Tensor Computation: A New Framework for High-Dimensional Problems in EDA

Many critical EDA problems suffer from the curse of dimensionality, i.e. the very fast-scaling computational burden produced by large number of parameters and/or unknown variables. This phenomenon may be caused by multiple spatial or temporal factors (e.g. 3-D field solvers discretizations and multi-rate circuit simulation), nonlinearity of devices and circuits, large number of design or optimization parameters (e.g. full-chip routing/placement and circuit sizing), or extensive process variations (e.g. variability/reliability analysis and design for manufacturability). The computational challenges generated by such high dimensional problems are generally hard to handle efficiently with traditional EDA core algorithms that are based on matrix and vector computation. This paper presents "tensor computation" as an alternative general framework for the development of efficient EDA algorithms and tools. A tensor is a high-dimensional generalization of a matrix and a vector, and is a natural choice for both storing and solving efficiently high-dimensional EDA problems. This paper gives a basic tutorial on tensors, demonstrates some recent examples of EDA applications (e.g., nonlinear circuit modeling and high-dimensional uncertainty quantification), and suggests further open EDA problems where the use of tensor computation could be of advantage.Comment: 14 figures. Accepted by IEEE Trans. CAD of Integrated Circuits and System

Technology Independent Synthesis of CMOS Operational Amplifiers

Analog circuit design does not enjoy as much automation as its digital counterpart. Analog sizing is inherently knowledge intensive and requires accurate modeling of the different parametric effects of the devices. Besides, the set of constraints in a typical analog design problem is large, involving complex tradeoffs. For these reasons, the task of modeling an analog design problem in a form viable for automation is much more tedious than the digital design. Consequently, analog blocks are still handcrafted intuitively and often become a bottleneck in the integrated circuit design, thereby increasing the time to market. In this work, we address the problem of automatically solving an analog circuit design problem. Specifically, we propose methods to automate the transistor-level sizing of OpAmps. Given the specifications and the netlist of the OpAmp, our methodology produces a design that has the accuracy of the BSIM models used for simulation and the advantage of a quick design time. The approach is based on generating an initial first-order design and then refining it. In principle, the refining approach is a simulated-annealing scheme that uses (i) localized simulations and (ii) convex optimization scheme (COS). The optimal set of input variables for localized simulations has been selected by using techniques from Design of Experiments (DOE). To formulate the design problem as a COS problem, we have used monomial circuit models that are fitted from simulation data. These models accurately predict the performance of the circuit in the proximity of the initial guess. The models can also be used to gain valuable insight into the behavior of the circuit and understand the interrelations between the different performance constraints. A software framework that implements this methodology has been coded in SKILL language of Cadence. The methodology can be applied to design different OpAmp topologies across different technologies. In other words, the framework is both technology independent and topology independent. In addition, we develop a scheme to empirically model the small signal parameters like \u27gm\u27 and \u27gds\u27 of CMOS transistors. The monomial device models are reusable for a given technology and can be used to formulate the OpAmp design problem as a COS problem. The efficacy of the framework has been demonstrated by automatically designing different OpAmp topologies across different technologies. We designed a two-stage OpAmp and a telescopic OpAmp in TSMC025 and AMI016 technologies. Our results show significant (10–15%) improvement in the performance of both the OpAmps in both the technologies. While the methodology has shown encouraging results in the sub-micrometer regime, the effectiveness of the tool has to be investigated in the deep-sub-micron technologies

CMOS design of chaotic oscillators using state variables: a monolithic Chua's circuit

