隨著 CMOS 技術的興起，通過硬體設計以實現能夠克服傳統馮紐曼架構的神經網絡的可能性上獲得了巨大進展。通過即時的數據處理及多階儲存單元(MLC)運算可以在消耗更少的能量下，處理大量複雜的數據，同時更有著益發出色的可擴展性。
在一開始的探討中，我們通過使用有機類材料中的氧化石墨烯(Graphene oxide)作為介電層來探討其電阻開關機制。在摻入石墨烯電荷捕捉層的元件中，其表現出良好的操作穩定性及雙極性的開關特性，而其開關比大約為10^2-10^3之間。而在特定數量的連續直流偏壓開關操作後，隨著SET、RESET電壓的增加，在其開關比維持大約為10^2的情況下，元件的操作特性逐漸由雙極性轉向互補式開關特性，通過電性分析和曲線擬合法我們驗證了傳導機制的改變。而在互補開關中隨著電壓的增加，注入電子數量隨之快速上升，這樣的現象促成了中間層電荷儲存區的形成，進而提升性能。該元件在85°C的高溫操作下顯示出長達10^4秒的數據保留周期(retention)和良好的直流耐久性(endurance)。除此之外，該元件還能夠以多阻態操作存儲多位數據（4位元），從而實現高密度記憶體。
另外，具有連續電導變化的人工突觸裝置對於仿生神經形態系統的硬體操作方面實現至關重要。隨著技術的演進，可穿戴式的伸縮技術研究與應用在世界上也變得越發重要。無論從製造或適用性方面來看，高溫都是限制該項技術演進的重要議題之一。因此，在接下來的研究中，我們將展示以高介電常數(High k)同時與互補金氧半導體製程兼容(CMOS compatible)的金屬氧化物(HfO2)作為介電層，並探討其在可撓基板上異質堆疊出的憶阻體開關特性與其在人工神經網路中的應用。該元件在500 μA限電流下表現出約1.2 × 10^4的出色記憶儲存空間(ION/IOFF)和極高的穩定度(σHRS ∼ 3.95 × 10^-10 S)。同時該元件在超過500次的直流存儲操作中表現出相當出色的機械應力和電性穩定度，並且在120°C下的高溫環境下數據保留周期超過10^4秒而沒有任何退化。同時該器件可用於六種不同狀態下的多級儲存單元(MLC)操作，並可用於實 2位元數據存儲應用。而該元件的傳導機制以低電場區間的蕭基發射和高電場區間的跳躍傳導為主的高阻態(HRS)，和在低阻態(LRS)的歐姆傳導所組成。我們同時在神經網絡中以96.07%的準確率作為基準對該元件進行了訓練，並觀察其電導變化和高溫操作對特性影響。另外也特別在高溫環境下以95.68%的片外訓練精確度對元件進行了訓練，並在整個過程中觀察其精確度曲線的變化。結果顯示該元件在超過 10^4秒的長期彎曲應力(r = 8 mm)下仍維持出色的機械應力穩定性，同時保有完好的記憶儲存空間。在實驗中為觀察長期機械應力對學習過程的影響，每30分鐘進行一次神經精確度量測，結果顯示其最大值為96.01%。
此外，通過將矽(Si)摻雜到具有高度線性突觸特性的高介電常數薄膜(HfOx)中，我們得到了一種高度可靠且多工的電阻式記憶體，並且可以通過調變限電流在閾值切換(threshold)和非揮發性(NVM)開關行為間進行切換。結果顯示在限電流小於1 µA時轉為揮發性操作，並在限電流高於10 µA時表現出非揮發性行為。通過RESET電壓調變也得到具備3bit/cell的高密度記憶體。而該元件不僅顯示了出色的增強/抑制線性響應，在高達10000個快速脈衝(100 ns)的情況下，有著良好的開關比 (>10^3)。在85°C的環境下以脈衝寬度為200 ns進行量測，也能維持穩定的數據保存能力 (10^5 s) 和高寫入耐久性(~10^7個週期)。儘管如此，類神經方面的應用已經通過使用Si:HfOx交叉陣列電阻的實驗校準數據的神經網路模擬得到成功的驗證。對於神經網路模擬，已經獲得了0.03的低非線性度和98.08%的模式識認精確度。而這樣的結果正顯示了在未來大規模使用交叉陣列的類神經形態元件中低功率選擇器和高密度非揮發性記憶體的發展潛力。
最後，考慮到高熱積存(thermal budget)的類神經形態應用，我們選擇在高度可撓曲的聚酰亞胺基板上沉積以氧化鉿(HfO2)作為介電層的2bit/cell堆疊電阻式隨機存取記憶體 (ReRAM)。而該元件的特性顯示，在低阻態(LRS)電流(ION)與高阻態(HRS)電流(IOFF)其ION/IOFF之比高於1.4×10^3，而在100 µA限電流下元件與元件間的變化度很小，顯示其相當高的操作穩定性。在5 mm彎曲半徑下超過 10^4次的重複彎曲應力測試則顯示了高機械穩定性及超過10^6次的寫入耐久性都顯示了這些元件非常適合應用於線上神經網路訓練。在 125°C 下超過 10^4秒的數據保留能力也增強了這些設備的長期推理能力。此外，這些元件的性能皆已經通過系統層級的模擬和實驗校準數據得到了在類神經方面應用的驗證。而根據系統層級模擬也顯示，在長達10年的操作區間內，推理準確度與基線相比僅有2%的損失。

The increasing initiations in CMOS technology, the hardware implemented neural network acquiring enormous significance due to the capability to overcome the von Neumann bottle neck disputes. The real time data processing and MLC operation can deal with massive amount of complex data consuming less energy with outstanding scalability.
