| Title |
Deep Learning Approach for Characteristics Prediction of Nanowire FETs by Process Condition |
| Authors |
하종현(Jonghyeon Ha) ; 이경엽(Gyeongyeop Lee) ; 서민기(Minki Suh) ; 방민지(Minji Bang) ; 김태형(Tae Heoung Kim) ; 김정식(Jungsik Kim) |
| DOI |
https://doi.org/10.5573/ieie.2022.59.12.29 |
| Keywords |
Machine learning; DNN; Nanowire FET; TCAD; Sentaurus-QTX |
| Abstract |
Since FinFET (Fin Field Effect Transistor), NWFET (Nanowire FET), the next-generation logic semiconductor GAAFET (Gate All Around Field Effect Transistor), and NSFET (Nanosheet FET) have been in the spotlight. GAAFET with excellent performance has a very high level of process difficulty compared to previous logic semiconductors. As the difficulty level of the process increases, more process time and amount of money are generated in the development stage. If machine learning is introduced into such high-cost semiconductor development, the process can proceed with less cost and time. In this paper, the NWFET dataset extracted using Synopsys TCAD (Technology Computed-Aided Design) Sentaurus tool and QTX tool was predicted and analyzed using forward prediction and reverse prediction for the variation of electrical characteristics according to parameter change of semiconductor devices through DNN (Deep Neural Network). Forward prediction is well predicted with low MSE (Mean Square Error) loss, but Unlike the predictions of D (diameter of circle), Wtop (top of trapezoid), Shape (Nanowire shape), and Scattering in the Reverse prediction, the rate of change in electrical characteristics because change in cDir (channel direction) and nSubbands (the number of subbands) was low in the predictions of cDir and nSubbands, and thus the distribution was not uniform. |