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CPU DB: Recording Microprocessor History

Andrew Danowitz, Kyle Kelley, James Mao, John P. Stevenson, Mark Horowitz
2012 Queue  
P P + = 攀渀攀爀最礀 漀瀀 딀 倀 倀攀爀 昀 氀 ㈀ 嘀 ㈀ ㈀ 氀 嘀 ㈀ ⬀ 倀 挀愀挀栀攀 倀 攀爀 昀 䘀 伀 㐀 ㈀ 䘀 伀 㐀 With PHYSICAL SCALING One of the nice side benefits of collecting this database is that it allows one to see how chip complexity  ...  Normalized Area norm norm A f er P 46 . 0 α * * The regression yields Perf norm ∝ n 0.37 trans Perf norm ∝ n 0.46 trans © 2012 ACM 1542-7730/12/0200 $10.00  ... 
doi:10.1145/2181796.2181798 fatcat:ykvzzzyaubezllzdgwbwx7mhdi

Probing temperature-induced phase transitions at individual ferroelectric domain walls [article]

Kyle P. Kelley, Sergei V. Kalinin, Eugene Eliseev, Shivaranjan Raghuraman, Stephen Jesse, Peter Maksymovych, Anna N. Morozovska
2022 arXiv   pre-print
As such, the equations of state for elastic stresses 𝜎 𝑖𝑗 and strains 𝑢 𝑖𝑗 are: 𝑢 𝑖𝑗 = 𝑠 𝑖𝑗𝑘𝑙 𝜎 𝑘𝑙 + 𝛽 𝑖𝑗 (𝑇 − 𝑇 0 ) + 𝑄 𝑖𝑗𝑘𝑙 𝑃 𝑘 𝑃 𝑙 , (3) Analysis of the SPS polarization  ...  potential 𝜑 and Landau-Ginzburg-Devonshire (LGD) equation for the 𝑃 3 : Figure 5 : 5 Figure 5: The top view of polarization 𝑃 3 across 180-degree domain wall (DW) at the SPS surface calculated for  ... 
arXiv:2207.03321v1 fatcat:y5serjn4mnf6jkkooqpztrusui

Exploring leakage in dielectric films via automated experiment in scanning probe microscopy [article]

Yongtao Liu, Shelby S. Fields, Takanori Mimura, Kyle P. Kelley, Jon F. Ihlefeld, Sergei V. Kalinin
2021 arXiv   pre-print
Full growth details are provided elsewhere. 24 Briefly, the TaN bottom electrode was sputtered from a TaN target onto a p-type (100)-oriented silicon wafer with a native oxide layer.  ... 
arXiv:2111.09918v1 fatcat:i5djwxuelrfhzi4uvxv3p6ta7a

Learning the right channel in multimodal imaging: automated experiment in Piezoresponse Force Microscopy [article]

Yongtao Liu, Rama K. Vasudevan, Kyle P. Kelley, Hiroshi Funakubo, Maxim Ziatdinov, Sergei V. Kalinin
2022 arXiv   pre-print
We report the development and experimental implementation of the automated experiment workflows for the identification of the best predictive channel for a phenomenon of interest in spectroscopic measurements. The approach is based on the combination of ensembled deep kernel learning for probabilistic predictions and a basic reinforcement learning policy for channel selection. It allows the identification of which of the available observational channels, sampled sequentially, are most
more » ... of selected behaviors, and hence have the strongest correlations. We implement this approach for multimodal imaging in Piezoresponse Force Microscopy (PFM), with the behaviors of interest manifesting in piezoresponse spectroscopy. We illustrate the best predictive channel for polarization-voltage hysteresis loop and frequency-voltage hysteresis loop areas is amplitude in the model samples. The same workflow and code are universal and applicable for any multimodal imaging and local characterization methods.
arXiv:2207.03039v1 fatcat:4gqcs6da5zcjzivnfeihadixxm

Ferroelectricity in Hafnia Controlled via Surface Electrochemical State [article]

Kyle P. Kelley, Anna N. Morozovska, Eugene A. Eliseev, Yongtao Liu, Shelby S. Fields, Samantha T. Jaszewski, Takanori Mimura, Jon F. Ihlefeld, Sergei V. Kalinin
2022 arXiv   pre-print
) ( " ) 𝑃 ! ) + ( "# * 𝑃 ! ) 𝑃 + ) − 𝑃 ! 𝐸 ! + , "# ) 𝑃 ! ) 𝐴 + ) + " " ) 𝐴 ! ) + " "# * 𝐴 ! ) 𝐴 + ) -, (1) Here 𝑉 -is the film volume. The coefficients 𝑎 ! and 𝑏 !  ...  Note that transitions from the structural phase (𝐴 ≠ 0, 𝑃 = 0) to the polar phase (𝐴 = 0, 𝑃 ≠ 0) defines antiferroelectric properties of the material. 53, 54 ere, the electric field 𝐸 !  ... 
arXiv:2207.12525v1 fatcat:e2paq4jvx5bozhietquvgt74vq

