A Flexible Neural Network-Based Tool for Package Second Level Interconnect Modeling

Karatoprak F., Sacin E. S., Ozese D., Durgun A. C., Baydogan M. G., Aygun K., ...Daha Fazla

IEEE 32nd Conference on Electrical Performance of Electronic Packaging and Systems (EPEPS), California, Amerika Birleşik Devletleri, 15 - 18 Ekim 2023 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/epeps58208.2023.10314939
  • Basıldığı Şehir: California
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Anahtar Kelimeler: high-speed I/O, Neural network, packaging, S-parameters, second level interconnect
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

This paper introduces a neural network (NN)-based practical design tool for quick assessment of package second level interconnects (SLIs) at the earlier design stages. The study addresses the well-known computational cost problem of data generation and training processes of NN implementation by proposing a flexible model approach, where the SLI geometry is divided into several building blocks, for which a separate NN model was trained. The NNs take geometrical parameters as inputs and return the complex S-parameter matrices as outputs. The electrical performance of the entire SLI geometry is obtained by cascading the S-paramaters of the building blocks.