether the tool is broken, collapsed, etc., and to estimate the remaining useful life(RUL) of the tool according to the working conditions of mass processing. By acquiring the vibration signal data with three-axis accelerometers installed on the machine tool, this technology could determine whether the current tool cutting vibration has exceeded the safety range by plotting a control chart. Once it exceeds the safe range, the current tool processing state will be assumed as abnormal. It gives users a reference to replace the broken tools immediately to prevent continuing processing, which causes vast loss such as poor quality of workpieces. In addition, this technology allows users to build models for distinct working conditions to predict the RUL of tools. It could allow users to evaluate the current health condition of tools and schedule the time to change the tool.

The DNN models of this technology include a LithoNet, an OPCNet, and a layout novelty detection network. LithoNet is a learning-based pre-simulation model for layout-to-SEM contour prediction, and OPCnet is a dual network of LithoNet for photomask optimization. Integrated with a well-trained LithoNet, our layout novelty detection network, consisting of a self-attention guided LithoNet and an autoencoder, can check if there are layout patterns easily resulting in local distortions in contours of metal lines based on multi-modal (global-local) feature fusion.

Our system provides system identification, servo tuning, and feed axis diagnosis for machine tools. Also, executing remote function verification, and data collection are included. All these functions were actually tested verified by the co-operation manufacturer. It help operators or engineers to maintain and adjust the machine so that the feed drive system match. During this critical time of the COVID-19 epidemic, the proposed system provides a zero-touch remote diagnostic function and method for engineers to maintain the machines in the processing plant can effectively reduce costs and time.

This technology aims to automatically extract and define features from a data-driven framework of data engineering, machine learning, and ensemble learning algorithms to analyzing the big data collected from equipment sensors and quality characteristic measurement results. This technology provides prediction results of unmeasured products quality characteristics and its confident score base on the extracted feature for advanced process control.

With the rapid advancement of artificial intelligenceIoT, many solutions have been successfully implemented. However, for very large biomedical image computations, such as MRI’s, the deep learning/ training will be a lot more time consuming.

This project proposes a learning from demonstration (LfD) system that allows robots to be not only taught by human via demonstration but also adjusted by themselves.

With the rapid advancement of artificial intelligence and IoT, many solutions have been successfully implemented. However, for very large biomedical image computations, such as MRI’s, the deep learning/ training will be a lot more time consuming. At the moment, GPU’s are considered the baby of the AI world, Google has also developed a TPU to expedite AI procedures. Both GPU and TPU make the process swift. Prof. Darsen Lu’s Research Team has built an open simulation platform called (simNemo) to enable design exploration by academic and industry users in Taiwan on both the AI platform and AI application fronts.

With the rapid advancement of artificial intelligenceIoT, many solutions have been successfully implemented. However, for very large biomedical image computations, such as MRI’s, the deep learning/ training will be a lot more time consuming.