當前的電腦系統是採用馮諾伊曼架構，然而在數據處理量日漸龐大的情況下，運算與記憶單元之間數據的傳遞面臨延遲及功耗等瓶頸。啟發於人類大腦，同時具有運算及記憶功能的記憶體內運算（In-memory computing, IMC）為能夠克服馮諾伊曼瓶頸的其中一種研究方向。電阻式切換能夠在執行完邏輯運算之後將其資料穩定地儲存，而被視為最有希望實現記憶體內運算的元件之一。
本篇論文以TiN/HfOx/HfZrOx/TiN的結構下實現具高可靠度的可切換式二極體，研究結果顯示在最佳化下RHRS/RLRS比值約2600，整流比約1000，資料儲存可保留長達十年。HfZrOx薄膜在沈積完成後即進行400°C熱退火製程，可形成斜方晶相且具有鐵電特性，二極體的切換由HfZrOx的極化值以及外加電場主導。透過堆疊非晶的HfOx可以使記憶狀態更穩定地維持，HfZrOx和HfOx皆視為n型氧化物半導體，而非晶的HfOx可以抑制空乏區中的空間電荷被復合。可切換式二極體能夠在單一元件下，以時序邏輯的方式實現16種布林函數。本論文提出的可切換式二極體除了具有結構簡單以及良好的記憶特性與運算能力，能夠實現記憶體內運算，更重要的是，選用的材料可以整合於現今製程中。

Current von Neumann system that shuttles data between memory and computing unit has faced the bottleneck of latency and power consumption, thus the system beyond von Neumann architecture with the function of in-memory computing （IMC） that is inspired by the human brain is essential for data-centric AI. Resistive switching （RS） is one of the most promising candidates to implement IMC since it enables “stateful” logics that simultaneously perform logic operations and store data.
In this research, stack structure TiN/HfOx/HfZrOx/TiN was explored to accomplish switchable diode behavior with desirable memory characteristics in terms of high RHRS/RLRS ratio of ~2600, large rectification of ~1000, retention up to 10 years. HfZrOx thin films were crystallized at 400°C post-deposition annealing, forming the orthorhombic phase with ferroelectric characteristic. The bias-controlled direction of built-in diode was studied to be dominated by the polarization of the HfZrOx while the good retention can be achieved by stacking amorphous HfOx which is beneficial to suppress the recombination in space charge region as both HfZrOx and HfOx assumed to be n-type semiconducting-like oxide. More importantly, 16 Boolean functions can be realized in single device through sequential logic cycles. The prominent nonvolatile memory and computation performance make it a promising device for in-memory computing. Besides electrical properties, it well outperforms other IMC devices due to simple structure and fab-friendly materials, inspiring a new approach to capitalize on the salient features of ferroelectric HfZrOx for IMC in the AI era.

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