Automatic synthesis of analog layout : a survey

A review of recent research in the automatic synthesis of physical geometry for analog integrated circuits is presented. On introduction, an explanation of the difficulties involved in analog layout as opposed to digital layout is covered. Review of the literature then follows. Emphasis is placed on the exposition of general methods for addressing problems specific to analog layout, with the details of specific systems only being given when they surve to illustrate these methods well. The conclusion discusses problems remaining and offers a prediction as to how technology will evolve to solve them. It is argued that although progress has been and will continue to be made in the automation of analog IC layout, due to fundamental differences in the nature of analog IC design as opposed to digital design, it should not be expected that the level of automation of the former will reach that of the latter any time soon

Global design of analog cells using statistical optimization techniques

We present a methodology for automated sizing of analog cells using statistical optimization in a simulation based approach. This methodology enables us to design complex analog cells from scratch within reasonable CPU time. Three different specification types are covered: strong constraints on the electrical performance of the cells, weak constraints on this performance, and design objectives. A mathematical cost function is proposed and a bunch of heuristics is given to increase accuracy and reduce CPU time to minimize the cost function. A technique is also presented to yield designs with reduced variability in the performance parameters, under random variations of the transistor technological parameters. Several CMOS analog cells with complexity levels up to 48 transistors are designed for illustration. Measurements from fabricated prototypes demonstrate the suitability of the proposed methodology

On the suitability and development of layout templates for analog layout reuse and layout-aware synthesis

Accelerating the synthesis of increasingly complex analog integrated circuits is key to bridge the widening gap between what we can integrate and what we can design while meeting ever-tightening time-to-market constraints. It is a well-known fact in the semiconductor industry that such goal can only be attained by means of adequate CAD methodologies, techniques, and accompanying tools. This is particularly important in analog physical synthesis (a.k.a. layout generation), where large sensitivities of the circuit performances to the many subtle details of layout implementation (device matching, loading and coupling effects, reliability, and area features are of utmost importance to analog designers), render complete automation a truly challenging task. To approach the problem, two directions have been traditionally considered, knowledge-based and optimization-based, both with their own pros and cons. Besides, recently reported solutions oriented to speed up the overall design flow by means of reuse-based practices or by cutting off time-consuming, error-prone spins between electrical and layout synthesis (a technique known as layout-aware synthesis), rely on a outstandingly rapid yet efficient layout generation method. This paper analyses the suitability of procedural layout generation based on templates (a knowledge-based approach) by examining the requirements that both layout reuse and layout-aware solutions impose, and how layout templates face them. The ability to capture the know-how of experienced layout designers and the turnaround times for layout instancing are considered main comparative aspects in relation to other layout generation approaches. A discussion on the benefit-cost trade-off of using layout templates is also included. In addition to this analysis, the paper delves deeper into systematic techniques to develop fully reusable layout templates for analog circuits, either for a change of the circuit sizing (i.e., layout retargeting) or a change of the fabrication process (i.e., layout migration). Several examples implemented with the Cadence's Virtuoso tool suite are provided as demonstration of the paper's contributions.Ministerio de Educación y Ciencia TEC2004-0175

