Modeling, Optimization and Testing for Analog/Mixed-Signal Circuits in Deeply Scaled CMOS Technologies

As CMOS technologies move to sub-100nm regions, the design and verification for analog/mixed-signal circuits become more and more difficult due to the problems including the decrease of transconductance, severe gate leakage and profound mismatches. The increasing manufacturing-induced process variations and their impacts on circuit performances make the already complex circuit design even more sophisticated in the deeply scaled CMOS technologies. Given these barriers, efforts are needed to ensure the circuits are robust and optimized with consideration of parametric variations. This research presents innovative computer-aided design approaches to address three such problems: (1) large analog/mixed-signal performance modeling under process variations, (2) yield-aware optimization for complex analog/mixedsignal systems and (3) on-chip test scheme development to detect and compensate parametric failures. The first problem focus on the efficient circuit performance evaluation with consideration of process variations which serves as the baseline for robust analog circuit design. We propose statistical performance modeling methods for two popular types of complex analog/mixed-signal circuits including Sigma-Delta ADCs and charge-pump PLLs. A more general performance modeling is achieved by employing a geostatistics motivated performance model (Kriging model), which is accurate and efficient for capturing stand-alone analog circuit block performances. Based on the generated block-level performance models, we can solve the more challenging problem of yield-aware system optimization for large analog/mixed-signal systems. Multi-yield pareto fronts are utilized in the hierarchical optimization framework so that the statistical optimal solutions can be achieved efficiently for the systems. We further look into on-chip design-for-test (DFT) circuits in analog systems and solve the problems of linearity test in ADCs and DFT scheme optimization in charge-pump PLLs. Finally a design example of digital intensive PLL is presented to illustrate the practical applications of the modeling, optimization and testing approaches for large analog/mixed-signal systems

Statistical Performance Modeling of SRAMs

Yield analysis is a critical step in memory designs considering a variety of performance constraints. Traditional circuit level Monte-Carlo simulations for yield estimation of Static Random Access Memory (SRAM) cell is quite time consuming due to their characteristic of low failure rate, while statistical method of yield sensitivity analysis is meaningful for its high efficiency. This thesis proposes a novel statistical model to conduct yield sensitivity prediction on SRAM cells at the simulation level, which excels regular circuit simulations in a significant runtime speedup. Based on the theory of Kriging method that is widely used in geostatistics, we develop a series of statistical model building and updating strategies to obtain satisfactory accuracy and efficiency in SRAM yield sensitivity analysis. Generally, this model applies to the yield and sensitivity evaluation with varying design parameters, under the constraints of most SRAM performance metric. Moreover, it is potentially suitable for any designated distribution of the process variation regardless of the sampling method

Surrogate based Optimization and Verification of Analog and Mixed Signal Circuits

Nonlinear Analog and Mixed Signal (AMS) circuits are very complex and expensive to design and verify. Deeper technology scaling has made these designs susceptible to noise and process variations which presents a growing concern due to the degradation in the circuit performances and risks of design failures. In fact, due to process parameters, AMS circuits like phase locked loops may present chaotic behavior that can be confused with noisy behavior. To design and verify circuits, current industrial designs rely heavily on simulation based verification and knowledge based optimization techniques. However, such techniques lack mathematical rigor necessary to catch up with the growing design constraints besides being computationally intractable. Given all aforementioned barriers, new techniques are needed to ensure that circuits are robust and optimized despite process variations and possible chaotic behavior. In this thesis, we develop a methodology for optimization and verification of AMS circuits advancing three frontiers in the variability-aware design flow. The first frontier is a robust circuit sizing methodology wherein a multi-level circuit optimization approach is proposed. The optimization is conducted in two phases. First, a global sizing phase powered by a regional sensitivity analysis to quickly scout the feasible design space that reduces the optimization search. Second, nominal sizing step based on space mapping of two AMS circuits models at different levels of abstraction is developed for the sake of breaking the re-design loop without performance penalties. The second frontier concerns a dynamics verification scheme of the circuit behavior (i.e., study the chaotic vs. stochastic circuit behavior). It is based on a surrogate generation approach and a statistical proof by contradiction technique using Gaussian Kernel measure in the state space domain. The last frontier focus on quantitative verification approaches to predict parametric yield for both a single and multiple circuit performance constraints. The single performance approach is based on a combination of geometrical intertwined reachability analysis and a non-parametric statistical verification scheme. On the other hand, the multiple performances approach involves process parameter reduction, state space based pattern matching, and multiple hypothesis testing procedures. The performance of the proposed methodology is demonstrated on several benchmark analog and mixed signal circuits. The optimization approach greatly improves computational efficiency while locating a comparable/better design point than other approaches. Moreover, great improvements were achieved using our verification methods with many orders of speedup compared to existing techniques

