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

Algorithms for Verification of Analog and Mixed-Signal Integrated Circuits

Over the past few decades, the tremendous growth in the complexity of analog and mixed-signal (AMS) systems has posed great challenges to AMS verification, resulting in a rapidly growing verification gap. Existing formal methods provide appealing completeness and reliability, yet they suffer from their limited efficiency and scalability. Data oriented machine learning based methods offer efficient and scalable solutions but do not guarantee completeness or full coverage. Additionally, the trend towards shorter time to market for AMS chips urges the development of efficient verification algorithms to accelerate with the joint design and testing phases. This dissertation envisions a hierarchical and hybrid AMS verification framework by consolidating assorted algorithms to embrace efficiency, scalability and completeness in a statistical sense. Leveraging diverse advantages from various verification techniques, this dissertation develops algorithms in different categories. In the context of formal methods, this dissertation proposes a generic and comprehensive model abstraction paradigm to model AMS content with a unifying analog representation. Moreover, an algorithm is proposed to parallelize reachability analysis by decomposing AMS systems into subsystems with lower complexity, and dividing the circuit's reachable state space exploration, which is formulated as a satisfiability problem, into subproblems with a reduced number of constraints. The proposed modeling method and the hierarchical parallelization enhance the efficiency and scalability of reachability analysis for AMS verification. On the subject of learning based method, the dissertation proposes to convert the verification problem into a binary classification problem solved using support vector machine (SVM) based learning algorithms. To reduce the need of simulations for training sample collection, an active learning strategy based on probabilistic version space reduction is proposed to perform adaptive sampling. An expansion of the active learning strategy for the purpose of conservative prediction is leveraged to minimize the occurrence of false negatives. Moreover, another learning based method is proposed to characterize AMS systems with a sparse Bayesian learning regression model. An implicit feature weighting mechanism based on the kernel method is embedded in the Bayesian learning model for concurrent quantification of influence of circuit parameters on the targeted specification, which can be efficiently solved in an iterative method similar to the expectation maximization (EM) algorithm. Besides, the achieved sparse parameter weighting offers favorable assistance to design analysis and test optimization

Master of Science

thesisThis document describes an improved method of formal verification of complex analog/mixed-signal (AMS) circuits. Currently, in our LEMA tool, verification properties are encoded using labeled Petri net (LPN). These LPNs are generated manually, a tedious process that requires the user to have considerable familiarity with the tool. To eliminate this time-consuming process, our LEMA tool is extended to include a translator that converts properties written in a property specification language to LPNs. New methods are also implemented to separate the transient period from the stable output period, thus improving the generated model. Also, the current methodology generates the circuit models for the input values used during the simulation of the circuit. So, models generated for other control input values are not accurate. In this case, accuracy of the generated models is improved by using a linear abstraction method like interpolation

From FPGA to ASIC: A RISC-V processor experience

This work document a correct design flow using these tools in the Lagarto RISC- V Processor and the RTL design considerations that must be taken into account, to move from a design for FPGA to design for ASIC

Efficient verification/testing of system-on-chip through fault grading and analog behavioral modeling

