MixedMode Simulation of PhaseLocked Loops

Preface. Acknowledgments. 1 Introduction. 2 Design Flow Overview. 2.1 Design Levels. 2.2 Top-down System Design. 2.3 Bottom-up Verification. 3 Simulation Tools in System Design. 3.1 Use of Simulation Tools within the Design Flow. 3.2 Specific Simulation Algorithms of RF Simulators. 3.3 Criteria of the Simulator Selection. 3.4 Internet Resources for Simulation Tools. 4 System Level Modeling. 4.1 System Level Simulation. 4.2 Simulation Technology of System Level Simulators. 4.3 Complex Baseband Simulation. 4.4 Model Libraries for System Simulation. 4.5 Creation of Own Primitive and Hierarchical Models. 5 VHDL-AMS for Block Level Simulation. 5.1 Introduction. 5.2 VHDL-AMS Standardization. 5.3 A Simple Block Level Example - Analog PLL. 5.4 Summary 6 Introduction to VHDL-AMS. 6.1 Aim of this Introduction. 6.2 Repetition of Basics of VHDL 1076-1993. 6.3 Conservative Systems Description. 6.4 Description of Nonconservative Systems. 6.5 Mixed-Signal Simulation. 6.6 Analysis Domains. 6.7 Summary. 7 Selected RF Blocks in VHDL-AMS. 7.1 Library Overview. 7.2 Signal Sources. 7.3 Basic RF Building Blocks. 7.4 Measurement and Observation Units. 7.5 Block Level Example of a Linear PLL. 8 Macromodeling in VHDL-AMS. 8.1 Introduction. 8.2 General Methodology. 8.3 Input and Output Stages. 8.3.1 Input stages. 8.3.2 Output stages. 8.4 OpAmp Macromodel. 9 Complex Example: WLAN Receiver. 9.1 Introduction. 9.2 Example Specification. 9.3 Example Modeling. 9.4 Example Calibration. 9.5 Example Verification. 10 Modeling of Analog Blocks in Verilog-A. 10.1 Introduction. 10.2 Writing Custom Behavioral Models. 10.3 Overview of the Cadence Model Library rfLib. 10.4 Modeling and Simulation of a WLAN Receiver. 11 Characterization forBottom-Up Verification. 11.1 Concept of Characterization. 11.2 RF Characteristics and Parameters. 11.3 Application of Characterization. 11.4 Example Characterization of an LNA. 11.5 Characterization Environment. 11.6 Characterization Using the OCEAN Script Language. 12 Advanced Methods for Overall System Specification and Validation. 12.1 Gap between System Level and Block Level Simulation. 12.2 File Coupling of Simulators. 12.3 Direct Cosimulation of System Level and Analog Simulators. 12.4 Generated Black Box Models. References. Index.

This paper describes a methodology to generate behavioral and functional level PLL models. Two sets of PLL block models are derived, one on functional level as a linear PLL and one on behavioral level as a nonlinear PLL. These sets, both containing a voltage controlled oscillator, a phase detector, a frequency divider and reference oscillator, allow for example to build a digital PLL as an RF frequency synthesizer. The models include the most important effects as well as noise in the frequency domain, which influence the behavior of the total PLL. The behavioral model is not valid only for the time domain but also for the frequency domain, which is essential for the characterization of RF blocks.

In modern distributed energy systems (DES), focus is shifting from the conventional centralized approach towards distributed architectures. However, modeling and analysis of these systems is more complex, as it involves the interface of multiple energy sources with many different type of loads through power electronics converters. The integration of power electronics converters allows distributed renewable energy sources to become part of modern electronics power distribution systems (EPDS). It will also facilitate the ongoing research towards DC-based DES which is mostly composed of commercial DC-DC converters whose internal structure and parameters are unknown. For the system level analysis, the behavioral modeling technique is the only choice. Since most power electronics converters are non-linear systems and linear models can’t model their dynamics to a desired level of accuracy, hence non-linear modeling is required for accurate modeling. The non-linear modeling approach presented here aims to develop behavioral models that can predict the response of the system over the entire operating range. In this work, either a lookup table or a polytopic structure-based modeling technique is used. The technique is further applied to cascade and parallel connected converters, being two DES scenarios. First the procedure is verified via application to switching models in a simulation and then validated for commercial converters via experiments. The results show that the developed behavioral models accurately predict both the transient and steady state response.

