[9a-F211-1~12] 18.2 Process Informatics | The 87th JSAP Autumn Meeting 2026

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Session Details

Oral presentation18 Informatics : 18.2 Process Informatics

[9a-F211-1~12]18.2 Process Informatics

Wed. Sep 9, 2026 9:00 AM - 12:15 PM JST
Wed. Sep 9, 2026 12:00 AM - 3:15 AM UTC

F211 (Frontier Rsch. in Applied Sci. Bld.)

9:00 AM - 9:15 AM JST(12:00 AM - 12:15 AM UTC)

[9a-F211-1]Influence of intermediate parameters on multilayer thin film property prediction using multi-stage regression models

Soma Kondo¹, 〇Kentaro Kutsukake^1,2, Yasuyoshi Kurokawa^1,3,4, Shion Takeno¹, Ryoji Katsube¹, Noritaka Usami^1,2,4 (1.Grad. Eng. Nagoya Univ., 2.IMaSS, Nagoya Univ., 3.Grad. Sci. & Eng. Kansai Univ., 4.InFuS Nagoya Univ.)

9:15 AM - 9:30 AM JST(12:15 AM - 12:30 AM UTC)

[9a-F211-2]High-Reliability Optimization Method Using Automated Iterative Retraining of Surrogate Models: Application to SiC Solution Growth

〇Tomoki Harada¹, Toru Ujihara², Shota Seki^1,2 (1.Aixtal, 2.Nagoya Univ.)

9:30 AM - 9:45 AM JST(12:30 AM - 12:45 AM UTC)English Presentation

[9a-F211-3]Bayesian Optimisation for SiC Polishing Simulations Using SSIM

〇Roberto Iaconi¹, Nelson Quijada¹, Kevin Richard G. Operiano¹, Susumu Maeda¹, Fumiya Kawate¹, Saeed Sepasy², Yoshifumi Watanabe² (1.Aixtal Corporation, 2.Mipox Corporation)

9:45 AM - 10:00 AM JST(12:45 AM - 1:00 AM UTC)

[9a-F211-4]Proposal of Process Informatics Based on Parametric Testing
— Construction of a Quality Knowledge Platform through Integration of Equipment State Variables and Device Characteristics —

〇TAKEKI ANDO^1,2 (1.M&M International, 2.ONC Inc)

10:00 AM - 10:15 AM JST(1:00 AM - 1:15 AM UTC)Presentation by Applicant for JSAP Young Scientists Presentation Award

[9a-F211-5]Simulations with Temperature Conditions for Understanding Sintering Mechanisms

〇Nao Matsuya¹, Yoko Kasai¹, Syuzo Tsuchida¹, Taichi Nakamura¹ (1.Panasonic Holdings Co.)

10:15 AM - 10:30 AM JST(1:15 AM - 1:30 AM UTC)

[9a-F211-6]Building a Model to Predict Power Output of Perovskite Solar Cells
Using Weather and Atmospheric Parameters

〇Yuki Kasai¹, Raifuku Itaru¹, Ishikawa Yasuaki¹ (1.Aoyama Gakuin Univ.)

10:45 AM - 11:00 AM JST(1:45 AM - 2:00 AM UTC)

[9a-F211-7]Layer-selective and asymmetric reorganization of electrostatic-potential topology in aggressively scaled transistors

〇Hirokazu Fukidome¹, Haruto Nakamura¹, Daiki Tataka¹, Kento Shiga¹, Uran Yamamoto¹, Qiyue He¹, Yotaro Machida², Ryunosuke Nagaoka², Issei Watanabe³, Masato Kotsugi² (1.RIEC, Tohou Univ., 2.Tokyo Univ. Science, 3.NICT)

11:00 AM - 11:15 AM JST(2:00 AM - 2:15 AM UTC)

[9a-F211-8]Interpretable self driving sputtering: extracting growth rules from black-box optimization

〇Yuuki Wakabayashi¹, Yui Ogawa¹, Franz Benedict Romero¹, Coleman Wagner¹, Takuma Otsuka², Yoshitaka Taniyasu¹ (1.NTT BRL, 2.NTT CS Lab.)

