Built-in Self-Test and Calibration of Mixed-signal Devices
Type of Degreedissertation
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Wide adoption of deep sub-micron and nanoscale technologies in the modern semiconductor industry is resulted in very large complex mixed-signal devices. It has then become more difficult to estimate and control device parameters, which are now increasingly vulnerable to fabrication process variations. Conventional design-for-test (DFT) methods have been already well studied for digital circuitry to ensure verification of its functionality and fault coverage. Built-in self-test (BIST) approaches have been developed for design automation of digital ICs. However, such DFT techniques cannot be applied to analog and mixed-signal circuits directly. Therefore, new techniques must be employed to detect faults in analog components and to provide certain level of calibration capability to dynamically adjust the parameters of an analog device for better yield of chips. The most important analog devices in a mixed-signal system-on-chip (SoC) are analog-to-digital converter (ADC) and digital-to-analog converter (DAC). Such converters transfer data between digital and analog circuits and convert analog signals to digital bits or vice versa. In this research, novel digital signal processor (DSP)-based post-fabrication process-independent BIST approaches and variation tolerant design technique for ADC and DAC are studied. We use a sigma-delta modulation technique for measurement and a polynomial fitting algorithm for device calibration. In the proposed technique, a digital signal processor is programmed and used as test pattern generator (TPG), output response analyzer (ORA) and test control unit. The polynomial fitting algorithm characterizes the nonlinearity errors and the polynomial is used to generate compensating signals to reduce nonlinearity errors to 0.5LSB. This technique can be applied to other digitally-controllable mixed-signal devices and a general test-characterization-calibration approach modeled after this work can be developed to detect, measure, and compensate nonlinearity errors caused by device parameter deviations.