Channel Characteristic-Based Deep Neural Network Models for Accurate Eye Diagram Estimation in High Bandwidth Memory (HBM) Silicon Interposer

MLA

Youngwoo Kim, et al. “Channel Characteristic-Based Deep Neural Network Models for Accurate Eye Diagram Estimation in High Bandwidth Memory (HBM) Silicon Interposer.” IEEE Transactions on Electromagnetic Compatibility, vol. 64, Feb. 2022, pp. 196–208. EBSCOhost, https://doi.org/10.1109/temc.2021.3081713.

APA

Youngwoo Kim, Joungho Kim, Daehwan Lho, Kyungjun Cho, Seongguk Kim, Subin Kim, Junyong Park, Jinwook Song, Hyungmin Kang, HyunWook Park, Boogyo Sim, & Shinyoung Park. (2022). Channel Characteristic-Based Deep Neural Network Models for Accurate Eye Diagram Estimation in High Bandwidth Memory (HBM) Silicon Interposer. IEEE Transactions on Electromagnetic Compatibility, 64, 196–208. https://doi.org/10.1109/temc.2021.3081713

Chicago

Youngwoo Kim, Joungho Kim, Daehwan Lho, Kyungjun Cho, Seongguk Kim, Subin Kim, Junyong Park, et al. 2022. “Channel Characteristic-Based Deep Neural Network Models for Accurate Eye Diagram Estimation in High Bandwidth Memory (HBM) Silicon Interposer.” IEEE Transactions on Electromagnetic Compatibility 64 (February): 196–208. doi:10.1109/temc.2021.3081713.