Purely Electronic Switching with High Uniformity, Resistance Tunability, and Good Retention in Pt‐Dispersed SiO<sub>2</sub> Thin Films for ReRAM

Choi, Byung Joon; Chen, Albert B. K.; Yang, Xiang; Chen, I‐Wei

doi:10.1002/adma.201102132

Cited by 98 publications

(53 citation statements)

References 42 publications

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“…(ζ HR essentially defines the spatial extent of electron's wave-function, which decays exponentially in a random material.) Meanwhile, V c * is thickness independent in the nanometallic regime 824. These unique attributes allow additional freedom to increase R c and decrease P by many orders of magnitude by using thicker films to take advantage of their higher HRS (see Figure 6a).…”

Section: Resultsmentioning

confidence: 99%

“…Off-switching power should be proportional to the area of the resistance cell if the voltage/current density required to trigger switching is independent of the area. Indeed, literature data of off-switching power of some 20 RRAMs shown in Figure 1 support such a “scaling law”: they vary from the mW range for micrometer-sized devices to the μW range for nanometer-sized devices2345678910111213141516171819202122. Recognizing such a trend, our goal here is to systematically seek scalable strategies to further lower the power for RRAM off-switching.…”

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confidence: 90%

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Dynamic-Load-Enabled Ultra-low Power Multiple-State RRAM Devices

Yang

Chen

2012

Sci Rep

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Bipolar resistance-switching materials allowing intermediate states of wide-varying resistance values hold the potential of drastically reduced power for non-volatile memory. To exploit this potential, we have introduced into a nanometallic resistance-random-access-memory (RRAM) device an asymmetric dynamic load, which can reliably lower switching power by orders of magnitude. The dynamic load is highly resistive during on-switching allowing access to the highly resistive intermediate states; during off-switching the load vanishes to enable switching at low voltage. This approach is entirely scalable and applicable to other bipolar RRAM with intermediate states. The projected power is 12 nW for a 100 × 100 nm2 device and 500 pW for a 10 × 10 nm2 device. The dynamic range of the load can be increased to allow power to be further decreased by taking advantage of the exponential decay of wave-function in a newly discovered nanometallic random material, reaching possibly 1 pW for a 10×10 nm2 nanometallic RRAM device.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 90%

Dynamic-Load-Enabled Ultra-low Power Multiple-State RRAM Devices

Yang

Chen

2012

Sci Rep

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“…The adopted Verilog-A Memristor model is based on the device parameters from [26], which have been scaled to 65 nm technology according to the resistance and area relation in [37]. The memristor material is W/SiGe/a-Si/Ag and the read current applied on the memristor is 2 A.…”

Section: A Circuit Level Implementation and Simulationmentioning

confidence: 99%

Harmonica: A Framework of Heterogeneous Computing Systems With Memristor-Based Neuromorphic Computing Accelerators

Liu

Mao

Liu

et al. 2016

IEEE Trans. Circuits Syst. I

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Following technology scaling, on-chip heterogeneous architecture emerges as a promising solution to combat the power wall of microprocessors. This work presents Harmonica-a framework of heterogeneous computing system enhanced by memristor-based neuromorphic computing accelerators (NCAs). In Harmonica, a conventional pipeline is augmented with a NCA which is designed to speedup artificial neural network (ANN) relevant executions by leveraging the extremely efficient mixed-signal computation capability of nanoscale memristor-based crossbar (MBC) arrays. With the help of a mixed-signal interconnection network (M-Net), the hierarchically arranged MBC arrays can accelerate the computation of a variety of ANNs. Moreover, an inline calibration scheme is proposed to ensure the computation accuracy degradation incurred by the memristor resistance shifting within an acceptable range during NCA executions. Compared to general-purpose processor, Harmonica can achieve on average 27.06× performance speedup and 25.23× energy savings when the NCA is configured with auto-associative memory (AAM) implementation. If the NCA is configured with multilayer perception (MLP) implementation, the performance speedup and energy savings can be boosted to 178.41× and 184.24×, respectively, with slightly degraded computation accuracy. Moreover, the performance and power efficiency of Harmonica are superior to the designs with either digital neural processing units (D-NPUs) or MBC arrays cooperating with a digital interconnection network. Compared to the baseline of general-purpose processor, the classification rate degradation of Harmonica in MLP or AAM is less than 8% or 4%, respectively. . His research interests include large-scale neuromorphic computing circuits and systems, highperformance computing architectures, energy-efficient embedded computing.Jianhua (Joshua) Yang (M'08) received the B.A. . He spent over 8 years at HP Labs before joining UMass in 2015. His current research interests are Nanoelectronics and Nanoionics, especially for unconventional computing applications, where he authored and co-authored over 100 papers in peer-reviewed academic journals and conferences, and holds 61 granted and over 70 pending US Patents.Hai (Helen) Li (M'08-SM'16) received the B.S. and M.S. degrees in microelectronics from Tsinghua University,

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“…6(a)). The Ohmic behavior for ON state was observed before for a Pt dispersed SiO x device, 33 and the Ohmic ON state was explained by metal-insulator transition of random material. But it was a bipolar resistive device.…”

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confidence: 96%

Low voltage resistive switching devices based on chemically produced silicon oxide

Jiang

Xia

2013

Applied Physics Letters

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We developed nonvolatile metal/SiOx/Si memristive devices based on ultrathin (∼1 nm) silicon oxide that was produced in a Piranha solution. The devices exhibited repeatable resistive switching behavior with low programming voltages (as low as 0.5 V) and high ON/OFF conductance ratio. Devices with active metals as top electrodes were bipolar switches, while those with inert metal electrodes were unipolar. We also studied the switching mechanisms for both types of devices based on the filament formation and rupture, and proposed conduction models for Pt/SiOx/Si devices.

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Purely Electronic Switching with High Uniformity, Resistance Tunability, and Good Retention in Pt‐Dispersed SiO₂ Thin Films for ReRAM

Cited by 98 publications

References 42 publications

Dynamic-Load-Enabled Ultra-low Power Multiple-State RRAM Devices

Dynamic-Load-Enabled Ultra-low Power Multiple-State RRAM Devices

Harmonica: A Framework of Heterogeneous Computing Systems With Memristor-Based Neuromorphic Computing Accelerators

Low voltage resistive switching devices based on chemically produced silicon oxide

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Purely Electronic Switching with High Uniformity, Resistance Tunability, and Good Retention in Pt‐Dispersed SiO2 Thin Films for ReRAM

Cited by 98 publications

References 42 publications

Dynamic-Load-Enabled Ultra-low Power Multiple-State RRAM Devices

Dynamic-Load-Enabled Ultra-low Power Multiple-State RRAM Devices

Harmonica: A Framework of Heterogeneous Computing Systems With Memristor-Based Neuromorphic Computing Accelerators

Low voltage resistive switching devices based on chemically produced silicon oxide

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Purely Electronic Switching with High Uniformity, Resistance Tunability, and Good Retention in Pt‐Dispersed SiO₂ Thin Films for ReRAM