A Reuse-based framework for the design of analog and mixed-signal ICs

Despite the spectacular breakthroughs of the semiconductor industry, the ability to design integrated circuits (ICs) under stringent time-to-market (TTM) requirements is lagging behind integration capacity, so far keeping pace with still valid Moore's Law. The resulting gap is threatening with slowing down such a phenomenal growth. The design community believes that it is only by means of powerful CAD tools and design methodologies -and, possibly, a design paradigm shift-that this design gap can be bridged. In this sense, reuse-based design is seen as a promising solution, and concepts such as IP Block, Virtual Component, and Design Reuse have become commonplace thanks to the significant advances in the digital arena. Unfortunately, the very nature of analog and mixed-signal (AMS) design has hindered a similar level of consensus and development. This paper presents a framework for the reuse-based design of AMS circuits. The framework is founded on three key elements: (1) a CAD-supported hierarchical design flow that facilitates the incorporation of AMS reusable blocks, reduces the overall design time, and expedites the management of increasing AMS design complexity; (2) a complete, clear definition of the AMS reusable block, structured into three separate facets or views: the behavioral, structural, and layout facets, the two first for top-down electrical synthesis and bottom-up verification, the latter used during bottom-up physical synthesis; (3) the design for reusability set of tools, methods, and guidelines that, relying on intensive parameterization as well as on design knowledge capture and encapsulation, allows to produce fully reusable AMS blocks. A case study and a functional silicon prototype demonstrate the validity of the paper's proposals.Ministerio de Educación y Ciencia TEC2004-0175

Baseband analog front-end and digital back-end for reconfigurable multi-standard terminals

Multimedia applications are driving wireless network operators to add high-speed data services such as Edge (E-GPRS), WCDMA (UMTS) and WLAN (IEEE 802.11a,b,g) to the existing GSM network. This creates the need for multi-mode cellular handsets that support a wide range of communication standards, each with a different RF frequency, signal bandwidth, modulation scheme etc. This in turn generates several design challenges for the analog and digital building blocks of the physical layer. In addition to the above-mentioned protocols, mobile devices often include Bluetooth, GPS, FM-radio and TV services that can work concurrently with data and voice communication. Multi-mode, multi-band, and multi-standard mobile terminals must satisfy all these different requirements. Sharing and/or switching transceiver building blocks in these handsets is mandatory in order to extend battery life and/or reduce cost. Only adaptive circuits that are able to reconfigure themselves within the handover time can meet the design requirements of a single receiver or transmitter covering all the different standards while ensuring seamless inter-interoperability. This paper presents analog and digital base-band circuits that are able to support GSM (with Edge), WCDMA (UMTS), WLAN and Bluetooth using reconfigurable building blocks. The blocks can trade off power consumption for performance on the fly, depending on the standard to be supported and the required QoS (Quality of Service) leve

Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization.

The key operation in stochastic neural networks, which have become the state-of-the-art approach for solving problems in machine learning, information theory, and statistics, is a stochastic dot-product. While there have been many demonstrations of dot-product circuits and, separately, of stochastic neurons, the efficient hardware implementation combining both functionalities is still missing. Here we report compact, fast, energy-efficient, and scalable stochastic dot-product circuits based on either passively integrated metal-oxide memristors or embedded floating-gate memories. The circuit's high performance is due to mixed-signal implementation, while the efficient stochastic operation is achieved by utilizing circuit's noise, intrinsic and/or extrinsic to the memory cell array. The dynamic scaling of weights, enabled by analog memory devices, allows for efficient realization of different annealing approaches to improve functionality. The proposed approach is experimentally verified for two representative applications, namely by implementing neural network for solving a four-node graph-partitioning problem, and a Boltzmann machine with 10-input and 8-hidden neurons

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

Analysis of fault-tolerant neurocontrol architectures

The fault-tolerance of analog parallel distributed implementations of a multivariable aircraft neurocontroller is analyzed by simulating weight and neuron failures in a simplified scheme of analog processing based on the functional architecture of the ETANN chip (Electrically Trainable Artificial Neural Network). The neural information processing is found to be only partially distributed throughout the set of weights of the neurocontroller synthesized with the backpropagation algorithm. Although the degree of distribution of the neural processing, and consequently the fault-tolerance of the neurocontroller, could be enhanced using Locally Distributed Weight and Neuron Approaches, a satisfactory level of fault-tolerance could only be obtained by retraining the degrated VLSI neurocontroller. The possibility of maintaining neurocontrol performance and stability in the presence of single weight of neuron failures was demonstrated through an automated retraining procedure of the neurocontroller based on a pre-programmed choice and sequence of the training parameters