Analog design for manufacturability: lithography-aware analog layout retargeting

As transistor sizes shrink over time in the advanced nanometer technologies, lithography effects have become a dominant contributor of integrated circuit (IC) yield degradation. Random manufacturing variations, such as photolithographic defect or spot defect, may cause fatal functional failures, while systematic process variations, such as dose fluctuation and defocus, can result in wafer pattern distortions and in turn ruin circuit performance. This dissertation is focused on yield optimization at the circuit design stage or so-called design for manufacturability (DFM) with respect to analog ICs, which has not yet been sufficiently addressed by traditional DFM solutions. On top of a graph-based analog layout retargeting framework, in this dissertation the photolithographic defects and lithography process variations are alleviated by geometrical layout manipulation operations including wire widening, wire shifting, process variation band (PV-band) shifting, and optical proximity correction (OPC). The ultimate objective of this research is to develop efficient algorithms and methodologies in order to achieve lithography-robust analog IC layout design without circuit performance degradation

Layout-accurate Ultra-fast System-level Design Exploration Through Verilog-ams

This research addresses problems in designing analog and mixed-signal (AMS) systems by bridging the gap between system-level and circuit-level simulation by making simulations fast like system-level and accurate like circuit-level. The tools proposed include metamodel integrated Verilog-AMS based design exploration flows. The research involves design centering, metamodel generation flows for creating efficient behavioral models, and Verilog-AMS integration techniques for model realization. The core of the proposed solution is transistor-level and layout-level metamodeling and their incorporation in Verilog-AMS. Metamodeling is used to construct efficient and layout-accurate surrogate models for AMS system building blocks. Verilog-AMS, an AMS hardware description language, is employed to build surrogate model implementations that can be simulated with industrial standard simulators. The case-study circuits and systems include an operational amplifier (OP-AMP), a voltage-controlled oscillator (VCO), a charge-pump phase-locked loop (PLL), and a continuous-time delta-sigma modulator (DSM). The minimum and maximum error rates of the proposed OP-AMP model are 0.11 % and 2.86 %, respectively. The error rates for the PLL lock time and power estimation are 0.7 % and 3.0 %, respectively. The OP-AMP optimization using the proposed approach is ~17000× faster than the transistor-level model based approach. The optimization achieves a ~4× power reduction for the OP-AMP design. The PLL parasitic-aware optimization achieves a 10× speedup and a 147 µW power reduction. Thus the experimental results validate the effectiveness of the proposed solution

Detection and Diagnosis of Out-of-Specification Failures in Mixed-Signal Circuits

Verifying whether a circuit meets its intended specifications, as well as diagnosing the circuits that do not, is indispensable at every stage of integrated circuit design. Otherwise, a significant portion of fabricated circuits could fail or behave correctly only under certain conditions. Shrinking process technologies and increased integration has further complicated this task. This is especially true of mixed-signal circuits, where a slight parametric shift in an analog component can change the output significantly. We are thus rapidly approaching a proverbial wall, where migrating existing circuits to advanced technology nodes and/or designing the next generation circuits may not be possible without suitable verification and debug strategies. Traditional approaches target accuracy and not scalability, limiting their use to high-dimensional systems. Relaxing the accuracy requirement mitigates the computational cost. Simultaneously, quantifying the level of inaccuracy retains the effectiveness of these metrics. We exercise this accuracy vs. turn-around-time trade-off to deal with multiple mixed-signal problems across both the pre- and post-silicon domains. We first obtain approximate failure probability estimates along with their confidence bands using limited simulation budgets. We then generate “failure regions” that naturally explain the parametric interactions resulting in predicted failures. These two pre-silicon contributions together enable us to estimate and reduce the failure probability, which we demonstrate on a high-dimensional phase-locked loop test-case. We leverage this pre-silicon knowledge towards test-set selection and post-silicon debug to alleviate the limited controllability and observability in the post-silicon domain. We select a set of test-points that maximizes the probability of observing failures. We then use post-silicon measurements at these test-points to identify systematic deviations from pre-silicon belief. This is demonstrated using the phase-locked loop test-case, where we boost the number of failures to observable levels and use the obtained measurements to root-cause underlying parametric shifts. The pre-silicon contributions can also be extended to perform equivalence checking and to help diagnose detected model-mismatches. The resultant calibrated model allows us to apply our work to the system level as well. The equivalence checking and model-mismatch diagnosis is successfully demonstrated using a high-level abstraction model for the phase-locked loop test-case

Measurements, Models, Systems and Design

531 s.

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