隨著人工智慧（Artificial Intelligence）與邊緣運算（Edge Computing）需求快速成長，傳統馮紐曼架構（Von Neumann Architecture）在資料傳輸與能耗效率上逐漸面臨瓶頸。為模擬人腦中突觸的可塑性與並行處理特性，發展具備神經形態運算功能（Neuromorphic Computing）之非揮發性記憶體元件（Non-Volatile Memory Devices）成為重要研究方向。其中，電阻式記憶體（Resistive Random-Access Memory, RRAM）因其結構簡單、與CMOS製程兼容性高與多階電導特性而備受關注。然而，為實現具穩定的類突觸行為之RRAM元件，仍需針對材料堆疊設計與導電通道調控機制進行深入探討。
本研究針對具備神經形態應用潛力之雙層結構RRAM元件進行設計與分析，選用高能障材料氧化鋁（Aluminum Oxide, Al2O3）與鐵電氧化物鐵酸鉍（Bismuth Ferrite, BiFeO3, BFO）構成雙層切換層，系統性調控其厚度比例，評估其對元件電性、導電機制及神經形態特性的影響。實驗結果顯示，所有樣品皆展現穩定的雙極性電阻切換行為（Bipolar Resistive Switching），且隨著Al2O3占比提高，元件切換行為由漸進式導通/突變式中斷（Gradual Set / Abrupt Reset）漸轉為突變式導通/漸進式中斷（Abrupt Set / Gradual Reset），對應導電通道生成與斷裂過程受結構所調控之趨勢。
導電機制分析顯示，較厚的BFO結構具備明確之歐姆傳導（Ohmic Conduction）、PF發射（Poole–Frenkel Emission）與F-N穿隧（Fowler–Nordheim Tunneling）等多階段導電行為，而當Al2O3占比提高時，穿隧機制逐漸消失，轉為以陷阱輔助遷移為主之漸進式導電特性。此外，在神經形態脈衝測試中，三組雙層結構皆展現可逆電導調變能力，且具備長期增強與抑制（Long-Term Potentiation and Depression, LTP/LTD）以及成對脈衝促進（Paired-Pulse Facilitation, PPF）等類突觸行為，並可對應其切換特性與材料堆疊比例。實驗結果中所得時間常數亦落於生物突觸短期可塑性（Short-Term Synaptic Plasticity）之時程範圍，顯示其模擬類神經行為之可行性。

With the rapid advancement of artificial intelligence (AI) and edge computing technologies, the traditional von Neumann architecture is increasingly facing bottlenecks in data throughput and energy efficiency. To mimic the plasticity and parallel processing capability of biological synapses, developing non-volatile memory devices with neuromorphic computing functionality has become a primary research focus. Among them, resistive random-access memory (ReRAM) devices have attracted considerable attention due to their simple structure, high integration potential, and multi-level conductance states. However, achieving reliable synaptic-like behavior in ReRAM still requires detailed investigations into material stacking design and filament modulation mechanisms.
In this study, a bilayer switching structure comprising aluminum oxide (Al₂O₃) and bismuth ferrite (BiFeO₃) was developed to investigate its influence on resistive switching behavior, conduction mechanisms, and neuromorphic characteristics. The thickness ratio between the two layers was systematically varied to evaluate its effect on overall device performance. Experimental results show that all devices exhibit stable bipolar resistive switching, and with increasing Al2O3 content, the switching transition evolves from gradual set / abrupt reset to abrupt set / gradual reset.
Conduction mechanism analysis reveals that thicker BFO devices follow a three-stage transition — Ohmic conduction, Poole-Frenkel emission, and Fowler-Nordheim tunneling, while increased Al2O3 content suppresses tunneling behavior and promotes trap-assisted gradual conduction. Furthermore, all bilayer samples demonstrate reversible conductance modulation under pulse stimulation, exhibiting long-term potentiation and depression (LTP/LTD) and paired-pulse facilitation (PPF), in correlation with their respective switching behavior and layer proportions. The extracted time constants fall within the timescale of biological short-term synaptic plasticity, indicating the feasibility of mimicking transient neural learning behaviors.

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