In this work, we improved the performance of germanium (Ge) channel Charge Trapping MemTransistors (CTMTs) as synaptic device by using Ti-doped HfO ₂ as charge trapping layer (CTL). We manipulated the amount of Ti dopant within the HfO ₂ 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 ₂ 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.

中文翻译：

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掺Ti HfO ₂提高电荷陷阱膜作为突触器件的性能

在这项工作中，我们通过使用掺Ti的HfO ₂作为电荷俘获层（CTL）来改善锗（Ge）沟道电荷俘获膜晶体管（CTMT）作为突触器件的性能 。我们控制了HfO _2中的Ti掺杂量 CTL通过改变原子层沉积（ALD）中的Hf / Ti循环比来执行能带工程。定量分析钛的含量，并借助透射电子显微镜（TEM）图像和X射线光电子能谱（XPS）分析来构建栅叠层的能带结构。然后，我们制造了电荷捕获电容器并表征了它们的存储特性，例如存储窗口。通过实施两性陷阱模型，热活化电子保留模型和高级电荷衰减模型，提取了CTL的陷阱分布。最后，我们用掺杂Ti的HfO ₂制造了CTMT。 作为CTL，并以突触装置的性能为特征，例如压抑和增强的非线性以及电导通/断比。我们将NeuroSim模拟器与多层感知器和卷积神经网络模型一起使用，以使用CTMT作为突触设备评估神经网络硬件加速器的模式识别精度，并将CTMT的性能与其他类型的突触设备进行基准测试。