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Complex processes in science and engineering are often formulated as multistage decision-making problems. In this paper, we consider a type of multistage decision-making process called a cascade process. A cascade process is a multistage process in which the output of one stage is used as an input for the subsequent stage. When the cost of each stage is expensive, it is difficult to search for the optimal controllable parameters for each stage exhaustively. To address this problem, we formulate the optimization of the cascade process as an extension of the Bayesian optimization framework and propose two types of acquisition functions based on credible intervals and expected improvement. We investigate the theoretical properties of the proposed acquisition functions and demonstrate their effectiveness through numerical experiments. In addition, we consider an extension called suspension setting in which we are allowed to suspend the cascade process at the middle of the multistage decision-making process that often arises in practical problems. We apply the proposed method in a test problem involving a solar cell simulator, which was the motivation for this study., 70pages, 7 figures

In material characterization, identifying defective areas on a material surface is fundamental. The conventional approach involves measuring the relevant physical properties point-by-point at the predetermined mesh grid points on the surface and determining the area at which the property does not reach the desired level. To identify defective areas more efficiently, we propose adaptive mapping methods in which measurement resources are used preferentially to detect the boundaries of defective areas. We interpret this problem as an active-learning (AL) of the level set estimation (LSE) problem. The goal of AL-based LSE is to determine the level set of the physical property function defined on the surface with as small number of measurements as possible. Furthermore, to handle the situations in which materials with similar specifications are repeatedly produced, we introduce a transfer learning approach so that the information of previously produced materials can be effectively utilized. As a proof-of-concept, we applied the proposed methods to the red-zone estimation problem of silicon wafers and demonstrated that we could identify the defective areas with significantly lower measurement costs than those of conventional methods.

Three-dimensional distribution of arsenic (As) dopants at Σ3{111}, Σ9{221}, Σ9{114}, and Σ9{111}/{115} grain boundaries (GBs) in silicon (Si) is examined by correlative analytical methods using atom probe tomography (APT) combined with low-temperature focused ion beam (LT-FIB), scanning transmission electron microscopy, and ab initio calculations. Σ3{111} GBs, consisting of only 6-membered rings with small bond distortions, do not exhibit an apparent As segregation. Meanwhile, it is hypothesized that As atoms would segregate at 5-membered rings in the other GBs via anisotropic bond distortions spontaneously introduced so as to lower the donor level, as Jahn-Teller distortions. In addition, APT combined with LT-FIB suggests preferential As segregation around stretched 11‾0 bonds reconstructed in Σ9{114} and Σ9{111}/{115} GBs, that are inevitably introduced in the 11‾0 tilt GBs with the tilt angle larger than 70.5°. It is hypothesized that As atoms would form As dimers at stretched 11‾0 bonds and the adjacent ⟨111⟩ bonds, due to the tendency of As with five valence electrons to form a three-coordinated configuration, which is efficiently attained by an As dimer of a long length. This work provides important insights into As segregation at GBs; it is mainly determined by electronic interactions depending on the characteristics of valence electrons of As atoms, as well as on local bond distortions at GBs, via anisotropic bond distortions and dimerization.

We demonstrate an improved annealing procedure to suppress defect generation during impurity gettering process. A multiple cycled annealing and cooling for impurity gettering provides higher carrier lifetime in the wafers compared with continuously annealed wafers (conventional method). Microscopic photoluminescence images revealed that dislocation propagation from grown-in dislocations and defect generation in intra grain were suppressed in samples with the multiple cycled annealing. Therefore the multiple cycled annealing procedure is concluded to be a promising technique to improve electrical property of multicrystalline silicon for solar cells.