This paper presents design considerations for monolithic implementation of piecewise-linear (PWL) dynamic systems in CMOS technology. Starting from a review of available CMOS circuit primitives and their respective merits and drawbacks, the paper proposes a synthesis approach for PWL dynamic systems, based on state-variable methods, and identifies the associated analog operators. The GmC approach, combining quasi-linear VCCS's, PWL VCCS's, and capacitors is then explored regarding the implementation of these operators. CMOS basic building blocks for the realization of the quasi-linear VCCS's and PWL VCCS's are presented and applied to design a Chua's circuit IC. The influence of GmC parasitics on the performance of dynamic PWL systems is illustrated through this example. Measured chaotic attractors from a Chua's circuit prototype are given. The prototype has been fabricated in a 2.4- mu m double-poly n-well CMOS technology, and occupies 0.35 mm/sup 2/, with a power consumption of 1.6 mW for a +or-2.5-V symmetric supply. Measurements show bifurcation toward a double-scroll Chua's attractor by changing a bias current

Design and application of reconfigurable circuits and systems

Open Acces

Optimization of Short-Channel RF CMOS Low Noise Amplifiers by Geometric Programming

Geometric programming (GP) is an optimization method to produce globally optimal circuit parameters with high computational efficiency. Such a method has been applied to short-channel (90 nm and 180 nm) CMOS Low Noise Amplifiers (LNAs) with common-source inductive degeneration to obtain optimal design parameters by minimizing the noise figure. An extensive survey of analytical models and experimental results reported in the literature was carried out to quantify the issue of excessive thermal noise for short-channel MOSFETs. Geometric programming compatible functions have been determined to calculate the noise figure of short-channel CMOS devices by taking into consideration channel-length modulation and velocity saturation effects

Time-domain optimization of amplifiers based on distributed genetic algorithms

Thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Electrical and Computer EngineeringThe work presented in this thesis addresses the task of circuit optimization, helping the designer facing the high performance and high efficiency circuits demands of the market and technology evolution. A novel framework is introduced, based on time-domain analysis, genetic algorithm optimization, and distributed processing. The time-domain optimization methodology is based on the step response of the amplifier. The main advantage of this new time-domain methodology is that, when a given settling-error is reached within the desired settling-time, it is automatically guaranteed that the amplifier has enough open-loop gain, AOL, output-swing (OS), slew-rate (SR), closed loop bandwidth and closed loop stability. Thus, this simplification of the circuit‟s evaluation helps the optimization process to converge faster. The method used to calculate the step response expression of the circuit is based on the inverse Laplace transform applied to the transfer function, symbolically, multiplied by 1/s (which represents the unity input step). Furthermore, may be applied to transfer functions of circuits with unlimited number of zeros/poles, without approximation in order to keep accuracy. Thus, complex circuit, with several design/optimization degrees of freedom can also be considered. The expression of the step response, from the proposed methodology, is based on the DC bias operating point of the devices of the circuit. For this, complex and accurate device models (e.g. BSIM3v3) are integrated. During the optimization process, the time-domain evaluation of the amplifier is used by the genetic algorithm, in the classification of the genetic individuals. The time-domain evaluator is integrated into the developed optimization platform, as independent library, coded using C programming language. The genetic algorithms have demonstrated to be a good approach for optimization since they are flexible and independent from the optimization-objective. Different levels of abstraction can be optimized either system level or circuit level. Optimization of any new block is basically carried-out by simply providing additional configuration files, e.g. chromosome format, in text format; and the circuit library where the fitness value of each individual of the genetic algorithm is computed. Distributed processing is also employed to address the increasing processing time demanded by the complex circuit analysis, and the accurate models of the circuit devices. The communication by remote processing nodes is based on Message Passing interface (MPI). It is demonstrated that the distributed processing reduced the optimization run-time by more than one order of magnitude. Platform assessment is carried by several examples of two-stage amplifiers, which have been optimized and successfully used, embedded, in larger systems, such as data converters. A dedicated example of an inverter-based self-biased two-stage amplifier has been designed, laid-out and fabricated as a stand-alone circuit and experimentally evaluated. The measured results are a direct demonstration of the effectiveness of the proposed time-domain optimization methodology.Portuguese Foundation for the Science and Technology (FCT