A novel low power phase change memory using inter-granular switching

Lung, Hsiang-Lan; Ho, Yung-Han; Zhu, Yu; Chien, Wei-Ming; Kim, S.; Kim, W.; Cheng, Huai-Yu; Ray, A.; BrightSky, M.; Bruce, Robert L.; Yeh, Chau‐Shioung; Lam, C.

doi:10.1109/vlsit.2016.7573405

Cited by 14 publications

(7 citation statements)

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“…Nevertheless, the main challenge to be overcome in PCM to target ultra low power applications in advanced technology nodes is the programming current reduction. Main efforts in recent years have been oriented to the development of scaled architectures [3,4] and to the engineering of the active material [5,6]. However, the main part of the power used during the programming operations of a PCM device is represented by the heat losses [1], making the PCM performance to be far from the adiabatic limit represented by a perfect thermal isolation and zero losses [7].…”

Section: Introductionmentioning

confidence: 99%

Phase-change memory electro-thermal analysis and engineering thanks to enhanced thermal confinement

Serra

Lefèvre

Cueto

et al. 2021

Solid-State Electronics

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In this paper we compare the performances of SiN with respect to an optimized SiC encapsulation in Wall based Phase-Change Memory (PCM) integrating a Ge-rich Ge-Sb-Te alloy (GGST) suitable for high temperature stability in automotive applications. Thanks to the electrical characterization of 4kb arrays, 3D electro-thermal simulations and TEM analyses performed on programmed devices, we demonstrate the higher programming efficiency in SiC-based PCM devices, thanks to the lower thermal conductivity of the optimized encapsulation. Indeed, the uniform temperature profile achieved in the active layer of SiC encapsulated PCM leads to a retention of one hour at 250 • C. A theoretical model is here proposed to describe the electro-thermal behavior of the device, linking the electrical properties, such as the resistance as a function of current characteristics, to the thermal conductivity of the materials that constitute the device. Finally, thanks to our findings, we provide some guidelines to achieve drastic current reduction via the thermal engineering of the next generation PCM technology.

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Section: Introductionmentioning

confidence: 99%

Phase-change memory electro-thermal analysis and engineering thanks to enhanced thermal confinement

Serra

Lefèvre

Cueto

et al. 2021

Solid-State Electronics

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“…Phase‐change random access memory (PCRAM) offers low power consumption, fast switching speeds, and excellent scalability . Recently, it was shown that the switching properties of Ge–Sb–Te based materials used in PCRAM can be further improved by arranging the pseudobinary components Ge–Te and Sb–Te into a superlattice (SL) structure, leading to a significant decrease in switching power consumption .…”

mentioning

confidence: 99%

High‐Speed Bipolar Switching of Sputtered Ge–Te/Sb–Te Superlattice iPCM with Enhanced Cyclability

Mitrofanov

Saito

Miyata

et al. 2019

Physica Rapid Research Ltrs

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Ge–Sb–Te alloy based phase‐change memory devices have recently been shown to be switchable in a bipolar mode. However, to date bipolar switching has only been demonstrated at relatively low speeds (on the order of tens of µs), and with endurance limited to 104 switching cycles. In this work, in lieu of a Ge–Sb–Te alloy, fast and durable bipolar switching is demonstrated in interfacial phase change memory (iPCM). It is revealed that in iPCM devices, bipolar switching can be carried out at least a factor of 1000 times faster and with at least 1000 times greater endurance than in conventional Ge–Sb–Te alloy based phase‐change devices. Additionally, bipolar switching was found to exhibit a larger RESET to SET resistance ratio and lower power consumption compared to conventional Ge–Sb–Te alloy based devices.

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“…low-current/low-power processing, high resistance ratio, high speed, extended endurance/ retention, CMOS compatibility, analog reconfigurability, and design scalability [3,[13][14][15][16]). For instance, different materials are frequently used for resistive switching layers in memory devices, such as TiO 2 , HfO 2 , Ta 2 O 5 , WO 3 [17][18][19][20][21][22][23][24][25][26] for metal oxides ReRAM devices; GaSbGe, GST, GeTe/SbTe, graphene [27][28][29][30][31][32][33] for phase change memory devices; and SiO 2 , Zr, ZnO:Cr doped HfO 2 , and perovskites [34][35][36][37][38][39] for ferroelectric memories. Although the resistive switching mechanism may vary from one device to another and from technology to another (e.g.…”

Section: Modeling Framework and Comparison Of Memristive Devices And ...mentioning

confidence: 99%

Modeling framework and comparison of memristive devices and associated STDP learning windows for neuromorphic applications

Zayer

Dghais

Hamdi

2019

J. Phys. D: Appl. Phys.

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This paper presents a comparative synthesis of the suitability of three memristive device technologies and their corresponding spike-timing-dependent plasticity (STDP) learning windows for neuromorphic applications. The physical mechanisms behind the nonlinear switching memristive dynamics of ReRAM, based on titanium dioxide, ferroelectric tunnel junctions, and phase change memory are analyzed towards the development of accurate and computationally efficient compact models which are implemented as a Verilog-A description. The developed Verilog-A compact models are separately validated and compared with the measurement data. Moreover, the asynchronous STDP learning rule is implemented using the above mentioned memristive devices as artificial synapse for spike-based neuromorphic computing. The considered memristive technologies are compared and discussed towards their integration in fast and/or large-scale circuit implementations.

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A novel low power phase change memory using inter-granular switching

Cited by 14 publications

References 0 publications

Phase-change memory electro-thermal analysis and engineering thanks to enhanced thermal confinement

Phase-change memory electro-thermal analysis and engineering thanks to enhanced thermal confinement

High‐Speed Bipolar Switching of Sputtered Ge–Te/Sb–Te Superlattice iPCM with Enhanced Cyclability

Modeling framework and comparison of memristive devices and associated STDP learning windows for neuromorphic applications

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