Seonghoon Jin | Searcharxiv

arXiv2022

Restructuring TCAD System: Teaching Traditional TCAD New Tricks

Traditional TCAD simulation has succeeded in predicting and optimizing the device performance; however, it still faces a massive challenge - a high computational cost. There have been many attempts to replace TCAD with deep learning, but it has not yet been completely replaced. This paper presents a novel algorithm restructuring the traditional TCAD system. The proposed algorithm predicts three-dimensional (3-D) TCAD simulation in real-time while capturing a variance, enables deep learning and TCAD to complement each other, and fully resolves convergence errors.

eess.SP↗

arXiv2015

General Geometric Fluctuation Modeling for Device Variability Analysis

The authors propose a new modeling approach based on the impedance field method (IFM) to analyze the general geometric variations in device simulations. Compared with the direct modeling of multiple variational devices, the proposed geometric variation (GV) model shows a better efficiency thanks to its IFM based nature. Compared with the existing random geometric fluctuation (RGF) model where the noise sources are limited to the interfaces, the present GV model provides better accuracy and wider application areas as it transforms the geometric variation into global mesh deformation and computes the noise sources induced by the geometric variation in the whole simulation domain. GV model also provides great insights into the device by providing the effective noise sources, equation-wise contributions, and sensitivity maps that are useful for device characterization and optimization.

cond-mat.mes-hall↗