11:15 AM - 11:30 AM JST(2:15 AM - 2:30 AM UTC)Presentation by Applicant for JSAP Young Scientists Presentation Award

[9a-F211-9]Development of a Beam-Pattern Control System for Solid-State Laser Resonators
Using Deep Reinforcement Learning

〇Yuki Ikeya¹, Takashi Sekine¹, Endo Tsubasa², Toshio Otsu², Takaaki Morita¹, Yoshinori Tamaoki¹, Yoshinori Kato¹, Toshiyuki Kawashima¹, Yohei Kobayashi² (1.Hamamatsu Photonics K.K., 2.ISSP, Univ.Tokyo.)

11:30 AM - 11:45 AM JST(2:30 AM - 2:45 AM UTC)Presentation by Applicant for JSAP Young Scientists Presentation Award

The 87th JSAP Autumn Meeting 2026

Session Details

[9a-F211-1~12]18.2 Process Informatics

[9a-F211-1]Influence of intermediate parameters on multilayer thin film property prediction using multi-stage regression models

[9a-F211-2]High-Reliability Optimization Method Using Automated Iterative Retraining of Surrogate Models: Application to SiC Solution Growth

[9a-F211-3]Bayesian Optimisation for SiC Polishing Simulations Using SSIM

[9a-F211-4]Proposal of Process Informatics Based on Parametric Testing
— Construction of a Quality Knowledge Platform through Integration of Equipment State Variables and Device Characteristics —

[9a-F211-5]Simulations with Temperature Conditions for Understanding Sintering Mechanisms

[9a-F211-6]Building a Model to Predict Power Output of Perovskite Solar Cells
Using Weather and Atmospheric Parameters

[9a-F211-7]Layer-selective and asymmetric reorganization of electrostatic-potential topology in aggressively scaled transistors

[9a-F211-8]Interpretable self driving sputtering: extracting growth rules from black-box optimization

[9a-F211-9]Development of a Beam-Pattern Control System for Solid-State Laser Resonators
Using Deep Reinforcement Learning

[9a-F211-10]LLM-agent-driven autonomous operation of a fully automated laser-processing experimental system

[9a-F211-11]Autonomous Control of Particle Size Distribution via Robotic Grinding

[9a-F211-12]Robotic Powder-Mixing System with Load Control and Solvent Addition for Mechanochemical Synthesis

Session Details

[9a-F211-1~12]18.2 Process Informatics

[9a-F211-1]Influence of intermediate parameters on multilayer thin film property prediction using multi-stage regression models

[9a-F211-2]High-Reliability Optimization Method Using Automated Iterative Retraining of Surrogate Models: Application to SiC Solution Growth

[9a-F211-3]Bayesian Optimisation for SiC Polishing Simulations Using SSIM

[9a-F211-4]Proposal of Process Informatics Based on Parametric Testing— Construction of a Quality Knowledge Platform through Integration of Equipment State Variables and Device Characteristics —

[9a-F211-5]Simulations with Temperature Conditions for Understanding Sintering Mechanisms

[9a-F211-6]Building a Model to Predict Power Output of Perovskite Solar CellsUsing Weather and Atmospheric Parameters

[9a-F211-7]Layer-selective and asymmetric reorganization of electrostatic-potential topology in aggressively scaled transistors

[9a-F211-8]Interpretable self driving sputtering: extracting growth rules from black-box optimization

[9a-F211-9]Development of a Beam-Pattern Control System for Solid-State Laser ResonatorsUsing Deep Reinforcement Learning

[9a-F211-10]LLM-agent-driven autonomous operation of a fully automated laser-processing experimental system

[9a-F211-11]Autonomous Control of Particle Size Distribution via Robotic Grinding

[9a-F211-12]Robotic Powder-Mixing System with Load Control and Solvent Addition for Mechanochemical Synthesis

[9a-F211-4]Proposal of Process Informatics Based on Parametric Testing
— Construction of a Quality Knowledge Platform through Integration of Equipment State Variables and Device Characteristics —

[9a-F211-6]Building a Model to Predict Power Output of Perovskite Solar Cells
Using Weather and Atmospheric Parameters

[9a-F211-9]Development of a Beam-Pattern Control System for Solid-State Laser Resonators
Using Deep Reinforcement Learning