Exploring Physics of Ferroelectric Domain Walls in Real Time: Deep Learning Enabled Scanning Probe Microscopy [article]

Yongtao Liu, Kyle P. Kelley, Hiroshi Funakubo, Sergei V. Kalinin, Maxim Ziatdinov
2022 arXiv   pre-print
In this approach, the predictive mean and uncertainty on the newly observed data 𝑥 * given the training set D are expressed as 𝑃(𝑥 * |𝐷) = ∫ 𝑃(𝑥 * |𝑤)𝑃(𝑤|𝐷)𝑑𝑤 𝜃 ≈ 1 𝑁 ∑ 𝑃(𝑥 * |𝑤 𝑛 , 𝐷  ...  ) = 𝑓 * N 𝑛=1 , (1a) 𝑈[𝑓 * ] = 1 𝑁 ∑ (𝑓 * 𝑛 − 𝑓 * ̂)2 𝑁 𝑛=1 , ( 1b ) where 𝑤 𝑛 ~𝑃(𝑤|𝐷) are neural network weights drawn from the posterior.  ... 
arXiv:2206.11457v1 fatcat:t6mt77oe4ngnzj4b54mdirp6ki

Automated and Autonomous Experiment in Electron and Scanning Probe Microscopy [article]

Sergei V. Kalinin, Maxim A. Ziatdinov, Jacob Hinkle, Stephen Jesse, Ayana Ghosh, Kyle P. Kelley, Andrew R. Lupini, Bobby G. Sumpter, Rama K. Vasudevan
2021 arXiv   pre-print
Spatial Location Defect at Wall Pr istine P P Defect Wall Pr ist ine Wall Phy sical Model  ... 
arXiv:2103.12165v1 fatcat:z3uh2jxrgfbf7d4kol6ed6bcra

Characterization of the abiotic drivers of abundance of nearshore Arctic fishes

Noah S. Khalsa, Kyle P. Gatt, Trent M. Sutton, Amanda L. Kelley
2021 Ecology and Evolution  
- - - - 0.54 ± 0.58 p-value 0.99 0.01 0.01 0.82 0.36 Rainbow smelt df 0.00 1.40 0.98 0.84 - 0.46 Coefficient ± SE - - - - −2.21 ± 0.34 p-value 0.76 0.00 0.01 0.01 0.00 Round whitefish df 0.60 0.00 0.85  ...  stickleback df 0.00 0.93 0.00 1.26 - 0.27 Coefficient ± SE - - - - 1.02 ± 0.23 p-value 0.96 0.00 0.57 0.04 0.00 Whitespotted greenling df 0.94 0.00 0.40 0.84 - 0.79 Coefficient ± SE - - - - - p-value  ... 
doi:10.1002/ece3.7940 pmid:34429935 pmcid:PMC8366885 fatcat:4dbrqcuuljekjhvnatsys42uoq

Automated Experiments of Local Non-linear Behavior in Ferroelectric Materials [article]

Yongtao Liu, Kyle P. Kelley, Rama K. Vasudevan, Wanlin Zhu, John Hayden, Jon-Paul Maria, Hiroshi Funakubo, Maxim A. Ziatdinov, Susan Trolier-McKinstry, Sergei V. Kalinin
2022 arXiv   pre-print
to the physics of ferroelectric materials; intrinsic nonlinearities produce a fielddependence in properties such as the dielectric susceptibility, even in single domain single crystals. 11, 13, 14 P  ...  Alternatively, Kelley, 67 Liu, 68 and Volpe 69 have shown that in combination, computer vision and automated experiments can select locations for in-depth spectroscopic studies based on a-priori  ... 
arXiv:2206.15110v1 fatcat:t24zonymynbcnk5nffh4jx6zay

Bayesian inference in band excitation Scanning Probe Microscopy for optimal dynamic model selection in imaging [article]

Rama K. Vasudevan, Kyle P. Kelley, Eugene Eliseev, Stephen Jesse, Hiroshi Funakubo, Anna Morozovska, Sergei V. Kalinin
2020 arXiv   pre-print
The corresponding p(M) map for the 2D synthetic dataset is shown in Figure 5 .  ...  The Duffing model is preferred for all cases (probability p>0.5), but the distinguishability becomes more difficult in higher noise settings.  ... 
arXiv:2002.08391v1 fatcat:l34zsdiaqrdejeya62lasuy34a

Unusual Electrical Conductivity Driven by Localized Stoichiometry Modification at Vertical Epitaxial Interfaces [article]

