In this study, we investigated the effect of the post-etch cleaning of GaAs surfaces. We found that a plasma damage layer was formed on GaAs surfaces by dry etching, and an As-rich layer remained after post-etch cleaning. The As rich layer needs to be removed because it is replaced by micron-sized particles when stored in an air. We also found that a pure GaAs surface can be obtained by performing additional cleaning consisting of oxide formation and removal.

This presentation focuses on semiconductor wafer cleaning technology, one of the most critical technologies in semiconductor device manufacturing for obtaining high yield and reliability, and discusses the past, present, and future of the technology. Emphasis is placed on the review of contamination control and cleaning technologies in the early days since the invention of the transistor. To celebrate the 30+1-year anniversary of the UCPSS, a review will be given of both the first conference held in Leuven in 1992 and the second one held in Bruges in 1994. There will be more research challenges and business opportunities in environmentally benign, innovative damage-free wafer cleaning and surface preparation technologies for future applications.

In this paper, we present a new force field (FF) parameterization method with direct matching of crystal structures and atomic charges optimization in end-to-end differentiable manner. The advancement of force field (FF) parameterization methods has been accelerated by differentiable programming. Automatic differentiation (AD) has facilitated energy and force matching by differentiating these quantities with respect to the FF parameters, which we mention as force differentiation and matching (FDM). Nevertheless, crystal structure matching with AD remains difficult due to the converged structures optimized by the iterative algorithm being non-differentiable with respect to the FF parameters. To overcome this limitation, we introduce structure differentiation and matching (SDM) technique for generating FFs of small organic molecules using reference data, including stable monomer structures, crystal structures, lattice energies, and potential energy surfaces (PESs) of dihedral angles. SDM employs implicit function differentiation (IFD) and differentiable Ewald techniques to optimize FF parameters and atomic charges correspondingly. Our case study of eight exemplified molecules demonstrates that SDM substantially outperforms the conventional FDM, with error factors reduced to less than one-quarter with the charge optimization method called SDM(q-opt). This strategy achieves remarkable precision in reproducing lattice constants, atomic configurations, lattice energies, and PESs. Furthermore, molecular dynamics simulations confirm the stability of the generated crystal structures. This method can be adapted to other FF categories, such as polarized FFs and those with explicit hydrogen bonding interactions. We foresee that SDM(q-opt) will emerge as a standard method for parameterizing FFs using crystal structures.

Atomic layer etching (ALE) has been practically implemented as a technique to achieve atomic/molecular level control. However, its main disadvantage is that it involves long process time. The surface reaction mechanism required to realize high-throughput (HT) SiN ALE was investigated. CH3F/Ar plasma was applied in the adsorption step, while Ar plasma was used in the desorption step. Finally, an additional O2 ashing step was applied. To reduce process time, HT ALE was performed at high ion energy, and the amount of etched SiN was evaluated. HT SiN ALE conducted at short time intervals and at high ion energy underwent a quasi-self-limited reaction, which is a characteristic of ALE, and the process time decreased. However, HT ALE using CH3F in the adsorption step caused an increase in the extent of the damage. Thus, the use of C4F8 (without H) can significantly reduce damage even under HT ALE conditions.

We report the development of a novel line-scanning microscope capable of acquiring high-speed time-correlated single-photon counting (TCSPC)-based fluorescence lifetime imaging microscopy (FLIM) imaging. The system consists of a laser-line focus, which is optically conjugated to a 1024 × 8 single-photon avalanche diode (SPAD)-based line-imaging complementary metal-oxide semiconductor (CMOS), with 23.78 µm pixel pitch at 49.31% fill factor. Incorporation of on-chip histogramming on the line-sensor enables acquisition rates 33 times faster than our previously reported bespoke high-speed FLIM platforms. We demonstrate the imaging capability of the high-speed FLIM platform in a number of biological applications.

Differentiable programming has accelerated the development of force-field (FF) parameterization techniques. Specifically, automatic differentiation (AD) facilitates energy and force matching by differentiating them with respect to the FF parameters; hereinafter, referred to as force differentiation and matching (FDM). Conversely, crystal structure matching with AD has persisted as a challenge because the converged structures optimized by the iterative algorithm cannot be differentiated with respect to the FF parameters. Therefore, in this paper, we propose a structure differentiation and matching (SDM) method, wherein the converged structures are directly differentiated using the parameters with implicit function differentiation and matched with the experimental crystal structures. Subsequently, with a case study, we compared the reproducibility of the crystal structures, internal atomic coordinates, and lattice energies on eight exemplary molecules with the differentiable Ewald method for long-range interactions. The results indicated that SDM outperformed FDM on all three criteria. The FFs generated by SDM reproduced the lattice constants with a mean error of 0.56 %, the internal atomic coordinates with an error of 0.16 Angstrom, and the lattice energies with an error of 0.14 kcal/mol. The corresponding accuracies obtained with FDM were 1.2 %, 0.22 Angstrom, and 2.40 kcal/mol, respectively. Furthermore, we performed molecular dynamic simulations on a supercell, containing more than 3000 atoms, to confirm if the crystal structures were preserved under temperature fluctuations at 300 K.Overall, this method is not limited to Amber-type FFs and can be easily applied to the other types of FFs. Thus, we believe that SDM will emerge as one of the new standards for parameterizing FFs with crystal structures.

This paper describes the following two points. (1) A proposal for the method of derivation for the group egogram, (2) a confirmation that the derived group egogram can be interpreted and used in the same way as the individual egogram. The group egogram is a method of numeric expression for group’s character. In this study, we combined two methods for quantification of group’s character: the “egograms” and the “group decision-making stress method.” The egogram is a method used to analyze an individual’s character, while the stress method is a type of group decision making support method. In the field of team sports research, the egogram has been used for team analysis, and the team’s egogram is derived from the arithmetic mean of the egograms of each individual team member. However, the shape of the graph produced by the five indicators is important for the egogram. Therefore, it is not appropriate to use simple arithmetic averages with other member’s egograms that differ in overall scale. For this reason, it is necessary to find a method to compare the graphs with the same scale without changing their shape. In mathematical views, the stress method can “align the scales of everyone’s egogram while keeping the meaning (shape) of each individual’s egogram, and then take the average.” In other words, we can derive the group’s egogram without contradiction mathematically. We use these methods to establish a method for deriving the “group egogram,” which gives a numeric expression for group’s character. In addition, we conduct evaluation experiments to confirm whether the mathematically derived group egogram really expresses the character of the group. The four evaluation items are “the risk taking tendency,” “the team orientation,” “the punctual tendency,” and “the harmony tendency.” As a result, we were able to confirm effectiveness of the group egogram on all evaluation items.