Probabilistic Artificial Neural Network for Line-Edge-Roughness-Induced Random Variation in FinFET
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-sha...
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| Format: | Article |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9452104/ |
| _version_ | 1819098324703117312 |
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| author | Jaehyuk Lim Jinwoong Lee Changhwan Shin |
| author_facet | Jaehyuk Lim Jinwoong Lee Changhwan Shin |
| author_sort | Jaehyuk Lim |
| collection | DOAJ |
| description | 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 (I<sub>off</sub>), saturation drain current (I<sub>dsat</sub>), linear drain current (I<sub>dlin</sub>), low drain current (I<sub>dlo</sub>), high drain current (I<sub>dhi</sub>), saturation threshold voltage (V<sub>tsat</sub>), and linear threshold voltage (V<sub>tlin</sub>)] 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. |
| first_indexed | 2024-12-22T00:29:11Z |
| format | Article |
| id | doaj.art-d54f8d2e73e745bc84c4b4a05ea397c3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T00:29:11Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d54f8d2e73e745bc84c4b4a05ea397c32022-12-21T18:45:00ZengIEEEIEEE Access2169-35362021-01-019865818658910.1109/ACCESS.2021.30884619452104Probabilistic Artificial Neural Network for Line-Edge-Roughness-Induced Random Variation in FinFETJaehyuk Lim0https://orcid.org/0000-0003-1636-8865Jinwoong Lee1Changhwan Shin2https://orcid.org/0000-0001-6057-3773Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of KoreaLine-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 (I<sub>off</sub>), saturation drain current (I<sub>dsat</sub>), linear drain current (I<sub>dlin</sub>), low drain current (I<sub>dlo</sub>), high drain current (I<sub>dhi</sub>), saturation threshold voltage (V<sub>tsat</sub>), and linear threshold voltage (V<sub>tlin</sub>)] 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.https://ieeexplore.ieee.org/document/9452104/Line edge roughnessprocess-induced random variationFinFETmachine learningartificial neural network |
| spellingShingle | Jaehyuk Lim Jinwoong Lee Changhwan Shin Probabilistic Artificial Neural Network for Line-Edge-Roughness-Induced Random Variation in FinFET IEEE Access Line edge roughness process-induced random variation FinFET machine learning artificial neural network |
| title | Probabilistic Artificial Neural Network for Line-Edge-Roughness-Induced Random Variation in FinFET |
| title_full | Probabilistic Artificial Neural Network for Line-Edge-Roughness-Induced Random Variation in FinFET |
| title_fullStr | Probabilistic Artificial Neural Network for Line-Edge-Roughness-Induced Random Variation in FinFET |
| title_full_unstemmed | Probabilistic Artificial Neural Network for Line-Edge-Roughness-Induced Random Variation in FinFET |
| title_short | Probabilistic Artificial Neural Network for Line-Edge-Roughness-Induced Random Variation in FinFET |
| title_sort | probabilistic artificial neural network for line edge roughness induced random variation in finfet |
| topic | Line edge roughness process-induced random variation FinFET machine learning artificial neural network |
| url | https://ieeexplore.ieee.org/document/9452104/ |
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