Wenrui Zhang, Shaobo Cheng, Christopher M Rouleau, Kyle P. Kelley, Jong Keum, Eli Stavitski, Yimei Zhu, Matthew F. Chisholm, Zheng Gai, Gyula Eres
2020 arXiv   pre-print
Precise control of lattice mismatch accommodation and cation interdiffusion across the interface is critical to modulate correlated functionalities in epitaxial heterostructures, particularly when the interface composition is positioned near a compositional phase transition boundary. Here we select La1-xSrxMnO3 (LSMO) as a prototypical phase transition material and establish vertical epitaxial interfaces with NiO to explore the strong interplay between strain accommodation, stoichiometry
more » ... ation, and localized electron transport across the interface. It is found that localized stoichiometry modification overcomes the plaguing dead layer problem in LSMO and leads to strongly directional conductivity, as manifested by more than three orders of magnitude difference between out-of-plane to in-plane conductivity. Comprehensive structural characterization and transport measurements reveal that this emerging behavior is related to a compositional change produced by directional cation diffusion that pushes the LSMO phase transition from insulating into metallic within an ultrathin interface region. This study explores the nature of unusual electric conductivity at vertical epitaxial interfaces and establishes an effective route for engineering nanoscale electron transport for oxide electronics.
arXiv:2007.14668v1 fatcat:yancann2dbeqbprplu5bej32re

Hypothesis-Driven Automated Experiment in Scanning Probe Microscopy: Exploring the Domain Growth Laws in Ferroelectric Materials [article]

Yongtao Liu, Anna Morozovska, Eugene Eliseev, Kyle P. Kelley, Rama Vasudevan, Maxim Ziatdinov, Sergei V. Kalinin
2022 arXiv   pre-print
where The acquisition function value in each unmeasured point 𝑥 * was equal to the posterior predictive uncertainty 𝜃 𝑛 ~ 𝑃(𝜃|𝐷) were samples drawn from the posterior and D was the available (measured  ... 
arXiv:2202.01089v1 fatcat:spxs7qa7zjbgnikgsd4qz65t7e

Fast Scanning Probe Microscopy via Machine Learning: Non-rectangular scans with compressed sensing and Gaussian process optimization [article]

Kyle P. Kelley, Maxim Ziatdinov, Liam Collins, Michael A. Susner, Rama K. Vasudevan, Nina Balke, Sergei V. Kalinin, Stephen Jesse
2020 arXiv   pre-print
Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functional imaging are few in number. Here, using piezoresponse force microscopy (PFM) as a model application, we demonstrate a factor of 5.8 improvement in imaging rate using a combination of sparse spiral scanning with compressive sensing and Gaussian processing reconstruction. It is found that even extremely sparse scans
more » ... er strong reconstructions with less than 6 % error for Gaussian processing reconstructions. Further, we analyze the error associated with each reconstructive technique per reconstruction iteration finding the error is similar past approximately 15 iterations, while at initial iterations Gaussian processing outperforms compressive sensing. This study highlights the capabilities of reconstruction techniques when applied to sparse data, particularly sparse spiral PFM scans, with broad applications in scanning probe and electron microscopies.
arXiv:2004.11817v1 fatcat:4zyiumvtizbtrmt7sqgoy4rehy

Polaritonic hybrid-epsilon-near-zero modes: engineering strong optoelectronic coupling and dispersion in doped cadmium oxide bilayers [article]

Evan L. Runnerstrom, Kyle P. Kelley, Thomas G. Folland, Nader Engheta, Joshua D. Caldwell, Jon-Paul Maria
2018 arXiv   pre-print
ω 2 +iγω is the dielectric function of the plasmonic layer (ε∞, ωp, γ: high frequency dielectric constant, plasma frequency, damping frequency). † ω ≈ ωp 1 − kxd 4 − i γ 2 , where d is film thickness,  ...  Note: reflectivity maps plot R = R p /R s (the ratio of reflected p-polarized to s-polarized light) on the color axis, energy (wavenumbers) on the y-axis, and the film-parallel component of incident wavevector  ... 
arXiv:1808.03847v1 fatcat:73jncik3pfenfox3ajbnq3ulwi

Experimental discovery of structure-property relationships in ferroelectric materials via active learning [article]

Yongtao Liu, Kyle P. Kelley, Rama K. Vasudevan, Hiroshi Funakubo, Maxim A. Ziatdinov, Sergei V. Kalinin
2021 arXiv   pre-print
error map of DKL prediction and the histogram distribution of error, (o-p) the embedded latent maps of the trained DKL.  ...  DCNN based image recognition. 36 For example, in piezoresponse force microscopy (PFM), the AE was introduced based on a line-by-line feedback system employed during classical rectangular scanning by Kelley  ... 
arXiv:2108.06037v2 fatcat:2csyo7lp6zhnbb7ng6bbir6b3a
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