Shadow Void Optimization of Microelectronic Packages with Tree-Based Learners

Kayacan A., Baydogan M. G., DURGUN A. C., Aygun K., Ye D., Geyik C., ...Daha Fazla

29th IEEE Workshop on Signal and Power Integrity, SPI 2025, Gaeta, İtalya, 11 - 14 Mayıs 2025, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/spi64682.2025.11014381
  • Basıldığı Şehir: Gaeta
  • Basıldığı Ülke: İtalya
  • Anahtar Kelimeler: decision tree, optimization, sequential sampling, shadow voiding
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

Shadow voiding is a critical technique for optimizing second-level interconnect (SLI) designs in microelectronic packages, aiming to minimize impedance mismatches while adhering to manufacturing and electrical constraints. This paper introduces a tree-based active learning algorithm for shadow void optimization. The proposed method efficiently explores geometric design parameters using decision trees and truncated SVD to reduce dimensionality. High-performing designs are identified via penalized regression within partitioned subspaces and evaluated through sequential sampling. The algorithm is applied to a 7-2-7 package, demonstrating significant improvements in electrical performance and computational efficiency. Results show that tree-based learners effectively characterize the design space, making them a practical solution for high-dimensional optimization problems in microelectronics.