System Identification, Diagnosis, and Built-In Self-Test of High Switching Frequency DC-DC Converters

Complex electronic systems include multiple power domains and drastically varying dynamic power consumption patterns, requiring the use of multiple power conversion and regulation units. High frequency switching converters have been gaining prominence in the DC-DC converter market due to smaller solution size (higher power density) and higher efficiency. As the filter components become smaller in value and size, they are unfortunately also subject to higher process variations and worse degradation profiles jeopardizing stable operation of the power supply. This dissertation presents techniques to track changes in the dynamic loop characteristics of the DC-DC converters without disturbing the normal mode of operation. A digital pseudo-noise (PN) based stimulus is used to excite the DC-DC system at various circuit nodes to calculate the corresponding closed-loop impulse response. The test signal energy is spread over a wide bandwidth and the signal analysis is achieved by correlating the PN input sequence with the disturbed output generated, thereby accumulating the desired behavior over time. A mixed-signal cross-correlation circuit is used to derive on-chip impulse responses, with smaller memory and lower computational requirement in comparison to a digital correlator approach. Model reference based parametric and non-parametric techniques are discussed to analyze the impulse response results in both time and frequency domain. The proposed techniques can extract open-loop phase margin and closed-loop unity-gain frequency within 5.2% and 4.1% error, respectively, for the load current range of 30-200mA. Converter parameters such as natural frequency (ωn), quality factor (Q), and center frequency (ωc) can be estimated within 3.6%, 4.7%, and 3.8% error respectively, over load inductance of 4.7-10.3μH, and filter capacitance of 200400nF. A 5-MHz switching frequency, 5-8.125V input voltage range, voltage-mode con-

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