2 research outputs found

    Hot carrier aging and its variation under use-bias: kinetics, prediction, impact on Vdd and SRAM

    Get PDF
    As CMOS scales down, hot carrier aging (HCA) scales up and can be a limiting aging process again. This has motivated re-visiting HCA, but recent works have focused on accelerated HCA by raising stress biases and there is little information on HCA under use-biases. Early works proposed that HCA mechanism under high and low biases are different, questioning if the high-bias data can be used for predicting HCA under use-bias. A key advance of this work is proposing a new methodology for evaluating the HCA-induced variation under use-bias. For the first time, the capability of predicting HCA under use-bias is experimentally verified. The importance of separating RTN from HCA is demonstrated. We point out the HCA measured by the commercial Source-Measure-Unit (SMU) gives erroneous power exponent. The proposed methodology minimizes the number of tests and the model requires only 3 fitting parameters, making it readily implementable

    Hot Carrier Aging and its Variation Under Use-Bias: Kinetics, Prediction, Impact on Vdd and SRAM

    No full text
    As CMOS scales down, hot carrier aging (HCA) scales up and can be a limiting aging process again. This has motivated re-visiting HCA, but recent works have focused on accelerated HCA by raising stress biases and there is little information on HCA under use-biases. Early works proposed that HCA mechanism under high and low biases are different, questioning if the high-bias data can be used for predicting HCA under use-bias. A key advance of this work is proposing a new methodology for evaluating the HCA-induced variation under use-bias. For the first time, the capability of predicting HCA under use-bias is experimentally verified. The importance of separating RTN from HCA is demonstrated. We point out the HCA measured by the commercial Source-Measure-Unit (SMU) gives erroneous power exponent. The proposed methodology minimizes the number of tests and the model requires only 3 fitting parameters, making it readily implementable
    corecore