Frequency Response of Graphene Electrolyte-Gated Field-Effect Transistors

This work develops the first frequency-dependent small-signal model for graphene electrolyte-gated field-effect transistors (EGFETs). Graphene EGFETs are microfabricated to measure intrinsic voltage gain, frequency response, and to develop a frequency-dependent small-signal model. The transfer function of the graphene EGFET small-signal model is found to contain a unique pole due to a resistive element, which stems from electrolyte gating. Intrinsic voltage gain, cutoff frequency, and transition frequency for the microfabricated graphene EGFETs are approximately 3.1 V/V, 1.9 kHz, and 6.9 kHz, respectively. This work marks a critical step in the development of high-speed chemical and biological sensors using graphene EGFETs.United States. Office of Naval Research (Grant N00014-12-1-0959)United States. Office of Naval Research (Grant N0014-16-1-2230)United States. National Aeronautics and Space Administration (Award NNX14AH11A)United States. Army Research Office (Contract W911NF-13-D-0001

Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference

Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics, and the non-ideal peripheral circuitry in AIMC chips, require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In this tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit. The AIHWKit is a Python library that simulates inference and training of DNNs using AIMC. We present an in-depth description of the AIHWKit design, functionality, and best practices to properly perform inference and training. We also present an overview of the Analog AI Cloud Composer, that provides the benefits of using the AIHWKit simulation platform in a fully managed cloud setting. Finally, we show examples on how users can expand and customize AIHWKit for their own needs. This tutorial is accompanied by comprehensive Jupyter Notebook code examples that can be run using AIHWKit, which can be downloaded from https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators

Analog in-memory computing (AIMC) -- a promising approach for energy-efficient acceleration of deep learning workloads -- computes matrix-vector multiplications (MVMs) but only approximately, due to nonidealities that often are non-deterministic or nonlinear. This can adversely impact the achievable deep neural network (DNN) inference accuracy as compared to a conventional floating point (FP) implementation. While retraining has previously been suggested to improve robustness, prior work has explored only a few DNN topologies, using disparate and overly simplified AIMC hardware models. Here, we use hardware-aware (HWA) training to systematically examine the accuracy of AIMC for multiple common artificial intelligence (AI) workloads across multiple DNN topologies, and investigate sensitivity and robustness to a broad set of nonidealities. By introducing a new and highly realistic AIMC crossbar-model, we improve significantly on earlier retraining approaches. We show that many large-scale DNNs of various topologies, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, can in fact be successfully retrained to show iso-accuracy on AIMC. Our results further suggest that AIMC nonidealities that add noise to the inputs or outputs, not the weights, have the largest impact on DNN accuracy, and that RNNs are particularly robust to all nonidealities.Comment: 35 pages, 7 figures, 5 table

Military Retention Incentives: Evidence from the Air Force Selective Reenlistment Bonus

The limited lateral entry and rigid pay structure for U.S. military personnel present challenges in retaining skilled individuals who have attractive options in the civilian labor market. One tool the services use to address this challenge is the Selective Reenlistment Bonus (SRB), which offers eligible personnel with particular skills a substantial cash bonus upon reenlistment. However, the sequential nature of the bonus offer and reenlistment process limits the ability to adjust manpower quickly, raising interest in research that estimates the effect of the SRB on retention. While this literature has acknowledged challenges including potential endogeneity of bonus levels, attrition, and reenlistment eligibility, many studies do not address these concerns adequately. This paper uses a comprehensive panel data set on Air Force enlisted personnel to estimate the effect of the SRB on retention rates. We exploit variation in bonus levels within skill groups, control for civilian labor market conditions, and model reenlistment eligibility to avoid common assumptions that lead to biased impact estimates. We find substantial heterogeneity in the effect of the bonus, with the largest effects on first-term service members and those whose skills have not historically received a substantial bonus. We also find evidence that the bonus affects the timing of reenlistment decisions in addition to their frequency

Measurement of the B0_s semileptonic branching ratio to an orbitally excited D_s** state, Br(B0_s -> Ds1(2536) mu nu)

