Two-dimensional (2D) semiconductors are among the best candidates for next-generation nanoelectronics, effectively addressing the issues encountered with transistors smaller than 1 nm. Wafer-scale 2D metal-doped semiconductor materials such as Fe-MoS2 and Fe-WS2 have been successfully developed and synthesized. Before applying these 2D materials to integrated circuits, fast and non-destructive determination of the doping concentration becomes a core issue and a major challenge. In this study, a new method for doping concentration characterization was developed using Mueller matrix spectroscopy, which is a fast and non-destructive method. Centimeter-scale monolayer MoS2 films doped with different Fe concentrations were synthesized via chemical vapor deposition. The Mueller matrix spectra (300–1000 nm) of monolayer Fe-MoS2 thin films with different doping concentrations were measured using two ellipsometry methods, and the doping concentration was classified using single-channel deep learning with an accuracy of 93%. Finally, multi-channel training was performed using multiple valid Mueller matrix elements to improve the recognition accuracy. The proposed method should advance the development of 2D semiconductors on integrated circuits.

The Cu-filling process in through-silicon via (TSV-Cu) is a key technology for chip stacking and three-dimensional vertical packaging. During this process, defects resulting from chemical–mechanical planarization (CMP) and annealing severely affect the reliability of the chips. Traditional methods of defect characterization are destructive and cumbersome. In this study, a new defect inspection method was developed using Mueller matrix spectroscopic ellipsometry. TSV-Cu with a 3-μm-diameter and 8-μm-deep Cu filling showed three typical types of characteristics: overdishing (defect-OD), protrusion (defect-P), and defect-free. The process dimension for each defect was 13 nm. First, the three typical defects caused by CMP and annealing were investigated. With single-channel deep learning and a Mueller matrix element (MME), the TSV-Cu defect types could be distinguished with an accuracy rate of 99.94%. Next, seven effective MMEs were used as independent channels in the artificial neural network to quantify the height variation in the Cu filling in the z-direction. The accuracy rate was 98.92% after training, and the recognition accuracy reached 1 nm. The proposed approach rapidly and nondestructively evaluates the annealing bonding performance of CMP processes, which can improve the reliability of high-density integration.

To enable high-density optical storage, better storage media structures, diversified recording methods, and improved accuracy of readout schemes should be considered. In this study, we propose a novel three-dimensional (3D) sloppy nanostructure as the optical storage device, and this nanostructure can be fabricated using the 3D laser direct writing technology. It is a 900 nm high, 1 × 2 µm wide Si slope on a 200 nm SiO2 layer with 200 nm Si3N4 deposited on top to enhance reflectivity. In this study, we propose a reflected spectrum-based method as the readout recording strategy to stabilize information readout more stable. The corresponding reflected spectrum varied when the side wall angle of the slope and the azimuth angle of the nanostructure were tuned. In addition, an artificial neural network was applied to readout the stored information from the reflected spectrum. To simulate the realistic fabrication error and measurement error, a 20% noise level was added to the study. Our findings showed that the readout accuracy was 99.86% for all 120 data sequences when the slope and azimuth angle were varied. We investigated the possibility of a higher storage density to fully demonstrate the storage superiority of this designed structure. Our findings also showed that the readout accuracy can reach its highest level at 97.25% when the storage step of the encoded structure becomes 7.5 times smaller. The study provides the possibility to further explore different nanostructures to achieve high-density optical storage.

State-of-the-art autonomous micro-robotic turtles suffer from various limitations, such as power restrictions that minimize their deployment times. In this paper, an Ionic Polymer Metal Composite (IPMC) actuator-based centimeter-level biomimetic underwater robot was designed and developed as a robotic turtle with self-charging capabilities to overcome such limitations. It could move forward and make turns driven by five IPMCs on the water. The underwater charging station was able to transmit wideband ultrasonic and electromagnetic fields for electromagnetic induction charging. An ultrasonic communication system with one ultrasonic transmitter and two ultrasonic receivers was first fabricated to implement communication between the underwater station and the biomimetic underwater robot for autonomous tracking and rechargeable capabilities. Experiments were carried out to confirm the operation of the biomimetic underwater robot, which verified the centimeter-level rechargeable capabilities and autonomous target tracking features. The micro-robot demonstrated a self-tracking radial displacement error of approximately 6 mm and a charging reliability rate of more than 73%.