A Disturbance-Free Built-In Self-Test and Diagnosis Technique for DC-DC Converters

Abstract

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 their high efficiency and smaller form factor. Unfortunately, they are also subject to higher process variations, and faster in-field degradation, jeopardizing stable operation of the power supply. This article presents a technique to track changes in the dynamic loop characteristics of DC-DC converters without disturbing the normal mode of operation using a white noise–based excitation and correlation. Using multiple points for injection and analysis, we show that the degraded part can be diagnosed to take remedial action. White noise excitation is generated via a pseudo-random disturbance at reference, load current, and pulse-width modulation (PWM) nodes of the converter with the test signal energy being spread over a wide bandwidth, without significantly affecting the converter noise and ripple floor. The impulse response is extracted by correlating the random input sequence with the disturbed output generated. Test signal analysis is achieved by correlating the pseudo-random input sequence with the output response and thereby accumulating the desired behavior over time and pulling it above the noise floor of the measurement set-up. An off-the-shelf power converter, LM27402, is used as the device-under-test (DUT) for experimental verification. Experimental results show that the proposed technique can estimate converter natural frequency and quality factor ( Q -factor) within ±2.5% and ±0.7% error margin respectively, over changes in load inductance and capacitance. For the diagnosis purpose, a measure of inductor's DC resistance (DCR) value, which is the inductor's series resistance and indicative of the degradation in inductor's Q -factor, is estimated within less than ±1.6% error margin.