Harsh Bais, PhD



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  • Michael Lekas, Sunwoo Lee, Wujoon Cha, James Hone, Member, IEEE, and Kenneth Shepard, Fellow, IEEE Noise Modeling of Graphene Resonant Channel Transistors, IEEE Transactions on Electron Devices (advanced on-line)

    In this paper, we present a compact model for graphene resonant channel transistors (G-RCTs) that uses extracted electrical and mechanical parameters to provide an accurate simulation of dc, RF, noise, and frequency-tuning characteristics of the device. The model is validated with measurements on fabricated G-RCTs, which include what we believe to be the first noise measurements conducted on any resonant transistor. The noise model, which considers both electrical and mechanical sources, is used to demonstrate the fundamental differences in the noise behavior of active and passive resonator technologies, and to show how optimization of device parameters can be used to improve the noise performance of RCTs.

  • Michael Lekas, Sunwoo Lee, Wujoon Cha, James Hone, and Kenneth Shepard Third-order intermodulation distortion in graphene resonant channel transistors, Applied Physics Letters 106, 073504 (2015); doi: 10.1063/1.4913462

    Third-order intermodulation distortion (IM3) is an important metric for electromechanical resonators used in radio frequency signal processing applications since it characterizes the nonlinearity of the device, and the amount of in-band interference it generates when subject to unwanted, out-of-band signals. In this letter, we measure and model IM3 in a strain-engineered graphene mechanical resonator operated as a graphene resonant channel transistor (G-RCT). The device analyzed in this work has a voltage third-order intercept point (VIIP3) of 69.5 dBm V at a gate-to-source DC bias (Vgs) of 2.5 V, which drops to 52.1 dBm V at Vgs=4.5V when driven with two out-of-band input tones spaced 5 and 10MHz from the resonant frequency. The decrease in the VIIP3 with Vgs coincides with an increase in the transmission response (S21) of the device, illustrating a trade-off between transduction efficiency and linearity. In addition, we find that conventional micro-electro-mechanical systems theory for IM3 calculation does not accurately describe our measurement data. To resolve this discrepancy, we develop a model for IM3 in G-RCTs that takes into account all of the output current terms present in the embedded transistor structure, as well as an effective Duffing parameter (αeff).

  • Matthew L. Johnston, Erik F. Young, Kenneth L. Shepard Whole-blood immunoassay for γH2AX as a radiation biodosimetry assay with minimal sample preparation, J Radiation and Environmental Biophysics (2015); doi: 10.1007/s00411-015-0595-4

    The current state of the art in high-throughput minimally invasive radiation biodosimetry involves the collection of samples in the field and analysis at a centralized facility. We have developed a simple biological immunoassay for radiation exposure that could extend this analysis out of the laboratory into the field. Such a forward placed assay would facilitate triage of a potentially exposed population. The phosphorylation and localization of the histone H2AX at double-stranded DNA breaks has already been proven to be an adequate surrogate assay for reporting DNA damage proportional to radiation dose. Here, we develop an assay for phosphorylated H2AX directed against minimally processed sample lysates. We conduct preliminary verification of H2AX phosphorylation using irradiated mouse embryo fibroblast cultures. Additional dosimetry is performed using human blood samples irradiated ex vivo. The assay reports H2AX phosphorylation in human blood samples in response to ionizing radiation over a range of 0–5 Gy in a linear fashion, without requiring filtering, enrichment, or purification of the blood sample.

  • Kevin J. Emmett, Jacob K. Rosenstein, Jan-Willem van de Meent, Ken L. Shepard, Chris H. Wiggins Statistical Inference for Nanopore Sequencing with a Biased Random Walk Model , Biophysical Journal Volume 108, Issue 8, p1852–1855, 21 April 2015

    Nanopore sequencing promises long read-lengths and single-molecule resolution, but the stochastic motion of the DNA molecule inside the pore is, as of this writing, a barrier to high accuracy reads. We develop a method of statistical inference that explicitly accounts for this error, and demonstrate that high accuracy (>99%) sequence inference is feasible even under highly diffusive motion by using a hidden Markov model to jointly analyze multiple stochastic reads. Using this model, we place bounds on achievable inference accuracy under a range of experimental parameters.