Hybrid Verification for Analog and Mixed-signal Circuits

With increasing design complexity and reliability requirements, analog and mixedsignal (AMS) verification manifests itself as a key bottleneck. While formal methods and machine learning have been proposed for AMS verification, these two types of techniques suffer from their own limitations, with the former being specifically limited by scalability and the latter by inherent errors in learning-based models. We present a new direction in AMS verification by proposing a hybrid formal/machinelearning- based verification technique (HFMV) to combine the best of the two worlds. HFMV builds formalism on the top of a machine learning model to verify AMS circuits efficiently while meeting a user-specified confidence level. Guided by formal checks, HFMV intelligently explores the high-dimensional parameter space of a given design by iteratively improving the machine learning model. As a result, it leads to accurate failure prediction in the case of a failing circuit or a reliable pass decision in the case of a good circuit. Our experimental results demonstrate that the proposed HFMV approach is capable of identifying hard-to-find failures which are completely missed by a huge number of random simulation samples while significantly cutting down training sample size and verification cycle time

Hybrid Verification for Analog and Mixed-signal Circuits

With increasing design complexity and reliability requirements, analog and mixedsignal (AMS) verification manifests itself as a key bottleneck. While formal methods and machine learning have been proposed for AMS verification, these two types of techniques suffer from their own limitations, with the former being specifically limited by scalability and the latter by inherent errors in learning-based models. We present a new direction in AMS verification by proposing a hybrid formal/machinelearning- based verification technique (HFMV) to combine the best of the two worlds. HFMV builds formalism on the top of a machine learning model to verify AMS circuits efficiently while meeting a user-specified confidence level. Guided by formal checks, HFMV intelligently explores the high-dimensional parameter space of a given design by iteratively improving the machine learning model. As a result, it leads to accurate failure prediction in the case of a failing circuit or a reliable pass decision in the case of a good circuit. Our experimental results demonstrate that the proposed HFMV approach is capable of identifying hard-to-find failures which are completely missed by a huge number of random simulation samples while significantly cutting down training sample size and verification cycle time

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

Exploiting Bounds Optimization for the Semi-formal Verification of Analog Circuits

This paper proposes a semi-formal methodology for modeling and verification of analog circuits behavioral properties using multivariate optimization techniques. Analog circuit differential models are automatically extracted and their qualitative behavior is computed for interval-valued parameters, inputs and initial conditions. The method has the advantage of guaranteeing the rough enclosure of any possible dynamical behavior of analog circuits. The circuit behavioral properties are then verified on the generated transient response bounds. Experimental results show that the resulting state variable envelopes can be effectively employed for a sound verification of analog circuit properties, in an acceptable run-time

Product assurance technology for custom LSI/VLSI electronics

The technology for obtaining custom integrated circuits from CMOS-bulk silicon foundries using a universal set of layout rules is presented. The technical efforts were guided by the requirement to develop a 3 micron CMOS test chip for the Combined Release and Radiation Effects Satellite (CRRES). This chip contains both analog and digital circuits. The development employed all the elements required to obtain custom circuits from silicon foundries, including circuit design, foundry interfacing, circuit test, and circuit qualification

Design study of a low cost civil aviation GPS receiver system

A low cost Navstar receiver system for civil aviation applications was defined. User objectives and constraints were established. Alternative navigation processing design trades were evaluated. Receiver hardware was synthesized by comparing technology projections with various candidate system designs. A control display unit design was recommended as the result of field test experience with Phase I GPS sets and a review of special human factors for general aviation users. Areas requiring technology development to ensure a low cost Navstar Set in the 1985 timeframe were identified

Formal verification of analog and mixed signal circuits using deductive and bounded approaches

