A 76-mm/sup 2/ 8-Mb chain ferroelectric memory

Takashima, D.; Takeuchi, Yoshinori; Miyakawa, T.; Itoh, Y.; Ogiwara, R.; Kamoshida, M.; Hoya, K.; Doumae, Sumiko; Ozaki, T.; Kanaya, Haruichi; Aoki, Masamitsu; Yamaguchi, Koji; Kunishima, I.; Oowaki, Y.

doi:10.1109/4.962293

Cited by 29 publications

(8 citation statements)

References 9 publications

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“…[99], fast switching speed [100], and almost unlimited cycling endurance [101,102]. Figure 11g shows the MIM stack of FeRAM, where an insulating layer based on a ferroelectric (FE) material, typically in doped HfO 2 [103] or perovskite materials [104,105], is sandwiched between two metal electrodes. Its operation principle relies on the polarization switching within the FE layer due to the rotation of electrical dipoles under an external bias [106].…”

Section: Memristive Devices With 2-terminal Structurementioning

confidence: 99%

Memristive and CMOS Devices for Neuromorphic Computing

et al. 2020

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Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of von Neumann architecture of conventional digital processors. The aim of neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness. Toward this goal, however, some major challenges have to be faced. Since the brain processes information by high-density neural networks with ultra-low power consumption, novel device concepts combining high scalability, low-power operation, and advanced computing functionality must be developed. This work provides an overview of the most promising device concepts in neuromorphic computing including complementary metal-oxide semiconductor (CMOS) and memristive technologies. First, the physics and operation of CMOS-based floating-gate memory devices in artificial neural networks will be addressed. Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures. Finally, the main technology challenges and perspectives of neuromorphic computing will be discussed.

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Section: Memristive Devices With 2-terminal Structurementioning

confidence: 99%

Memristive and CMOS Devices for Neuromorphic Computing

et al. 2020

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“…Figure 2h shows the typical polarization-voltage (P-V) characteristic of an FERAM device, indicating that an applied positive voltage sweep results in the permanent polarization with remnant polarization +P r , while a negative sweep results in a permanent remnant polarization -P r . Typical FE materials for implementation in FERAM technology are PbZrTiO 3 (PZT), [114] BiSrTiO 3 (BST), [115] and doped HfO 2 , [116] the latter raising intense interest due to the large relevance of HfO 2 as a CMOS compatible material, extensively adopted as gate dielectric and RRAM material. While the polarization switching can be sensed by a transient displacement current, there is generally no variation of resistance of the FERAM, which therefore cannot be used as a resistive memory similar to other memory concepts in Figure 2.…”

Section: Stochastic Memristive Devicesmentioning

confidence: 99%

“…While the polarization switching can be sensed by a transient displacement current, there is generally no variation of resistance of the FERAM, which therefore cannot be used as a resistive memory similar to other memory concepts in Figure . Typical FE materials for implementation in FERAM technology are PbZrTiO 3 (PZT), BiSrTiO 3 (BST), and doped HfO 2 , the latter raising intense interest due to the large relevance of HfO 2 as a CMOS compatible material, extensively adopted as gate dielectric and RRAM material.…”

Section: Stochastic Memristive Devicesmentioning

confidence: 99%

Stochastic Memory Devices for Security and Computing

Carboni

Ielmini

2019

Adv Elect Materials

110

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With the widespread use of mobile computing and internet of things, secured communication and chip authentication have become extremely important. Hardware‐based security concepts generally provide the best performance in terms of a good standard of security, low power consumption, and large‐area density. In these concepts, the stochastic properties of nanoscale devices, such as the physical and geometrical variations of the process, are harnessed for true random number generators (TRNGs) and physical unclonable functions (PUFs). Emerging memory devices, such as resistive‐switching memory (RRAM), phase‐change memory (PCM), and spin‐transfer torque magnetic memory (STT‐MRAM), rely on a unique combination of physical mechanisms for transport and switching, thus appear to be an ideal source of entropy for TRNGs and PUFs. An overview of stochastic phenomena in memory devices and their use for developing security and computing primitives is provided. First, a broad classification of methods to generate true random numbers via the stochastic properties of nanoscale devices is presented. Then, practical implementations of stochastic TRNGs, such as hardware security and stochastic computing, are shown. Finally, future challenges to stochastic memory development are discussed.

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“…The displacement current developed due to polarization switching is measured in FeRAM. 36,37 It is noteworthy that the dipole polarization switching does not change the resistance of the active material. By contrast, two ferromagnetic half-metallic electrodes, such as CoFeB, and insulating materials (e.g., MgO) in the MIM stack structure demonstrate STT-MRAM.…”

Section: Introductionmentioning

confidence: 99%