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due to charge injection. From the d.c. MOSFET threshold measurements, ESD-induced degradation can be evaluated. MOSFET gate dielectric leakage Evaluation of the d.c. MOSFET gate dielectric leakage is important to determine d.c. degradation issues with RF MOSFETs. MOSFET gate dielectric leakage can be observed as soft breakdown or hard breakdown. MOSFET soft breakdown can be induced by ESD electrical overstress. In MOSFET gate dielectric breakdown theory, evidence today demonstrates that MOSFET soft breakdown is an early stage of MOSFET hard breakdown . Hence, observed ESD-induced MOSFET gate leakage current may be regarded as a latent failure mechanism, which can lead to MOSFET device failure. Additionally, it has been found that the location of the MOSFET dielectric damage can in uence the d.c. characteristics of the MOSFET. MOSFET dielectric failure can occur MOSFET gate-to-drain, MOSFET gate-to-source, and MOSFET gate-to-channel; the location has an in uence on the d.c. characteristics of the MOSFET device.



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Most students of personality, however, would have hesitations with this subordination of personality to social psychology, especially if that bondage is a side effect of a dominant operational approach The scienti c emancipation of a subjective or intersubjective concept appears to hinge upon the discovery of objective indicators that cover the concept well If we wish to establish how much of a fever we run, we do not use a Likert scale but measure it with a thermometer If we want to gauge an applicant s intelligence, we apply a test rather than asking questions to the applicant or even to a number of third persons If the latter example is more problematic than the rst, that is because there may be doubt regarding the coverage of the concept of intelligence by an IQ score.





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degradation magnitude in the d.c. forward-bias I V characteristic. From an earlier study of the ESD-induced forward-bias degradation, the magnitude of the HBM pulse to cause ESD failure was known. A single HBM ESD pulse of a magnitude that would induce ESD failure was applied between the SiGeC HBT base and emitter pads. Post-ESD stress, the test wafer was re-evaluated for S-parameter extraction. In the Figures 6.30 6.33, the four two-port S-parameters (e.g., S11, S12, S21, and S22) are plotted as a function of frequency before and after HBM ESD stress, respectively. In the Figure 6.30, the S-parameter S11 results are shown pre- and post-HBM ESD stress; it can be observed that the S11 parameter magnitude is lower for all frequencies post-ESD stress. After ESD stress, the largest degradation is observed. In the Figure 6.31, the S-parameter S12 results are shown pre- and post-HBM ESD stress. In this parameter, post-ESD stress measurements were higher for lower frequencies but lower for the higher frequency regime.

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scores with the extension variables There are several methods for computing factor scores, but the choice is usually between only two variations The rst is multiple regression analysis The variables loaded by a factor are the predictors and the factor is the dependent variable The regression analysis provides the beta weights, which are then used to calculate the factor scores However, regression weights have the bouncing beta problem: Unless the sample is over 400, they bounce around when a new sample is collected or when the variable mix is changed slightly The instability of beta weights has led to the other recommended procedure for computing factor scores: unit weighting Unit weighing is de ned as adding together the scores of the variables that have high weights in the multiple regression from the variables to the factors, after the variables have been converted to the same metric, eg.

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, Z scores Each of the variables clearly related to the factor is weighted 1 if the weight is positive or 1 if the weight is negative With samples less than 400, unit weights have higher factor validities when cross-validated than do multiple regression weights (due to the latter s capitalization on chance) Factor scores have problems In addition to indeterminacy of CFA scores, each variable weighted in the scoring equation has its unique part added to the score as well as the part loaded by the factor This is the same problem that occurs when the items are correlated with the total score from the items The item-total correlations are in ated because that part of the item not measuring the construct is included both in the total score and in the item To avoid correlations in ated by correlated error, item-remainder correlations have been suggested.

In the same vein, one may have doubts about the thermometer scale as a measure of outdoor temperature and prefer a formula that includes sunshine, humidity, and wind force But once a certain level of coverage.

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