DC Field | Value | Language |
---|---|---|
dc.contributor.author | 서경민 | - |
dc.date.accessioned | 2022-10-31T16:31:38Z | - |
dc.date.available | 2022-10-31T16:31:38Z | - |
dc.date.issued | 2021 | - |
dc.identifier.other | OAK-2015-09615 | - |
dc.identifier.uri | http://postech.dcollection.net/common/orgView/200000507951 | ko_KR |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/114162 | - |
dc.description | Master | - |
dc.description.abstract | The possibility of a novel design of the gate to increase radio-frequency (RF) properties of graphene filed-effect transistors (GFETs) that use graphene pseudo-optics by using a deep learning (DL) was explored. The transmission probabilities for arbitrary gate shapes were calculated by using the finite-difference-time-domain method for massless Dirac fermions and used to train a deep learning. The trained DL predicts the trajectories of massless Dirac fermions according to graphene pseudo-optics. Furthermore, the trained DL can design an optimized gate shape for a targeted graphene pseudo-optic response that increases RF properties of GFETs. The DL-designed GFET has the cutoff frequency f_T=46 GHz and the maximum oscillation frequency f_max=49 GHz, and the conventional GFET has f_T=33.7 GHz and f_max=7.8 GHz with gate length L_g=300 nm. The results showed that the trained DL can increase RF properties of GFETs and solve the existing problem that GFETs have much lower f_max than f_T. | - |
dc.language | eng | - |
dc.publisher | 포항공과대학교 | - |
dc.title | Optimization of Radio-frequency Properties of Graphene FET enabled by Deep Learning | - |
dc.title.alternative | 딥 러닝을 이용한 그래핀 전계 트랜지스터의 고주파 특성 최적화 | - |
dc.type | Thesis | - |
dc.contributor.college | 전자전기공학과 | - |
dc.date.degree | 2021- 8 | - |
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