AI技術の活用で導波路の接続状態の良否を自動判定~専門技術に頼ることなく高周波デバイス特性の正確な評価を可能に~

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2025-06-11 産業技術総合研究所

AI技術の活用で導波路の接続状態の良否を自動判定~専門技術に頼ることなく高周波デバイス特性の正確な評価を可能に~

産総研は、ミリ波~テラヘルツ波領域の高周波測定において、導波路の接続状態をAI技術で自動判定する手法を開発。機械学習により、目視や作業者の熟練度に依存せずに接続良否を判断し、測定精度のばらつきを解消。周波数帯や測定対象に依存せず高い汎用性を確認。LOF(局所外れ値因子)を用いた判定により、接続異常の早期検知が可能。今後は導波路の自動アライメントと連携し、測定システムの完全自動化を目指す。

<関連情報>

1.1THzまでの周波数における機械学習に基づく導波管接続とプローブ接触状態の検出アルゴリズム Detection Algorithm for Waveguide Connection and Probe Contact States Based on Machine Learning in Frequency up to 1.1THz

Ryo Sakamaki, Seitaro Kon, Shuhei Amakawa, Takeshi Yoshida, Satoshi Tanaka, Minoru Fujishima
IEEE MTT-S International Microwave Symposium(IMS)2025 workshop/general session

Abstract

The connection state of waveguides (WGs) is one of the major error factors in RF measurements. The stability of WG connections during operation strongly depends on the operator’s skill. However, WG connections in the terahertz band up are particularly challenging, making it difficult to perform proper connections. This paper proposes a detection technique for the WG connection state in the frequency range up to 1.1 THz. The proposed technique enables stable measurement operation by integrating the algorithm into a terahertz measurement system. Rectangular WGs and RF probes with on-wafer measurement systems were used for the demonstrations. The proposed method utilizes the local outlier factor method and linear least-mean-square learning. This technique can detect the connection state of the WG even without requiring training data from the device under test (DUT) itself.

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