The development of mixed signal modelling languages such as VHDL-AMS and Verilog-A allows the behaviour of analogue and mixed signal circuits to be described at a higher level than is possible with conventional circuit-level (e.g. SPICE type) simulators. Mixed-signal simulators for these new languages can process such behavioural models far more efficiently than was previously possible. The non-linear characteristics of many circuits require differential equations to be used in the behavioural descriptions. Unfortunately, the methods used to solve these equations in a mixed-signal simulator are more computationally demanding than those required to evaluate the logic expressions used to describe digital circuits. Consequently, the simulation of analogue and mixed-signal circuits is still far less efficient than the simulation of a pure digital circuit. This paper presents an alternative approach to modelling the behaviour of analogue and mixed-signal circuits based upon piecewise linear (PWL) waveforms. Since the signals processed by these models are linearised, direct methods (i.e. non-iterative) can be used to simulate their behaviour. Several alternative methods are discussed. Examples showing the application of this approach to commonly used circuits are developed and the results compared against corresponding models written for conventional simulators. Finally, suggestions are given for implementing these techniques in a VHDL simulators. (6 pages)

The event-driven (ED) model is an efficient, behavioral-level simulation model that is primarily used in mixed-signal simulations. This paper builds a fast and accurate model for fractional-N charge pump phase-locked loop (CPPLL) simulating. In this model, between two adjacent events, the process is divided into two stages. In the first stage, due to the current injection, it is necessary to update the last processing result to improve the accuracy of the model. The second stage is to calculate the result of the next variable set. Compared to the conventional behavioral simulation model, which is simulated by the AMS simulator in Cadence, the proposed model only consumes one-tenth of the time.

In this paper an equivalent complex baseband representation of the analog component related non-linearity of digital transmitters relying on 1-bit complex baseband encoding is derived. By exploiting the properties of the pulsed RF encoding the novel behavioral modeling technique is able to represent accurately the non-linear memory effects of the power amplification stage. Furthermore, a band-limited kernel technique leads to more efficient modeling of the complete digital transmitter, and to relaxed sampling rate. For the parameter estimation the linear regression, common to Volterra model identification, can be employed. According to the simulation and measurement based verification results, the novel modeling technique excels the state-of-the-art in terms of modeling accuracy. It can be assumed that the given methodology serves both as a basis for future behavioral models and for the development of advanced encoding techniques for linearization purposes.

A new methodology called induced behavioral fault modeling is presented for fault simulation and testing of analog and mixed-signal circuits, and demonstrated using voltage controlled oscillators and phase-locked loops. The key of this methodology is to use actual layout and process defect information to derive a set of realistic faults at the transistor level, and to generate a small set of behavioral fault models-in a mixed-signal hardware description language-that abstracts the derived transistor-level faults. While ensuring the same fault coverage as transistor-level fault modeling, induced behavioral fault modeling offers significant advantage in simulation efficiency and modeling capacity.

A Doherty amplifier was investigated using behavioral amplifier modeling techniques. A measurement system for simultaneous wide bandwidth (>100 MHz) and large dynamic range (-70 dBc ACLR) sampled measurements that are needed for the behavioral modeling is presented. Both the measurement system and the performance of the Doherty amplifier are described. The findings are that the well known and widely used parallel Hammerstein model, also denoted the memory polynomial model, is well suitable also for modeling and predistortion of Doherty amplifiers, and that a Doherty amplifier can be designed with only minor memory effects. This is seen in the modeling error, which is reduced by 13 dB, compared with a constant gain, using a memoryless polynomial model and only 6 dB further by adding memory to the model.

Owing to verification bottleneck, analog and mixed signal IPs like Phase Locked Loop (PLL) take considerable amount of design cycle time. To evade this, we have presented detailed real value modeling approach, which facilitate analog accuracy in digital simulation, for Spread Spectrum Clock Generator (SSCG) PLL. SSCG PLLs are universally used to avoid Electro-migration Interference (EMI), major cause for silicon failure. The objective of the work is to accurately model SSCG PLL along with individual analog effects and noise behavior for full functional Mixed Signal System on Chip (MSSoC) verification. The accuracy of proposed modeling approach is validated through equivalence check with Post Layout SPICE (PLS). Such efficient models provide simulation speed-up hence, reducing Time To Market (TTM).