In a data sample of approximately 1.3 fb-1 collected with the D0 detector between 2002 and 2006, the orbitally excited charm state D_s1(2536) has been observed with a measured mass of 2535.7 +/- 0.6 (stat) +/- 0.5 (syst) MeV via the decay mode B0_s -> D_s1(2536) mu nu X. A first measurement is made of the branching ratio product Br(b(bar) -> D_s1(2536) mu nu X).Br(D_s1(2536)->D* K0_S). Assuming that D_s1(2536) production in semileptonic decay is entirely from B0_s, an extraction of the semileptonic branching ratio Br(B0_s -> D_s1(2536) mu nu X) is made.Comment: 7 pages, 2 figures, LaTeX, version with minor changes as accepted by Phys. Rev. Let

Simultaneous measurement of the ratio B(t->Wb)/B(t->Wq) and the top quark pair production cross section with the D0 detector at sqrt(s)=1.96 TeV

We present the first simultaneous measurement of the ratio of branching fractions, R=B(t->Wb)/B(t->Wq), with q being a d, s, or b quark, and the top quark pair production cross section sigma_ttbar in the lepton plus jets channel using 0.9 fb-1 of ppbar collision data at sqrt(s)=1.96 TeV collected with the D0 detector. We extract R and sigma_ttbar by analyzing samples of events with 0, 1 and >= 2 identified b jets. We measure R = 0.97 +0.09-0.08 (stat+syst) and sigma_ttbar = 8.18 +0.90-0.84 (stat+syst)} +/-0.50 (lumi) pb, in agreement with the standard model prediction.Comment: submitted to Phys.Rev.Letter

Search for W' bosons decaying to an electron and a neutrino with the D0 detector

This Letter describes the search for a new heavy charged gauge boson W' decaying into an electron and a neutrino. The data were collected with the D0 detector at the Fermilab Tevatron proton-antiproton Collider at a center-of-mass energy of 1.96 TeV, and correspond to an integrated luminosity of about 1 inverse femtobarn. Lacking any significant excess in the data in comparison with known processes, an upper limit is set on the production cross section times branching fraction, and a W' boson with mass below 1.00 TeV can be excluded at the 95% C.L., assuming standard-model-like couplings to fermions. This result significantly improves upon previous limits, and is the most stringent to date.Comment: submitted to Phys. Rev. Let

Search for charged Higgs bosons decaying to top and bottom quarks in ppbar collisions

We describe a search for production of a charged Higgs boson, q \bar{q'} -> H^+, reconstructed in the t\bar{b} final state in the mass range 180 <= M_{H^+} <= 300 GeV. The search was undertaken at the Fermilab Tevatron collider with a center-of-mass energy sqrt{s} = 1.96 TeV and uses 0.9 fb^{-1} of data collected with the D0 detector. We find no evidence for charged Higgs boson production and set upper limits on the production cross section in the Types I, II and III two-Higgs-doublet models (2HDMs). An excluded region in the (M_{H^+},tan\beta) plane for Type I 2HDM is presented.Comment: Submitted to Phys. Rev. Letter

Search for a scalar or vector particle decaying into Zgamma in ppbar collisions at sqrt(s) = 1.96 TeV

We present a search for a narrow scalar or vector resonance decaying into Zgamma with a subsequent Z decay into a pair of electrons or muons. The data for this search were collected with the D0 detector at the Fermilab Tevatron ppbar collider at a center of mass energy sqrt(s) = 1.96 TeV. Using 1.1 (1.0) fb-1 of data, we observe 49 (50) candidate events in the electron (muon) channel, in good agreement with the standard model prediction. From the combination of both channels, we derive 95% C.L. upper limits on the cross section times branching fraction (sigma x B) into Zgamma. These limits range from 0.19 (0.20) pb for a scalar (vector) resonance mass of 600 GeV/c^2 to 2.5 (3.1) pb for a mass of 140 GeV/c^2.Comment: Published by Phys. Lett.

Measurement of the lifetime of the B_c meson in the semileptonic decay channel

Using approximately 1.3 fb-1 of data collected by the D0 detector between 2002 and 2006, we measure the lifetime of the B_c meson in the B_c -> J/psi mu nu X final state. A simultaneous unbinned likelihood fit to the J/\psi+mu invariant mass and lifetime distributions yields a signal of 881 +/- 80 (stat) candidates and a lifetime measurement of \tau(B_c) = 0.448 +0.038 -0.036 (stat) +/- 0.032 (syst) ps.Comment: 7 pages, 2 figures, submitted to Phys. Rev. Let