1. Probabilistic Artificial Neural Network for Line-Edge-Roughness-Induced Random Variation in FinFET
- Author
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Jaehyuk Lim, Changhwan Shin, and Jin Woong Lee
- Subjects
General Computer Science ,Line edge roughness ,Hardware_PERFORMANCEANDRELIABILITY ,01 natural sciences ,law.invention ,03 medical and health sciences ,0302 clinical medicine ,law ,0103 physical sciences ,MOSFET ,Electronic engineering ,Hardware_INTEGRATEDCIRCUITS ,process-induced random variation ,General Materials Science ,Mathematics ,010302 applied physics ,Artificial neural network ,Transistor ,General Engineering ,Probabilistic logic ,Threshold voltage ,TK1-9971 ,machine learning ,FinFET ,Field-effect transistor ,Electrical engineering. Electronics. Nuclear engineering ,Random variable ,030217 neurology & neurosurgery ,artificial neural network ,Voltage - Abstract
Line-edge-roughness (LER) is one of undesirable process-induced random variation sources. LER is mostly occurred in the process of photo-lithography and etching, and it provokes random variation in performance of transistors such as metal oxide semiconductor field effect transistor (MOSFET), fin-shaped field effect transistor (FinFET), and gate-all-around field effect transistor (GAAFET). LER was analyzed/characterized with technology computer-aided design (TCAD), but it is fundamentally very time consuming. To tackle this issue, machine learning (ML)-based method is proposed in this work. LER parameters (i.e., amplitude, and correlation length X, Y) are provided as inputs. Then, artificial neural network (ANN) predicts 7-parameters [i.e., off-state leakage current (Ioff), saturation drain current (Idsat), linear drain current (Idlin), low drain current (Idlo), high drain current (Idhi), saturation threshold voltage (Vtsat), and linear threshold voltage (Vtlin)] which are usually used to evaluate the performance of FinFET. First, how datasets for training process of ANN were generated is explained. Next, the evaluation method for probabilistic problem is introduced. Finally, the architecture of ANN, training process and our new proposition is presented. It turned out that the prediction results (i.e., non-Gaussian distribution of device performance metrics) obtained from the ANN were very similar to that from TCAD in the respect of both qualitative and quantitative comparison.
- Published
- 2021