textThis dissertation presents several cost-effective production test solutions using fault grading and mixed-signal design verification cases enabled by analog behavioral modeling. Although the latest System-on-Chip (SOC) is getting denser, faster, and more complex, the manufacturing technology is dominated by subtle defects that are introduced by small-scale technology. Thus, SOC requires more mature testing strategies. By performing various types of testing, better quality SoC can be manufactured, but test resources are too limited to accommodate all those tests. To create the most efficient production test flow, any redundant or ineffective tests need to be removed or minimized. Chapter 3 proposes new method of test data volume reduction by combining the nonlinear property of feedback shift register (FSR) and dictionary coding. Instead of using the nonlinear FSR for actual hardware implementation, the expanded test set by nonlinear expansion is used as the one-column test sets and provides big reduction ratio for the test data volume. The experimental results show the combined method reduced the total test data volume and increased the fault coverage. Due to the increased number of test patterns, total test time is increased. Chapter 4 addresses a whole process of functional fault grading. Fault grading has always been a ”desire-to-have” flow because it can bring up significant value for cost saving and yield analysis. However, it is very hard to perform the fault grading on the complex large scale SOC. A commercial tool called Z01X is used as a fault grading platform, and whole fault grading process is coordinated and each detailed execution is performed. Simulation- based functional fault grading identifies the quality of the given functional tests against the static faults and transition delay faults. With the structural tests and functional tests, functional fault grading can indicate the way to achieve the same test coverage by spending minimal test time. Compared to the consumed time and resource for fault grading, the contribution to the test time saving might not be acceptable as very promising, but the fault grading data can be reused for yield analysis and test flow optimization. For the final production testing, confident decisions on the functional test selection can be made based on the fault grading results. Chapter 5 addresses the challenges of Package-on-Package (POP) testing. Because POP devices have pins on both the top and the bottom of the package, the increased test pins require more test channels to detect packaging defects. Boundary scan chain testing is used to detect those continuity defects by relying on leakage current from the power supply. This proposed test scheme does not require direct test channels on the top pins. Based on the counting algorithm, minimal numbers of test cycles are generated, and the test achieved full test coverage for any combinations of pin-to-pin shortage defects on the top pins of the POP package. The experimental results show about 10 times increased leakage current from the shorted defect. Also, it can be expanded to multi-site testing with less test channels for high-volume production. Fault grading is applied within different structural test categories in Chapter 6. Stuck-at faults can be considered as TDFs having infinite delay. Hence, the TDF Automatic Test Pattern Generation (ATPG) tests can detect both TDFs and stuck-at faults. By removing the stuck-at faults being detected by the given TDF ATPG tests, the tests that target stuck-at faults can be reduced, and the reduced stuck-at fault set results in fewer stuck-at ATPG patterns. The structural test time is reduced while keeping the same test coverage. This TDF grading is performed with the same ATPG tool used to generate the stuck-at and TDF ATPG tests. To expedite the mixed-signal design verification of complex SoC, analog behavioral modeling methods and strategies are addressed in Chapter 7 and case studies for detailed verification with actual mixed-signal design are ad- dressed in Chapter 8. Analog modeling effort can enhance verification quality for a mixed-signal design with less turnaround time, and it enables compatible integration of the mixed-signal design cores into the SoC. The modeling process may reveal any potential design errors or incorrect testbench setup, and it results in minimizing unnecessary debugging time for quality devices. Two mixed-signal design cases were verified by me using the analog models. A fully hierarchical digital-to-analog converter (DAC) model is implemented and silicon mismatches caused by process variation are modeled and inserted into the DAC model, and the calibration algorithm for the DAC is successfully verified by model-based simulation at the full DAC-level. When the mismatch amount is increased and exceeded the calibration capability of the DAC, the simulation results show the increased calibration error with some outliers. This verification method can identify the saturation range of the DAC and predict the yield of the devices from process variation. A phase-locked loop (PLL) design cases were also verified by me using the analog model. Both open-loop PLL model and closed-loop PLL model cases are presented. Quick bring-up of open-loop PLL model provides low simulation overhead for widely-used PLLs in the SOC and enables early starting of design verification for the upper-level design using the PLL generated clocks. Accurate closed-loop PLL model is implemented for DCO-based PLL design, and the mixed-simulation with analog models and schematic designs enables flexible analog verification. Only focused analog design block is set to the schematic design and the rest of the analog design is replaced by the analog model. Then, this scaled-down SPICE simulation is performed about 10 times to 100 times faster than full-scale SPICE simulation. The analog model of the focused block is compared with the scaled-down SPICE simulation result and the quality of the model is iteratively enhanced. Hence, the analog model enables both compatible integration and flexible analog design verification. This dissertation contributes to reduce test time and to enhance test quality, and helps to set up efficient production testing flows. Depending on the size and performance of CUT, proper testing schemes can maximize the efficiency of production testing. The topics covered in this dissertation can be used in optimizing the test flow and selecting the final production tests to achieve maximum test capability. In addition, the strategies and benefits of analog behavioral modeling techniques that I implemented are presented, and actual verification cases shows the effectiveness of analog modeling for better quality SoC products.Electrical and Computer Engineerin

Mixed signal design flow, a mixed signal PLL case study

Mixed-signal designs are becoming more and more complex every day. In order to adapt to the new market requirements, a formal process for design and verification of mixed signal systems i. e. top-down design and bottom-up verification methodology is required. This methodology has already been established for digital design. The goal of this research is to propose a new design methodology for mixed signal systems. In the first two chapters of this thesis, the need for a mixed signal design flow based on top-down design methodology will be discussed. The proposed design flow is based on behavioral modeling of the mixed signal system using one of the mixed signal behavioral modeling languages. These models can be used for design and verification through different steps of the design from system level modeling to final physical design. The other advantage of the proposed flow is analog and digital co-design. In the remaining chapters of this thesis, the proposed design flow was verified by designing an 800 MHz mixed signal PLL. The PLL uses a charge pump phase frequency detector, a single capacitor loop filter, and a feed forward error correction architecture using an active damping control circuit instead of passive resistor in loop filter. The design was done in 0. 18- µ m CMOS process technology