This thesis presents novel formal verification techniques to verify the important property of inevitability of states in analog and mixed signal (AMS) circuits. Two techniques to verify the inevitability of phase locking in a Charge Pump Phase Lock Loop (PLL) circuit are presented: mixed deductivebounded and deductive-only verification approaches. The deductive-bounded approach uses Lyapunov-like certificates with bounded advection of sets to verify the inevitability of phase locking. The deductive-only technique uses a combination of Lyapunov and Escape certificates to verify the inevitability property. Both deductive-only and deductive-bounded verification approaches involve positivity/negativity checks of polynomials over semi-algebraic sets, which both belong to the NP-hard set of problems. The Sum of Squares (SOS) programming technique is used to transform the positivity tests of polynomials to the feasibility of semi-definite programs. The efficacy of the approach is demonstrated by verifying the inevitability of phase locking for a third and fourth order CP PLL. Similarly, the inevitability of oscillation in ring oscillators (ROs) is verified using a numeric-symbolic deductive approach. The global inevitability (of oscillation) property is specified as a conjunction of several sub-properties that are verified via different Lyapunov-like certificates in different subsets of the state space. The construction of these certificates is posed as the verification of First Order Formulas (FOFs) having Universal-Existential quantifiers. A tractable numeric-symbolic approach, based on SOS programming and Quantifier Elimination (QE), is used to verify these FOFs. The approach is applied to the verification of inevitability of oscillation in ROs with odd and even topologies. Furthermore, frequency domain properties specification and verification for analog oscillators is presented. The behaviour of an oscillator in the frequency domain is specified, while it operates in close proximity to the desired limit cycle, employing finite Fourier series representation of a periodic signal. To be sufficiently robust enough against parameter variations, robustness of parameters is introduced in these specifications. These frequency domain properties are verified using a mixed time-frequency domain technique based on Satisfiability Modulo Ordinary Differential Equation (SMODE). The efficacy of the technique is demonstrated for the benchmark voltage controlled and tunnel diode oscillators

Methoden und Beschreibungssprachen zur Modellierung und Verifikation vonSchaltungen und Systemen: MBMV 2015 - Tagungsband, Chemnitz, 03. - 04. März 2015

Der Workshop Methoden und Beschreibungssprachen zur Modellierung und Verifikation von Schaltungen und Systemen (MBMV 2015) findet nun schon zum 18. mal statt. Ausrichter sind in diesem Jahr die Professur Schaltkreis- und Systementwurf der Technischen Universität Chemnitz und das Steinbeis-Forschungszentrum Systementwurf und Test. Der Workshop hat es sich zum Ziel gesetzt, neueste Trends, Ergebnisse und aktuelle Probleme auf dem Gebiet der Methoden zur Modellierung und Verifikation sowie der Beschreibungssprachen digitaler, analoger und Mixed-Signal-Schaltungen zu diskutieren. Er soll somit ein Forum zum Ideenaustausch sein. Weiterhin bietet der Workshop eine Plattform für den Austausch zwischen Forschung und Industrie sowie zur Pflege bestehender und zur Knüpfung neuer Kontakte. Jungen Wissenschaftlern erlaubt er, ihre Ideen und Ansätze einem breiten Publikum aus Wissenschaft und Wirtschaft zu präsentieren und im Rahmen der Veranstaltung auch fundiert zu diskutieren. Sein langjähriges Bestehen hat ihn zu einer festen Größe in vielen Veranstaltungskalendern gemacht. Traditionell sind auch die Treffen der ITGFachgruppen an den Workshop angegliedert. In diesem Jahr nutzen zwei im Rahmen der InnoProfile-Transfer-Initiative durch das Bundesministerium für Bildung und Forschung geförderte Projekte den Workshop, um in zwei eigenen Tracks ihre Forschungsergebnisse einem breiten Publikum zu präsentieren. Vertreter der Projekte Generische Plattform für Systemzuverlässigkeit und Verifikation (GPZV) und GINKO - Generische Infrastruktur zur nahtlosen energetischen Kopplung von Elektrofahrzeugen stellen Teile ihrer gegenwärtigen Arbeiten vor. Dies bereichert denWorkshop durch zusätzliche Themenschwerpunkte und bietet eine wertvolle Ergänzung zu den Beiträgen der Autoren. [... aus dem Vorwort