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RISS 인기검색어
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- 저자 : ( Injun Yeo ) (검색결과 4 건)
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A 12-bit 40 MS/s SAR ADC with Digital Foreground Self-calibration for Capacitor Mismatches
Injune Yeo,Byung-geun Lee 대한전자공학회 2020 Journal of semiconductor technology and science Vol.20 No.1
This study presents a 12-bit 40 MS/s successive approximation register (SAR) analog-to-digital converter (ADC) that utilizes a digital foreground self-calibration scheme for capacitor mismatches and a low-noise dynamic comparator. To reduce hardware complexity and AD power consumption, a split-capacitor, capacitive digital-to-analog converter (CDAC) is used to generate the comparator input voltage for binary search operations at the cost of increased capacitor matching requirements. Capacitor mismatches were calibrated with the digital foreground self-calibration technique, which was modified from our previous work to be adapted for the split-capacitor CDAC. In addition, a low-noise dynamic comparator that does not need an additional circuit and conversion cycles is also presented. A prototype ADC, which occupies an active die area of 0.098 mm2, is fabricated with a 65 nm standard CMOS process. By using the mismatch calibration scheme, the ADC achieves a spurious-free dynamic range and a signal-to-noise and distortion ratios of 79.0 dB and 67.4 dB, respectively, for a sampling rate of 40-MSample/s. The power consumption of the ADC is 1.96 mW when driven with a 1.2 V supply and the figure-of-merit is 25.58 fJ/conversion-step.
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Modeling and System-Level Simulation for Nonideal Conductance Response of Synaptic Devices
Gi, Sang-Gyun,Yeo, Injune,Chu, Myonglae,Moon, Kibong,Hwang, Hyunsang,Lee, Byung-geun Institute of Electrical and Electronics Engineers 2018 IEEE transactions on electron devices Vol.65 No.9
<P>This paper presents a new method for modeling the nonideal conductance response (CR) of synaptic devices. Unlike previous studies, which utilize physical device properties for modeling, this paper only uses the measured CR data. This allows the proposed modeling method to be easily applied to various types of synaptic devices without considering the unique physical properties of each device. An efficient piecewise linear approximation method which offers a tradeoff between computational complexity and simulation accuracy of neural networks is also presented to generate a linear device model out of nonlinear CR data. In addition, model parameters, which reflect the nonideal characteristics of the CR such as abrupt and asymmetric conductance changes, conductance variation, and limited conductance dynamic range, are introduced to evaluate the network performances in the presence of the nonidealities. By adjusting the model parameters, the desired CR satisfying the network performance requirements can be derived for device development. A three-layer neural network employing the device model has been designed and trained for the MNIST data set in order to demonstrate the application of the model to system-level simulations and verify the effectiveness of the modeling method.</P>
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( Hyunsoo Kim ),( Su Jong Yu ),( Injun Yeo ),( Jeong-ju Yoo ),( Dong Hyeon Lee ),( Yuri Cho ),( Eun Ju Cho ),( Jeong-hoon Lee ),( Yoon Jun Kim ),( Sungyoung Lee ),( Jongsoo Jun ),( Taesung Park ),( Ju 대한간학회 2016 춘·추계 학술대회 (KASL) Vol.2016 No.1
Aims: Sorafenib is the only standard treatment for advanced hepatocellular carcinoma (HCC), but it provides modest survival benefits over placebo, necessitating predictive biomarkers of the response to sorafenib. Methods: Serum samples were obtained before and after sorafenib treatment from 115 consecutive patients [training set (n = 65) and validation set (n = 50)] with HCC and analyzed by multiple reaction monitoring-mass spectrometry (MRM-MS) to quantify candidate biomarkers. Results: We verified a triple-marker panel to be predictive of the response to sorafenib by MRM-MS in HCC patients. This panel was a significant predictor (AUROC > 0.950) of the response to sorafenib treatment, having the best cutoff value by multivariate analysis. In the training set, patients who exceeded this threshold (0.4) had significantly better overall survival (median, 21.4 months) than those with lower values (median, 8.6 months; P = 0.001). Further, a value that was lower than this cutoff was an independent predictor of poor overall survival [hazard ratio (HR), 2.728; 95% confidence interval (CI), 1.312-5.672; P = 0.007] and remained an independent predictive factor of rapid progression (HR, 2.631; 95% CI, 1.448-4.780; P = 0.002). Consequently, when applied to the independent validation set, levels of the cut-off value for triple-marker panel maintained their prognostic value for poor clinical outcomes. Conclusions: A discriminatory signature that comprises a triple-marker panel independently correlates with poorer survival and more rapid progression in HCC patients who are treated with sorafenib. These findings provide new insights into targeted proteomics-based biomarkers, which might engender individualized sorafenib therapy.
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Kim, Hyunsoo,Yu, Su Jong,Yeo, Injun,Cho, Young Youn,Lee, Dong Hyeon,Cho, Yuri,Cho, Eun Ju,Lee, Jeong-Hoon,Kim, Yoon Jun,Lee, Sungyoung,Jun, Jongsoo,Park, Taesung,Yoon, Jung-Hwan,Kim, Youngsoo American Society for Biochemistry and Molecular Bi 2017 Molecular and Cellular Proteomics Vol.16 No.7
<P>Sorafenib is the only standard treatment for unresectable hepatocellular carcinoma (HCC), but it provides modest survival benefits over placebo, necessitating predictive biomarkers of the response to sorafenib. Serum samples were obtained from 115 consecutive patients with HCC before sorafenib treatment and analyzed by multiple reaction monitoring-mass spectrometry (MRM-MS) and ELISA to quantify candidate biomarkers. We verified a triple-marker panel to be predictive of the response to sorafenib by MRM-MS, comprising CD5 antigen-like (CD5L), immunoglobulin J (IGJ), and galectin-3-binding protein (LGALS3BP), in HCC patients. This panel was a significant predictor (AUROC > 0.950) of the response to sorafenib treatment, having the best cut-off value (0.4) by multivariate analysis. In the training set, patients who exceeded this cut-off value had significantly better overall survival (median, 21.4 months) than those with lower values (median, 8.6 months; p = 0.001). Further, a value that was lower than this cutoff was an independent predictor of poor overall survival [hazard ratio (HR), 2.728; 95% confidence interval (CI), 1.312-5.672; p = 0.007] and remained an independent predictive factor of rapid progression (HR, 2.631; 95% CI, 1.448-4.780; p = 0.002). When applied to the independent validation set, levels of the cut-off value for triple-marker panel maintained their prognostic value for poor clinical outcomes. On the contrast, the triple-marker panel was not a prognostic factor for patients who were treated with transarterial chemoembolization (TACE). The discriminatory signature of a triple-marker panel provides new insights into targeted proteomic biomarkers for individualized sorafenib therapy.</P>
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