| Autor: |
Jun Hui Park, Jung Nam Kim, Seonhaeng Lee, Gang-Jun Kim, Namhyun Lee, Rock-Hyun Baek, Dae Hwan Kim, Changhyun Kim, Myounggon Kang, Yoon Kim |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 12, Pp 23881-23886 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3357241 |
| Popis: |
Accurate current-voltage (I-V) modeling based on the Berkeley short-channel insulated-gate field-effect transistor model (BSIM) is pivotal for integrated circuit simulation. However, the current BSIM model does not support a buried-channel-array transistor (BCAT), which is the structure of the state-of-the-art commercial dynamic random access memory (DRAM) cell transistor. In this work, we propose an intelligent I-V modeling technique that combines genetic algorithm (GA) and deep learning (DL). This hybrid technique facilitates both optimization of BSIM parameter and accurate I-V modeling, even for devices not originally supported by BSIM. Additionally, we extended application of the DL to model one of the principal degradation mechanisms of transistor, the hot-carrier degradation (HCD). The successful modeling results of I-V characteristic and device degradation demonstrated that devices not supported by BSIM can be accurately modeled for integrated circuit simulations. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