Initially, organic material-based resistive switching mechanisms were studied by using graphene oxide as the switching layer. With the insertion of a charge trapping graphene layer, the device showed good stability and good electrical bipolar switching properties, with an ON/ OFF ratio about 10^2–10^3. The device gradually shifted toward complementary switching behavior while maintaining an ON/OFF ratio of 10^2 from bipolar switching behavior after a specific number of consecutive DC switching cycles with increases in the SET-RESET voltage. The conduction mechanisms for bipolar (P–F conduction) and the complementary switching were verified based on the electrical characteristics and curve fittings. Rapid increases in the injected electrons due to increased voltage in complementary switching facilitated the formation of an intermediate charge reservoir region that, in turn, enhanced performance. The device showed a retention period as high as 10^4 s at 85°C and good DC endurance. The device is also capable of multi-resistance states to obtain multi-bit (4-bit) data storage, leading to high density memory realization.
In addition, an artificial synaptic device with continuous conductance variation is essential for the hardware implementation of bioinspired neuromorphic systems. With increasing technological advancement, wearable flexible technology is gaining enormous importance in the research community. High temperature is one of the key issues in flexible technology from the fabrication and applicability aspects. In this work, we have demonstrated the performance of a complementary metal-oxide-semiconductor (CMOS)-compatible high-k material (HfO2) based flexible heterogeneous stacked resistive switching device in an artificial neural network. The device exhibited an excellent memory window (ION/IOFF) of around 1.2 × 10^4 with an ultralow variation (σHRS ∼ 3.95 × 10^-10 S) at 500 μA current compliance. The device shows excellent mechanical and electrical
stability for more than 500 DC cycles and data retention capability at 120 °C for more than 104 s without any degradation. The device can be used for multilevel cell (MLC) operation in six distinct states and can be useful for the implementation of 2-bit data storage applications. The conduction mechanism in the device was dominated by Schottky emission at the lower field region and hopping conduction at the higher field region of the high-resistance state (HRS), whereas ohmic conduction was satisfied at the low resistance state (LRS). We have trained the device in the neural network with 96.07% accuracy as the baseline and observed the effect of conductance variation and high-temperature operation. We trained the device at a high temperature with a 95.68% off-chip training accuracy and observed the accuracy profile throughout the time. The device also possessed an excellent mechanical stability under a long-term bending stress (r = 8 mm) of over 10^4 s with an intact memory window. The neural accuracy was measured every 30 min with a maximum of 96.01% to observe the effect of long-term mechanical stress on the off-chip learning process.
Moreover, A highly reliable and versatile resistive memory devices demonstrating both threshold and nonvolatile (NVM) switching behavior depending on the compliance current modulation has been utilized by doping a semiconducting (Si) material into a high-k (HfOx) film with highly linear synaptic behavior. The device shifted towards volatile switching at the CC less than 1 µA and exhibited NVM behavior at CC limit above 10 µA. 3-bit/cell data storage capability on RESET voltage modulation implemented for high density memory application. The device shows excellent programming linearity of potentiation/depression responses up to 10000 pulses compatible with fast pulse (100 ns) with good ON/OFF ratio (>10^3), stable data retention capability (10^5 s) at 85°C and high WRITE endurance (~10^7 cycles) with a pulse width of 200 ns. Nevertheless, the neuromorphic applications have been successfully verified by neural network simulations using experimentally calibrated data of the Si:HfOx resistive cross-point devices. A low nonlinearity of 0.03 with 98.08% pattern recognition accuracy have been obtained for neural network simulations. The calibrated results revealed the potentiality of device for low power selector and high density NVM storage in large scale crossbar array in future neuromorphic devices.
Finally, hafnium oxide (HfO2) based 2bits/cell stacked resistive random access memory devices (ReRAM) fabricated on highly flexible polyimide substrates for neuromorphic applications considering high thermal budget. The ratio of low resistance state (LRS) current (ION) to high resistance state (HRS) current (IOFF) or ION/IOFF for the fabricated devices was above 1.4×10^3 with a low device to device variation at 100 µA current compliance. The mechanical stability over 10^4 bending cycles at a 5 mm bending radius and endurance over 10^6 WRITE cycles make these devices suitable for online neural network training. The data retention capability over 10^4 seconds at 125°C also infuses these devices' long-term inference capability. Further, the performance of the devices has been verified for neuromorphic applications by system-level simulations with experimentally calibrated data. The system-level simulation reveals only a 2% loss in inference accuracy over ten years from the baseline.

CHAPTER 1
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