Design and automation techniques for hIgh-performance mixed-signal circuits

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In the era of ubiquitous sensing environment, the modern electronic system expands our perception of the outside world. Analog/mixed-signal circuit has played a critical role to bridge the physical and digital worlds. The boom of Internet-of-Things (IoT), bio-sensing, and digital camera calls for versatile high-performance mixed-signal circuits and the corresponding automated design methodology. However, high-performance analog circuits are area or power hungry. Moreover, the design cost is prohibitively expensive. To address these challenges, this dissertation explores solutions from both the design and automation techniques. Analog-to-digital converter (ADC) is an important subset of analog/mixed-signal circuits. Continuous time Delta-Sigma modulator (CTDSM) is a popular design choice for high-speed and high-resolution designs. CTDSMs feature a higher power efficiency than their discrete-time (DT) counterpart. The first work presents a high-speed 4th-order DSM featuring the CT-DT hybridization and an efficient excess-loop-delay (ELD) compensation technique in the charge domain. Compared to prior high-order CTDSMs, the proposed hybrid DSM achieves 4th-order noise shaping with single operational trans-conductance amplifier (OTA). Minimized number of OTAs reduces power and enhances stability. On top of that, an efficient ELD compensation technique is implemented by utilizing the inherent capacitor digital-to-analog converter (CDAC) of SAR. Fabricated in 40 nm CMOS, the prototype ADC achieved a peak Schreier Figure-of-Merits (FoM) of 176.1 dB, marking 4 dB improvement over prior arts. The second project explores the techniques to reduce the area consumption of high-resolution CTDSMs. The performance of existing high-resolution CTDSMs is limited by the feedback DAC. The stringent non-linearity requirement leads to the large area of DAC. To address this limitation, a low-complexity hardware-based 2nd-order dynamic-element-matching (DEM) is proposed. The partial sorter applied to the DEM minimizes the hardware cost. Moreover, feedforward path assisted loop filter adapts the highly-linear integrator design to the low power supply voltage. With these techniques combined, the prototype shows a feasible design pattern to achieve compact-area, high-resolution design at advanced technology nodes. A prototype fabricated in 40 nm CMOS measured 95dB SNDR, occupying only 0.37 mm² area. After the exploration of pushing the ADC performance boundary, this dissertation also demonstrates the automated design methodology. The design cost of high-performance mixed-signal circuit grows exponentially with the technology scaling. Existing analog automation techniques cannot handle practical circuit design constraints (e.g. robustness against variations). The third work presents RobustAnalog, a variation-aware analog circuit optimization via multi-task reinforcement learning (RL) and task-space pruning. RobustAnalog is mainly designed to tackle the process-voltage-temperature (PVT) robustness in the analog design. Correlations between similar variations are modeled and conflicts between distinct variations are mitigated. With task pruning, a small-sized proxy training task set is formed. The pruning reduces the queries to the full task set. Compared with the popular blackbox optimization methods, RobustAnalog significantly reduces the simulation cost. Therefore, RobustAnalog shows the staggering progress towards analog automation techniques that can be applied to real silicon conditions.


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