Earlier in Strategic Highway Research Program 2 (SHRP 2) research, Reliability Project L11: Evaluating Alternative Operations Strategies to Improve Travel Time Reliability presented a novel approach to establishing an economic value for travel time reliability. The approach is based on the real options theory from the financial sector, in which an individual purchases an insurance premium to protect against an undesirable outcome. The travel analogy is when a traveler knows the normal travel time to a destination but chooses an earlier departure time to reduce the risk of a late arrival. This extra travel time has a monetary value and represents the insurance premium that a traveler is willing to pay. The method is data driven, using actual local historical travel times as the basis for establishing a value of reliability, whereas previous methods have used behavioral modeling techniques. The Maryland State Highway Administration currently has a project development and programming process to address short-term congestion relief that includes the value of travel time reliability based on consolidated past research and methodologies. This project’s purpose was to further develop and pilot the viability of the options-theoretic approach in Maryland, establish a localized range of reliability values based on state travel time data, compare the new values to those already in use, apply the newly established values in the project development and analysis process, and conduct an assessment of the findings and results. A travel time data-driven methodology was constructed to produce a range of local values for reliability, and the sensitivity of this range of values was examined using an actual short-term congestion relief project to better understand application effects. While the data-driven method shows promise, additional validation is required on the underlying theories, method, and test application results. Suggestions for further research are presented. In addition, the applicability of incorporating the value of reliability into long-range project development, prioritization, and selection was explored as a proof of concept. Although the Maryland State Highway Administration is the basis for this pilot study, the underlying theory, principles, and data-driven method, as well as the further research suggestions, could be informative to other agencies and jurisdictions that have an interest in considering incorporating travel time reliability into their project development and programming decision-making process.

Memristive behavior has been clearly addressed through growth and shrinkage of thin filaments in metal-oxide junctions. Capacitance change has also been observed, raising the possibility of using them as memcapacitors. Therefore, this paper proves that metal-oxide junctions can behave as a memcapacitor element by analyzing its characteristics and modeling its memristive and memcapacitive behaviors. We develop two behavioral modeling techniques: charge-dependent memcapacitor model and voltage-dependent memcapacitor model. A new physical model for metal-oxide junctions is presented based on conducting filaments variations, and its effect on device capacitance and resistance. In this model, we apply the exponential nature of growth and shrinkage of thin filaments and use Simmons' tunneling equation to calculate the tunneling current. Simulation results show how the variations of practical device parameters can change the device behavior. They clarify the basic conditions for building a memcapacitor device with negligible change in resistance.

Data compilation methods, whether generated by remotely deployed sensors collecting physically observable measurements or collaborative communications, such as blogs and online survey forms, are amassing extremely large repositories of information. Compounding the overwhelming volume of historical and streaming information, is the quick turn-around required to analyze and report the essence of this information. For example, health care facilities must evaluate data collected by emergency rooms within the context of the individual patient's profile, the trend (over time and social agency) within the larger patient community, the payer's historically-generated statistics, and the provider's subjective, but expert, knowledge of the situation; and pattern recognition must be accomplished in the few hours prior to a treatment decision. Similarly, national security policy makers and action planners have a diversity of information, all with degrees of uncertainty about accuracy (vice precision), to be explored in a short time (hours to days) to support decisions with long-term international consequences. While models and simulations of actor behavior have a credible history of performance in specialized applications, these efforts typically require time-consuming human examination. Moreover, rarely do researchers apply statistical learning and modeling techniques understanding the outputs of large-scale models to go back and improve the simulation/models from such analysis. This panel discussion will highlight some of the recent successes in knowledge discovery and behavioral modeling techniques, as well as the research challenges remaining.

The advent of silicon chips that operate at 3 V and combine both analog and digital circuitry is discussed, and the engineering issues associated with 3 V mixed signal design are examined. The use of mixed-signal circuitry in communications applications such as the Ethernet interface analog macrocell is considered. Mixed signal simulation and testing are discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Summary1. Hodgson et al. [Journal of Applied Ecology46 (2009) 964] argue that connectivity is complex and uncertain, that it can be improved incidentally by increasing habitat extent, and that connectivity conservation is unlikely to be effective under climate change.2. We believe that they have overlooked recent research on dispersal behaviour and structural connectivity, which has improved our understanding of functional connectivity and revealed that it will not necessarily increase with habitat extent.3. New modelling techniques including least‐cost path models incorporate this more detailed understanding of connectivity into conservation planning, facilitating the true aim of connectivity conservation – to ensure appropriate interactions between habitat extent, quality and connectivity.4. Synthesis and applications. Advances in behavioural research and modelling techniques allow us to manage structural connectivity with as much certainty as we manage extent and quality of habitat. Successful landscape conservation to address both current threats and future climate change must manage these three elements in concert.

This study aims to determine the effectiveness of behavioral counseling modeling techniques to improve the character able to independent characters through lesson. This research is a quasi research (Quasi Experiment). The purpose of this study was to determine the effectiveness of modeling techniques in behavioral counseling models for able to independent of UPW B grade X students. The process of collecting data in this study used observation sheets, interview sheets, diaries and character able to independent questionnaires. The results of this study experimental group treated with modeling techniques indicator 1 produces tcount = 3.57> ttable = 1.94, From this study effective modeling techniques to improve the character able to independent