Resistive Memory Process Optimization for High Resistance Switching Toward Scalable Analog Compute Technology for Deep Learning

Kim, Y.; Seo, Soon‐Cheon; Consiglio, Steven; Jamison, P.; Higuchi, Hisashi; Rasch, Malte J.; Wu, Ernest Y.; Kong, Dexin; Saraf, Iqbal; Catano, Christopher; Muralidhar, R.; Nguyen, S.; DeVries, S.; Straten, O. van der; Sankarapandian, M.; Pujari, R.; Gasasira, Arthur; McDermott, Steven; Miyazoe, Hiroyuki; Koty, Devi; Yang, Qing; Yan, Hao; Clark, Robert D.; Tapily, Kandabara; Engelmann, Sebastian; Robison, Robert R.; Wajda, Cory S.; Mosden, Aelan; Tsunomura, T.; Soave, R.; Saulnier, Nicole; Haensch, Wilfried; Leusink, Gert J.; Biolsi, Peter; Narayanan, V.; Ando, Takashi

doi:10.1109/led.2021.3066181

Cited by 9 publications

(11 citation statements)

References 25 publications

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“…Kim et al put forward an iterative process that can monotonically improve the strategy, and by approximating the theoretical formula, gave a workable learning algorithm. After analysis, the standard strategy gradient and the strategy gradient of neural network were unified [20]. Therefore, as an integral part of university IPE, it is obligatory to participate in the construction of IPE education and teaching resource platform.…”

Section: Technologymentioning

confidence: 99%

“…[17] 93.24 92.28 Ref. [20] 95.57 96.68 Our 98.96 97. 14 6 Wireless Communications and Mobile Computing baseline model, which proves that intention information can improve the precision of slot value prediction.…”

Section: Model F1 (%) Accuracy (%)mentioning

confidence: 99%

“…The above results show that this model not only has a strong ability to process data with high dimensions and complex features but also has a stronger ability to extract local features, thus effectively improving the classification accuracy of shared data. Ref [15] Ref [17] Ref [20] Our Degree of accuracy (%) Model F1 Accuracy…”

Section: Model F1 (%) Accuracy (%)mentioning

confidence: 99%

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Construction of Data Resource Sharing Platform in College Students’ Ideological and Political Education Based on Deep Learning

Qiu

Xiao

Nguyen

2022

Wireless Communications and Mobile Computing

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At present, the irrational allocation of IPE (ideological and political education) resources in universities reflects the dilemma of university’s control of university students’ IPE system. In order to make educational big data really useful to us and really promote the sharing of educational resources and improve the utilization rate of resources, this paper discusses the construction of data resource sharing platform for college students in IPE field based on DL (Deep learning) based on the characteristics and existing problems of educational big data. Based on DL, the collected data are parameterized, the matching algorithm parameters are determined, the dynamic data sample subset is incrementally reduced, the discernibility matrix and logical analytic formula are established, the conjunctive normal form is obtained through calculation, and the kernel attribute is introduced into each conjunctions to realize incremental mining of dynamic data. The results show that the model in this paper achieves the best classification effect on six samples, with an average classification accuracy of 88.81%. The results show that the comprehensive shared data matching algorithm based on DL can realize the matching in a short time, and the matching process has high stability.

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Section: Technologymentioning

confidence: 99%

Section: Model F1 (%) Accuracy (%)mentioning

confidence: 99%

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Construction of Data Resource Sharing Platform in College Students’ Ideological and Political Education Based on Deep Learning

Qiu

Xiao

Nguyen

2022

Wireless Communications and Mobile Computing

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“…The detailed information for the fabrication process of the ReRAM stacks used in this study is previously presented in Refs. [18][19][20] as discussed its key aspects in Section 2. Moreover, the schematics in Figure 2a also compares the hydrogen-plasma-driven approach with the conventional reservoir-driven approach.…”

Section: Methodsmentioning

confidence: 99%

Reversing A Decades‐Long Scaling Law of Dielectric Breakdown using Hydrogen‐Plasma‐Treated HfO₂ ReRAM Devices

Ando

Jamison

2023

Adv Elect Materials

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Dielectric breakdown (BD) is known to cause component failure in electronic devices and high‐voltage power lines over many decades. In recent years, this failure mechanism has been exploited to intentionally form nanoscale filaments in resistive random‐access‐memory (ReRAM) devices for artificial intelligence (AI). The statistical nature of this failure mechanism, known as the inverse size scaling law based on the weakest‐link theory, dictates the ever‐higher forming voltages for ReRAM devices, in turn, requiring additional peripheral transistors. This law also causes diminishing lifetimes of electronic components for future generations of very‐large‐integrated circuits (VLSI) technology. Currently, the semiconductor industry faces these scaling barriers limiting the miniaturization of both VLSI and AI hardware technology. Here, experimental evidence of a reverse area dependence is presented by introducing an innovative fabrication process with a hydrogen‐plasma‐treated layer in a bilayer HfO2 structure. Using joint order statistics rather than traditional extreme‐value statistics, a physics‐based statistical model is developed in agreement with the experimental data, thus demonstrating that there is no fundamental reason preventing this law from being reversed or altered. These findings will have a significant impact on both technology scaling and fabrication innovation for electronic and/or bioinspired nanomaterials; moreover, stimulate much research in physics, statistics, and reliability.

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“…There are many different non-volatile memories under consideration for Analog AI such as MRAM, RRAM and ECRAM; and this paper is focused on phase change memory (PCM) structures. [1][2][3][4][5][6][7]. Each of these classes has many subvariants with different structures, materials, and performance ranges.…”

Section: Introductionmentioning

confidence: 99%

Failure Analysis Challenges of Phase Change Memory Test Structures with Two Case Studies

Demarest

Arnold

Brew

et al. 2021

International Symposium for Testing and Failure Analysis

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There are several variants of artificial intelligence (AI) hardware structures which are under study by the semiconductor industry as potential future synergistic technology adders to existing complementary metal–oxide–semiconductor (CMOS) designs. This paper will discuss some of the failure analysis challenges which have appeared in discrete test structures and test arrays of an exploratory PCM program at IBM's Albany AI Hardware Research Center.

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Resistive Memory Process Optimization for High Resistance Switching Toward Scalable Analog Compute Technology for Deep Learning

Cited by 9 publications

References 25 publications

Construction of Data Resource Sharing Platform in College Students’ Ideological and Political Education Based on Deep Learning

Construction of Data Resource Sharing Platform in College Students’ Ideological and Political Education Based on Deep Learning

Reversing A Decades‐Long Scaling Law of Dielectric Breakdown using Hydrogen‐Plasma‐Treated HfO₂ ReRAM Devices

Failure Analysis Challenges of Phase Change Memory Test Structures with Two Case Studies

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Resistive Memory Process Optimization for High Resistance Switching Toward Scalable Analog Compute Technology for Deep Learning

Cited by 9 publications

References 25 publications

Construction of Data Resource Sharing Platform in College Students’ Ideological and Political Education Based on Deep Learning

Construction of Data Resource Sharing Platform in College Students’ Ideological and Political Education Based on Deep Learning

Reversing A Decades‐Long Scaling Law of Dielectric Breakdown using Hydrogen‐Plasma‐Treated HfO2 ReRAM Devices

Failure Analysis Challenges of Phase Change Memory Test Structures with Two Case Studies

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Reversing A Decades‐Long Scaling Law of Dielectric Breakdown using Hydrogen‐Plasma‐Treated HfO₂ ReRAM Devices