Improving the Performance of Charge Trapping Memtransistor as Synaptic Device by Ti-Doped HfO<sub>2</sub>
In this work, we improved the performance of germanium (Ge) channel Charge Trapping MemTransistors (CTMTs) as synaptic device by using Ti-doped HfO<sub>2</sub> as charge trapping layer (CTL). We manipulated the amount of Ti dopant within the HfO<sub>2</sub> CTL to perform the...
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IEEE
2021-01-01
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| Series: | IEEE Journal of the Electron Devices Society |
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| Online Access: | https://ieeexplore.ieee.org/document/9296335/ |
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| author | Yu-Che Chou Wan-Hsuan Chung Chien-Wei Tsai Chin-Ya Yi Chao-Hsin Chien |
| author_facet | Yu-Che Chou Wan-Hsuan Chung Chien-Wei Tsai Chin-Ya Yi Chao-Hsin Chien |
| author_sort | Yu-Che Chou |
| collection | DOAJ |
| description | In this work, we improved the performance of germanium (Ge) channel Charge Trapping MemTransistors (CTMTs) as synaptic device by using Ti-doped HfO<sub>2</sub> as charge trapping layer (CTL). We manipulated the amount of Ti dopant within the HfO<sub>2</sub> CTL to perform the band engineering by varying the Hf/Ti cycle ratio in atomic layer deposition (ALD). The content of Ti was quantified and the energy band structures of the gate stack was constructed with the aid of transmission electron microscope (TEM) images and X-ray photoelectron spectroscopy (XPS) analysis. We then fabricated the charge trapping capacitors and characterized their memory characteristics such as memory windows. By the implementation of amphoteric trap model, thermal activated electron retention model and advanced charge decay model, the trap distribution of the CTL was extracted. Finally, we fabricated the CTMTs with Ti-doped HfO<sub>2</sub> as the CTL and characterized their performance as synaptic device such as nonlinearity of depression and potentiation and also conductance on/off ratio. We used NeuroSim simulator with multilayer perceptron and convolutional neural network models to evaluate the pattern recognition accuracy of neural network hardware accelerator using CTMTs as synaptic devices and benchmarked the performance of our CTMT with those of other types of synaptic devices. |
| format | Article |
| id | doaj-art-62e12bba226f495b903b4fba2b4e982c |
| institution | Kabale University |
| issn | 2168-6734 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of the Electron Devices Society |
| spelling | doaj-art-62e12bba226f495b903b4fba2b4e982c2025-08-25T23:00:22ZengIEEEIEEE Journal of the Electron Devices Society2168-67342021-01-01913714310.1109/JEDS.2020.30451949296335Improving the Performance of Charge Trapping Memtransistor as Synaptic Device by Ti-Doped HfO<sub>2</sub>Yu-Che Chou0https://orcid.org/0000-0002-7038-6768Wan-Hsuan Chung1https://orcid.org/0000-0002-7370-600XChien-Wei Tsai2Chin-Ya Yi3Chao-Hsin Chien4https://orcid.org/0000-0002-6698-6752Institute of Electronics, National Chiao Tung University, Hsinchu, TaiwanInstitute of Electronics, National Chiao Tung University, Hsinchu, TaiwanInstitute of Electronics, National Chiao Tung University, Hsinchu, TaiwanInstitute of Electronics, National Chiao Tung University, Hsinchu, TaiwanInstitute of Electronics, National Chiao Tung University, Hsinchu, TaiwanIn this work, we improved the performance of germanium (Ge) channel Charge Trapping MemTransistors (CTMTs) as synaptic device by using Ti-doped HfO<sub>2</sub> as charge trapping layer (CTL). We manipulated the amount of Ti dopant within the HfO<sub>2</sub> CTL to perform the band engineering by varying the Hf/Ti cycle ratio in atomic layer deposition (ALD). The content of Ti was quantified and the energy band structures of the gate stack was constructed with the aid of transmission electron microscope (TEM) images and X-ray photoelectron spectroscopy (XPS) analysis. We then fabricated the charge trapping capacitors and characterized their memory characteristics such as memory windows. By the implementation of amphoteric trap model, thermal activated electron retention model and advanced charge decay model, the trap distribution of the CTL was extracted. Finally, we fabricated the CTMTs with Ti-doped HfO<sub>2</sub> as the CTL and characterized their performance as synaptic device such as nonlinearity of depression and potentiation and also conductance on/off ratio. We used NeuroSim simulator with multilayer perceptron and convolutional neural network models to evaluate the pattern recognition accuracy of neural network hardware accelerator using CTMTs as synaptic devices and benchmarked the performance of our CTMT with those of other types of synaptic devices.https://ieeexplore.ieee.org/document/9296335/Germaniumdielectric materialsneural network hardwareanalog memoriesartificial intelligenceMOSFETs |
| spellingShingle | Yu-Che Chou Wan-Hsuan Chung Chien-Wei Tsai Chin-Ya Yi Chao-Hsin Chien Improving the Performance of Charge Trapping Memtransistor as Synaptic Device by Ti-Doped HfO<sub>2</sub> IEEE Journal of the Electron Devices Society Germanium dielectric materials neural network hardware analog memories artificial intelligence MOSFETs |
| title | Improving the Performance of Charge Trapping Memtransistor as Synaptic Device by Ti-Doped HfO<sub>2</sub> |
| title_full | Improving the Performance of Charge Trapping Memtransistor as Synaptic Device by Ti-Doped HfO<sub>2</sub> |
| title_fullStr | Improving the Performance of Charge Trapping Memtransistor as Synaptic Device by Ti-Doped HfO<sub>2</sub> |
| title_full_unstemmed | Improving the Performance of Charge Trapping Memtransistor as Synaptic Device by Ti-Doped HfO<sub>2</sub> |
| title_short | Improving the Performance of Charge Trapping Memtransistor as Synaptic Device by Ti-Doped HfO<sub>2</sub> |
| title_sort | improving the performance of charge trapping memtransistor as synaptic device by ti doped hfo sub 2 sub |
| topic | Germanium dielectric materials neural network hardware analog memories artificial intelligence MOSFETs |
| url | https://ieeexplore.ieee.org/document/9296335/ |
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