Learning Approaches to Analog and Mixed Signal Verification and Analysis

The increased integration and interaction of analog and digital components within a system has amplified the need for a fast, automated, combined analog, and digital verification methodology. There are many automated characterization, test, and verification methods used in practice for digital circuits, but analog and mixed signal circuits suffer from long simulation times brought on by transistor-level analysis. Due to the substantial amount of simulations required to properly characterize and verify an analog circuit, many undetected issues manifest themselves in the manufactured chips. Creating behavioral models, a circuit abstraction of analog components assists in reducing simulation time which allows for faster exploration of the design space. Traditionally, creating behavioral models for non-linear circuits is a manual process which relies heavily on design knowledge for proper parameter extraction and circuit abstraction. Manual modeling requires a high level of circuit knowledge and often fails to capture critical effects stemming from block interactions and second order device effects. For this reason, it is of interest to extract the models directly from the SPICE level descriptions so that these effects and interactions can be properly captured. As the devices are scaled, process variations have a more profound effect on the circuit behaviors and performances. Creating behavior models from the SPICE level descriptions, which include input parameters and a large process variation space, is a non-trivial task. In this dissertation, we focus on addressing various problems related to the design automation of analog and mixed signal circuits. Analog circuits are typically highly specialized and fined tuned to fit the desired specifications for any given system reducing the reusability of circuits from design to design. This hinders the advancement of automating various aspects of analog design, test, and layout. At the core of many automation techniques, simulations, or data collection are required. Unfortunately, for some complex analog circuits, a single simulation may take many days. This prohibits performing any type of behavior characterization or verification of the circuit. This leads us to the first fundamental problem with the automation of analog devices. How can we reduce the simulation cost while maintaining the robustness of transistor level simulations? As analog circuits can vary vastly from one design to the next and are hardly ever comprised of standard library based building blocks, the second fundamental question is how to create automated processes that are general enough to be applied to all or most circuit types? Finally, what circuit characteristics can we utilize to enhance the automation procedures? The objective of this dissertation is to explore these questions and provide suitable evidence that they can be answered. We begin by exploring machine learning techniques to model the design space using minimal simulation effort. Circuit partitioning is employed to reduce the complexity of the machine learning algorithms. Using the same partitioning algorithm we further explore the behavior characterization of analog circuits undergoing process variation. The circuit partitioning is general enough to be used by any CMOS based analog circuit. The ideas and learning gained from behavioral modeling during behavior characterization are used to improve the simulation through event propagation, input space search, complexity and information measurements. The reduction of the input space and behavioral modeling of low complexity, low information primitive elements reduces the simulation time of large analog and mixed signal circuits by 50-75%. The method is extended and applied to assist in analyzing analog circuit layout. All of the proposed methods are implemented on analog circuits ranging from small benchmark circuits to large, highly complex and specialized circuits. The proposed dependency based partitioning of large analog circuits in the time domain allows for fast identification of highly sensitive transistors as well as provides a natural division of circuit components. Modeling analog circuits in the time domain with this partitioning technique and SVM learning algorithms allows for very fast transient behavior predictions, three orders of magnitude faster than traditional simulators, while maintaining 95% accuracy. Analog verification can be explored through a reduction of simulation time by utilizing the partitions, information and complexity measures, and input space reduction. Behavioral models are created using supervised learning techniques for detected primitive elements. We will show the effectiveness of the method on four analog circuits where the simulation time is decreased by 55-75%. Utilizing the reduced simulation method, critical nodes can be found quickly and efficiently. The nodes found using this method match those found by an experienced layout engineer, but are detected automatically given the design and input specifications. The technique is further extended to find the tolerance of transistors to both process variation and power supply fluctuation. This information allows for corrections in layout overdesign or guidance in placing noise reducing components such as guard rings or decoupling capacitors. The proposed approaches significantly reduce the simulation time required to perform the tasks traditionally, maintain high accuracy, and can be automated