Low power 1T DRAM/NVM versatile memory featuring steep sub-60-mV/decade operation, fast 20-ns speed, and robust 85&amp;#x00B0;C-extrapolated 10&lt;sup&gt;16&lt;/sup&gt; endurance

Chiu, Yu-Chien; Cheng, Chien‐Hong; Chang, Chun–Yen; Lee, Min-Hung; Hsu, Hsiao‐Hsuan; Yen, Shiang-Shiou

doi:10.1109/vlsit.2015.7223671

Cited by 20 publications

(11 citation statements)

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“…[39,66] Such a cycling endurance is below that of conventional material-based flash memories (≈10 8 ), [67] DRAMs (≈10 12 ), and ferroelectric memories (>10 12 ). [68][69][70] Compared to the charge-based memory limited by tunneling layer degradation, RS memory with simpler structures has the characteristics of fast switching speed, high stability, low power consumption, and excellent device scalability. [64,[71][72][73] Nowadays, RRAMs and PCMs based on 2D layered materials can operate under small voltages (<1 V) and own fast switching speed (≈10 ns), [74][75][76] which is beneficial in averting large energy loss.…”

Section: Memory Implemented By 2d Layered Materialsmentioning

confidence: 99%

Emerging 2D Memory Devices for In‐Memory Computing

et al. 2021

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cube (HMC), [5] high bandwidth memory (HBM), [6,7] and 3D monolithic integration, [8] have been successively developed and achieved some success, the challenge of latency and energy consumption caused by massive data transmission still remains. Therefore, it is urgently necessary to enhance the information interaction capability by improving the architectural relationship between processing and memory units.The emergence of non-von-Neumann architecture solves the problem of the separation of processing and memory units, and alleviates the impact of bus bandwidth on computing efficiency. As a non-Von Neumann architecture, in-memory computing has been considered to be one of the future mainstream trends of hardware implementation for AI algorithms, [9] such as machine learning and deep learning. In-memory computing, where calculations are carried out in situ within each memory unit, [10] has massive parallelism and distributed computing characteristic through integrating millions of memory devices in a crossbar array (Figure 1), analogous to the neurobiological system. [11] Thus, it can radically subvert the von Neumann architecture and achieve the fusion of data processing and storage, thereby totally eliminating the latency and energy loss associated with data access. For enabling this computational architecture, new material systems and highperformance memory devices are highly pursued.2D layered materials refer to the material family that held together by strong in-plane chemical bonds and relatively weak out-of-plane van der Waals interactions, and have attracted worldwide attention due to their unique structure and physical properties. [12][13][14][15][16][17][18][19][20][21] On the one hand, the atomically thin thickness of 2D layered materials provide a significant advantage in achieving high-density integration and low-power operation of high-performance devices. [22][23][24][25][26][27][28] On the other hand, their dangling-bond-free surface and planar structure make them not only compatible with traditional wafer technology, but also can be stacked on top of each other unrestricted by lattice mismatch. Thus, varieties of available 2D layered materials with different electrical properties, such as graphene, transition metal dichalcogenides (TMDs, including MoS 2 , WSe 2 , MoTe 2 , etc.), black phosphorus (BP) and hexagonal boron nitride (h-BN), could be arbitrarily assembled to create a wide range of artificial van der Waals heterostructures (vdWHs) with wholly new functionalities that unavailable in the individual material. [29][30][31][32][33] For example, stable data storage can be realized with the aid of potential barrier formed in vdWHs. [34][35][36][37][38][39] Additionally, It is predicted that the conventional von Neumann computing architecture cannot meet the demands of future data-intensive computing applications due to the bottleneck between the processing and memory units. To try to solve this problem, in-memory computing technology, where calculations are carried out in situ within each nonv...

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Section: Memory Implemented By 2d Layered Materialsmentioning

confidence: 99%

Emerging 2D Memory Devices for In‐Memory Computing

et al. 2021

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“…To date, the FeFETs were demonstrated to exhibit either good retention or endurance, whereas complementary performance characteristics were poor. The range of characteristics was shown to be quite broad varying from some second/hour retention time and endurance up to 10 12 cycles 231,232 to a 10-year retention time and endurance down to ∼10 3 -10 4 cycles. [233][234][235][236][237][238][239][240] So far, the optimal ratio between retention (10 years at 85 °C) and endurance (10 7 cycles) was demonstrated by Chatterjee et al 241 The short memory retention time was attributed to two major reasons: (1) the depolarization field and (2) the charge trapping/gate leakage current (given the absence of a mobile ionic charge).…”

Section: Iv3 Ferroelectric Field-effect Transistor (Fefet)mentioning

confidence: 99%

Defects in ferroelectric HfO₂

et al. 2021

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“…Such NVM technologies include resistive random-access memory (RRAM), phase-change memory (PCM), ferroelectric Field-Effect Transistors (FET), and capacitors [4][5][6][7][8][9][10]. Among them, ferroelectric Hf-based oxides have been given wide attention for applications such as negative capacitance (NC) FETs, nonvolatile FeFET, 3D ferroelectric capacitors, ferroelectric diode, and ferroelectric tunnel junctions (FTJ) [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Unipolar Parity of Ferroelectric-Antiferroelectric Characterized by Junction Current in Crystalline Phase Hf1−xZrxO2 Diodes

et al. 2021

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Ferroelectric (FE) Hf1−xZrxO2 is a potential candidate for emerging memory in artificial intelligence (AI) and neuromorphic computation due to its non-volatility for data storage with natural bi-stable characteristics. This study experimentally characterizes and demonstrates the FE and antiferroelectric (AFE) material properties, which are modulated from doped Zr incorporated in the HfO2-system, with a diode-junction current for memory operations. Unipolar operations on one of the two hysteretic polarization branch loops of the mixed FE and AFE material give a low program voltage of 3 V with an ON/OFF ratio >100. This also benefits the switching endurance, which reaches >109 cycles. A model based on the polarization switching and tunneling mechanisms is revealed in the (A)FE diode to explain the bipolar and unipolar sweeps. In addition, the proposed FE-AFE diode with Hf1−xZrxO2 has a superior cycling endurance and lower stimulation voltage compared to perovskite FE-diodes due to its scaling capability for resistive FE memory devices.

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Low power 1T DRAM/NVM versatile memory featuring steep sub-60-mV/decade operation, fast 20-ns speed, and robust 85°C-extrapolated 10<sup>16</sup> endurance

Cited by 20 publications

References 2 publications

Emerging 2D Memory Devices for In‐Memory Computing

Emerging 2D Memory Devices for In‐Memory Computing

Defects in ferroelectric HfO₂

Unipolar Parity of Ferroelectric-Antiferroelectric Characterized by Junction Current in Crystalline Phase Hf1−xZrxO2 Diodes

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Low power 1T DRAM/NVM versatile memory featuring steep sub-60-mV/decade operation, fast 20-ns speed, and robust 85&#x00B0;C-extrapolated 10<sup>16</sup> endurance

Cited by 20 publications

References 2 publications

Emerging 2D Memory Devices for In‐Memory Computing

Emerging 2D Memory Devices for In‐Memory Computing

Defects in ferroelectric HfO2

Unipolar Parity of Ferroelectric-Antiferroelectric Characterized by Junction Current in Crystalline Phase Hf1−xZrxO2 Diodes

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Low power 1T DRAM/NVM versatile memory featuring steep sub-60-mV/decade operation, fast 20-ns speed, and robust 85°C-extrapolated 10<sup>16</sup> endurance

Defects in ferroelectric HfO₂