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A Novel Approach for ESD Generator Modeling Using Deep Neural Network

Jayoung Yang (Engineer, Samsung Electronics)

Jae-Young Shin (Engineer, Samsung Electronics)

Jaeho Lee (Engineer, Samsung Electronics)

Yoonna Oh (Principal Engineer, Samsung Electronics)

Jin-Sung Youn (Senior Engineer, Samsung Electronics)

Daehee Lee (Principal Engineer, Samsung Electronics)

Seong-Jin Mun (Senior Engineer, Samsung Electronics)

Chan-Seok Hwang (Principal Engineer, Samsung Electronics)

Jong-Bae Lee (Vice President, Samsung Electronics)

Location: Ballroom D

Date: Thursday, January 31

Time: 2:50pm - 3:30pm

Track: 15. Machine Learning for Microelectronics, Signaling & System Design, 12. Electromagnetic Compatibility/Mitigating Interference

Session Type: Technical Session

Vault Recording: TBD

Audience Level: All

We propose a novel methodology for modeling an electrostatic discharge (ESD) generator based on a deep neural network (DNN). The proposed methodology can predict an equivalent circuit model for several commercial ESD generators within a few second. For this, the DNN model based on multi-layer perceptron is trained with several different ESD waveforms. Consequently, we evaluate the proposed DNN model using various ESD waveforms, and the resulting correlation coefficient is about 0.9 for more than 95 % test samples. With our methodology, the predicted equivalent ESD circuit models have higher accuracy than the conventional approaches based on engineer's empirical knowledge.

Takeaway

- The basics of deep neural network (DNN) and electrostatic discharge (ESD) generator modeling
- A detailed methodology for training the DNN for the ESD generator modeling

Intended Audience

- Understanding of electrostatic discharge (ESD) generator model
- Basic knowledge of machine learning & deep neural network

Presentation Files

SLIDES_15_ANovelApproachForESD_Yang.pdf
PAPER_15_ANovelApproachForESD_Yang.pdf