1,081 research outputs found

    A survey and taxonomy of layout compaction algorithms

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    This paper presents a survey and a taxonomy of layout compaction algorithms, which are an essential part of modern symbolic layout tools employed in VLSI circuit design. Layout compaction techniques are also used in the low-end stages of silicon compilation tools and module generators. The paper addresses the main algorithms used in compaction, focusing on their implementation characteristics, performance, advantages and drawbacks. Compaction is a highly important operation to optimize the use of silicon area, achieve higher speed through wire length minimization, support technology retargeting and also allow the use of legacy layouts. Optimized cells that were developed for a fabrication process with a set of design rules have to be retargeted for a new and more compact process with a different set of design rules

    VLSI design methodology

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

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    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

    Transistor-Level Layout of Integrated Circuits

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    In this dissertation, we present the toolchain BonnCell and its underlying algorithms. It has been developed in close cooperation with the IBM Corporation and automatically generates the geometry for functional groups of 2 to approximately 50 transistors. Its input consists of a set of transistors, including properties like their sizes and their types, a specification of their connectivity, and parameters to flexibly control the technological framework as well as the algorithms' behavior. Using this data, the tool computes a detailed geometric realization of the circuit as polygonal shapes on 16 layers. To this end, a placement routine configures the transistors and arranges them in the plane, which is the main subject of this thesis. Subsequently, a routing engine determines wires connecting the transistors to ensure the circuit's desired functionality. We propose and analyze a family of algorithms that arranges sets of transistors in the plane such that a multi-criteria target function is optimized. The primary goal is to obtain solutions that are as compact as possible because chip area is a valuable resource in modern techologies. In addition to the core algorithms we formulate variants that handle particularly structured instances in a suitable way. We will show that for 90% of the instances in a representative test bed provided by IBM, BonnCell succeeds to generate fully functional layouts including the placement of the transistors and a routing of their interconnections. Moreover, BonnCell is in wide use within IBM's groups that are concerned with transistor-level layout - a task that has been performed manually before our automation was available. Beyond the processing of isolated test cases, two large-scale examples for applications of the tool in the industry will be presented: On the one hand the initial design phase of a large SRAM unit required only half of the expected 3 month period, on the other hand BonnCell could provide valuable input aiding central decisions in the early concept phase of the new 14 nm technology generation

    Integrated Circuits Parasitic Capacitance Extraction Using Machine Learning and its Application to Layout Optimization

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    The impact of parasitic elements on the overall circuit performance keeps increasing from one technology generation to the next. In advanced process nodes, the parasitic effects dominate the overall circuit performance. As a result, the accuracy requirements of parasitic extraction processes significantly increased, especially for parasitic capacitance extraction. Existing parasitic capacitance extraction tools face many challenges to cope with such new accuracy requirements that are set by semiconductor foundries (\u3c 5% error). Although field-solver methods can meet such requirements, they are very slow and have a limited capacity. The other alternative is the rule-based parasitic capacitance extraction methods, which are faster and have a high capacity; however, they cannot consistently provide good accuracy as they use a pre-characterized library of capacitance formulas that cover a limited number of layout patterns. On the other hand, the new parasitic extraction accuracy requirements also added more challenges on existing parasitic-aware routing optimization methods, where simplified parasitic models are used to optimize layouts. This dissertation provides new solutions for interconnect parasitic capacitance extraction and parasitic-aware routing optimization methodologies in order to cope with the new accuracy requirements of advanced process nodes as follows. First, machine learning compact models are developed in rule-based extractors to predict parasitic capacitances of cross-section layout patterns efficiently. The developed models mitigate the problems of the pre-characterized library approach, where each compact model is designed to extract parasitic capacitances of cross-sections of arbitrary distributed metal polygons that belong to a specific set of metal layers (i.e., layer combination) efficiently. Therefore, the number of covered layout patterns significantly increased. Second, machine learning compact models are developed to predict parasitic capacitances of middle-end-of-line (MEOL) layers around FINFETs and MOSFETs. Each compact model extracts parasitic capacitances of 3D MEOL patterns of a specific device type regardless of its metal polygons distribution. Therefore, the developed MEOL models can replace field-solvers in extracting MEOL patterns. Third, a novel accuracy-based hybrid parasitic capacitance extraction method is developed. The proposed hybrid flow divides a layout into windows and extracts the parasitic capacitances of each window using one of three parasitic capacitance extraction methods that include: 1) rule-based; 2) novel deep-neural-networks-based; and 3) field-solver methods. This hybrid methodology uses neural-networks classifiers to determine an appropriate extraction method for each window. Moreover, as an intermediate parasitic capacitance extraction method between rule-based and field-solver methods, a novel deep-neural-networks-based extraction method is developed. This intermediate level of accuracy and speed is needed since using only rule-based and field-solver methods (for hybrid extraction) results in using field-solver most of the time for any required high accuracy extraction. Eventually, a parasitic-aware layout routing optimization and analysis methodology is implemented based on an incremental parasitic extraction and a fast optimization methodology. Unlike existing flows that do not provide a mechanism to analyze the impact of modifying layout geometries on a circuit performance, the proposed methodology provides novel sensitivity circuit models to analyze the integrity of signals in layout routes. Such circuit models are based on an accurate matrix circuit representation, a cost function, and an accurate parasitic sensitivity extraction. The circuit models identify critical parasitic elements along with the corresponding layout geometries in a certain route, where they measure the sensitivity of a route’s performance to corresponding layout geometries very fast. Moreover, the proposed methodology uses a nonlinear programming technique to optimize problematic routes with pre-determined degrees of freedom using the proposed circuit models. Furthermore, it uses a novel incremental parasitic extraction method to extract parasitic elements of modified geometries efficiently, where the incremental extraction is used as a part of the routing optimization process to improve the optimization runtime and increase the optimization accuracy

    An approach to display layout of dynamic windows

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    The development of windows based user interface has introduced a new dimension to the field of human computer interaction. Now a user is able to perform multiple tasks at a time, often switching from one task to another. However windows environment also imposes the burden of manual windows management on the user. Several studies have suggested that manual window management is an unproductive chore often resulting in clutter and confusion on the display screen. Therefore we need a automatic windows layout generator to free the user to perform other useful tasks. This thesis introduces SPORDAC {Shadow Propagation for Overlap Removal and Display Area Compaction) algorithm. This algorithm aims to remove overlap from the display layout and encapsulate the layout in the finite display area. The SPORDAC prototype integrates the SPORDAC algorithm with simulated annealing to optimise the display area usage. The usefulness and applicability of the SPORDAC approach are illustrated with the implementation of a prototype, samples of generated layouts and analysis of the collected dat
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