熱驅動之類神經網路元件及在Cr2O3中電性偵測反鐵磁性

Abstract

為了解決傳統計算機架構:馮諾依曼架構(Von Neumann architecture)中的高能耗問題，基於反鐵磁（AFM）的自旋電子學被認為是有前途的候選者。與現今發展的鐵磁性自旋電子學相比，反鐵磁材料被認為會是下一主要的發展方向，其主要優點是不存在雜散場並且不會受到外部磁場所擾動。另一方面，受人腦啟發的全新架構也是未來應用的另一個重要方向。人腦神經網絡的低功耗和高效能是研究的主要動機。模擬人腦神經網絡的電子元件架構對於實現信息處理和決策的人工智能至關重要。在過去的幾十年裡，不同類型的類神經網路元件已經被開發出來，例如由離子擴散引起的憶阻器、由電壓閾值切換的結構相變元件(漸進結晶/非晶化)，以及基於磁區切換自旋電子學的器件。然而，這些設備也各自面臨挑戰，包括積體電路的可擴展性以及權重變化的非線性。因此，本篇論文提出替代方法來解決上述挑戰。在本研究中，我們介紹了一種基於非對稱溫度梯度的多層多腳位類神經網路元件；我們的元件展示出廣泛的突觸功能，包括突觸權重的增強、抑制以及反對稱和對稱脈衝時序依賴可塑性（STDP）。此熱驅動之類神經網路元件為未來人工智能的硬體實現提供了一個平台。 另一方面，我們嘗試利用自旋翻轉(Spin-flop)來實現尼爾矢量(Néel vector)翻轉。利用電性量測來檢測和操縱尼爾矢量已經在塊狀單軸的反鐵磁材料Cr2O3中實現。在這篇論文中，我們嘗試在Al2O3基板上生長高品質的單晶Cr2O3薄膜，並嘗試用電性量測方法檢測自旋翻轉。這些工作使我們向下一代自旋電子學邁出了一步。

To deal with the high energy consumption problem in the conventional computer architecture (Von Neumann architecture), antiferromagnetic (AFM) based spintronics is regarded as the promising candidate. The absence of a stray field and the robustness against external magnetic perturbation are the main advantages for antiferromagnetic materials compared with ferromagnetic-based spintronics. On the other hand, a brand-new architecture inspired by the human brain is another crucial platform for future application. The low power consumption and high performance the biological neural network enjoys are the primary motivations for neuromorphic computing devices. Neuromorphic computing devices, which emulate biological neural networks, are crucial in realizing artificial intelligence for information processing and decision-making. In the past decades, different types of neuromorphic computing devices with multiple resistance levels (defined as synaptic weight) have been developed, such as oxide-based memristors caused by ion diffusion, phase transition-based devices caused by threshold switching, progressive crystallization/amorphization, and spintronics-based devices caused by magnetic domain switching. However, these devices face significant challenges, including disruptions in the reading process, limited scalability in integrated circuits, and non-linearity in weight change. Therefore, alternative approaches are required to solve the above challenges. In this study, we introduce a multi-layer-multi-terminal neuromorphic computing device based on the asymmetric temperature gradient. Our device exhibits a wide range of synaptic functions, including potentiation, depression, and both anti-symmetric and symmetric spike-timing-dependent plasticity (STDP). The thermal driving strategy offers an energy-efficient platform for future neuromorphic computing devices to achieve artificial intelligence. On the other hand, we tried to employ the spin-flop transition to realize the AFM switching. The electrical detection and manipulation of the Néel vector have been realized in bulk uniaxial antiferromagnet Cr2O3. In this work, we also tried to grow high-quality epitaxial Cr2O3 thin film on Al2O3 substrate and tried to detect the spin-flop transition electrically. These works give us a step toward the next generation of electronics.