Artificial intelligence deep learning for 3D IC reliability prediction.

Three-dimensional integrated circuit (3D IC) technologies have been receiving much attention recently due to the near-ending of Moore's law of minimization in 2D IC. However, the reliability of 3D IC, which is greatly influenced by voids and failure in interconnects during the fabrication processes, typically requires slow testing and relies on human's judgement. Thus, the growing demand for 3D IC has generated considerable attention on the importance of reliability analysis and failure prediction. This research conducts 3D X-ray tomographic images combining with AI deep learning based on a convolutional neural network (CNN) for non-destructive analysis of solder interconnects. By training the AI machine using a reliable database of collected images, the AI can quickly detect and predict the interconnect operational faults of solder joints with an accuracy of up to 89.9% based on non-destructive 3D X-ray tomographic images. The important features which determine the "Good" or "Failure" condition for a reflowed microbump, such as area loss percentage at the middle cross-section, are also revealed.

On-chip thin film Zernike phase plate for in-focus transmission electron microscopy imaging of organic materials.

Transmission electron microscopy (TEM) is a powerful tool for imaging nanostructures, yet its capability is limited with respect to the imaging of organic materials because of the intrinsic low contrast problem. TEM phase plates have been in development for decades, yet a reliable phase plate technique has not been available because the performance of TEM phase plates deteriorates too quickly. Such an obstacle prohibits in-focus TEM phase imaging to be routinely achievable, thus limiting the technique being used in practical applications. Here we present an on-chip thin film Zernike phase plate which can effectively release charging and allow reliable in-focus TEM images of organic materials with enhanced contrast to be routinely obtained. With this stable system, we were able to characterize many polymer solar cell specimens and consequently identified and verified the existence of an unexpected nanoparticle phase. Furthermore, we were also able to observe the fine structures of an Escherichia coli specimen, without staining, using this on-chip thin film phase plate. Our system, which can be installed on a commercial TEM, opens up exciting possibilities for TEM to characterize organic materials.

Quantitative characterization of nanoparticles in blood by transmission electron microscopy with a window-type microchip nanopipet.

Transmission electron microscopy (TEM) is a unique and powerful tool for observation of nanoparticles. However, due to the uneven spatial distribution of particles conventionally dried on copper grids, TEM is rarely employed to evaluate the spatial distribution of nanoparticles in aqueous solutions. Here, we present a microchip nanopipet with a narrow chamber width for sorting nanoparticles from blood and preventing the aggregation of the particles during the drying process, enabling quantitative analysis of their aggregation/agglomeration states and the particle concentration in aqueous solutions. This microchip is adaptable to all commercial TEM holders. Such a nanopipet proves to be a simple and convenient sampling device for TEM image-based quantitative characterization.