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

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

Symbolic Extraction for Estimating Analog Layout Parasitics in Layout-Aware Synthesis

This paper presents a new layout parasitics extraction paradigm, symbolic extraction, for use in layout-aware analog synthesis methodologies. Unlike traditional post-layout extraction, symbolic extraction extracts layout parasitics in symbolic form from parameterized layouts. As a result, parasitic values can be calculated directly from given circuit and layout parameters. In layout-aware circuit synthesis process, tasks of time-consuming layout re-gerenarion and re-extraction can be replaced by this fast parasitics calculation step. In the paper, we discuss how to integrate symbolic extraction into the existing analog design flow and how symbolic extraction can be implemented

Geometrically-constrained, parasitic-aware synthesis of analog ICs

In order to speed up the design process of analog ICs, iterations between different design stages should be avoided as much as possible. More specifically, spins between electrical and physical synthesis should be reduced for this is a very time-consuming task: if circuit performance including layout-induced degradations proves unacceptable, a re-design cycle must be entered, and electrical, physical, or both synthesis processes, would have to be repeated. It is also worth noting that if geometric optimization (e.g., area minimization) is undertaken after electrical synthesis, it may add up as another source of unexpected degradation of the circuit performance due to the impact of the geometric variables (e.g., transistor folds) on the device and the routing parasitic values. This awkward scenario is caused by the complete separation of said electrical and physical synthesis, a design practice commonly followed so far. Parasitic-aware synthesis, consisting in including parasitic estimates to the circuit netlist directly during electrical synthesis, has been proposed as solution. While most of the reported contributions either tackle parasitic-aware synthesis without paying special attention to geometric optimization or approach both issues only partially, this paper addresses the problem in a unified way. In what has been called layout-aware electrical synthesis, a simulation-based optimization algorithm explores the design space with geometric variables constrained to meet certain user-defined goals, which provides reliable estimates of layout-induced parasitics at each iteration, and, thereby, accurate evaluation of the circuit ultimate performance. This technique, demonstrated here through several design examples, requires knowing layout details beforehand; to facilitate this, procedural layout generation is used as physical synthesis approach due to its rapidness and ability to capture analog layout know-how.Ministerio de Educación y Ciencia TEC2004-0175

Modeling of thermally induced skew variations in clock distribution network

Clock distribution network is sensitive to large thermal gradients on the die as the performance of both clock buffers and interconnects are affected by temperature. A robust clock network design relies on the accurate analysis of clock skew subject to temperature variations. In this work, we address the problem of thermally induced clock skew modeling in nanometer CMOS technologies. The complex thermal behavior of both buffers and interconnects are taken into account. In addition, our characterization of the temperature effect on buffers and interconnects provides valuable insight to designers about the potential impact of thermal variations on clock networks. The use of industrial standard data format in the interface allows our tool to be easily integrated into existing design flow

CAD methodologies for low power and reliable 3D ICs

The main objective of this dissertation is to explore and develop computer-aided-design (CAD) methodologies and optimization techniques for reliability, timing performance, and power consumption of through-silicon-via(TSV)-based and monolithic 3D IC designs. The 3D IC technology is a promising answer to the device scaling and interconnect problems that industry faces today. Yet, since multiple dies are stacked vertically in 3D ICs, new problems arise such as thermal, power delivery, and so on. New physical design methodologies and optimization techniques should be developed to address the problems and exploit the design freedom in 3D ICs. Towards the objective, this dissertation includes four research projects. The first project is on the co-optimization of traditional design metrics and reliability metrics for 3D ICs. It is well known that heat removal and power delivery are two major reliability concerns in 3D ICs. To alleviate thermal problem, two possible solutions have been proposed: thermal-through-silicon-vias (T-TSVs) and micro-fluidic-channel (MFC) based cooling. For power delivery, a complex power distribution network is required to deliver currents reliably to all parts of the 3D IC while suppressing the power supply noise to an acceptable level. However, these thermal and power networks pose major challenges in signal routability and congestion. In this project, a co-optimization methodology for signal, power, and thermal interconnects in 3D ICs is presented. The goal of the proposed approach is to improve signal, thermal, and power noise metrics and to provide fast and accurate design space explorations for early design stages. The second project is a study on 3D IC partition. For a 3D IC, the target circuit needs to be partitioned into multiple parts then mapped onto the dies. The partition style impacts design quality such as footprint, wirelength, timing, and so on. In this project, the design methodologies of 3D ICs with different partition styles are demonstrated. For the LEON3 multi-core microprocessor, three partitioning styles are compared: core-level, block-level, and gate-level. The design methodologies for such partitioning styles and their implications on the physical layout are discussed. Then, to perform timing optimizations for 3D ICs, two timing constraint generation methods are demonstrated that lead to different design quality. The third project is on the buffer insertion for timing optimization of 3D ICs. For high performance 3D ICs, it is crucial to perform thorough timing optimizations. Among timing optimization techniques, buffer insertion is known to be the most effective way. The TSVs have a large parasitic capacitance that increases the signal slew and the delay on the downstream. In this project, a slew-aware buffer insertion algorithm is developed that handles full 3D nets and considers TSV parasitics and slew effects on delay. Compared with the well-known van Ginneken algorithm and a commercial tool, the proposed algorithm finds buffering solutions with lower delay values and acceptable runtime overhead. The last project is on the ultra-high-density logic designs for monolithic 3D ICs. The nano-scale 3D interconnects available in monolithic 3D IC technology enable ultra-high-density device integration at the individual transistor-level. The benefits and challenges of monolithic 3D integration technology for logic designs are investigated. First, a 3D standard cell library for transistor-level monolithic 3D ICs is built and their timing and power behavior are characterized. Then, various interconnect options for monolithic 3D ICs that improve design quality are explored. Next, timing-closed, full-chip GDSII layouts are built and iso-performance power comparisons with 2D IC designs are performed. Important design metrics such as area, wirelength, timing, and power consumption are compared among transistor-level monolithic 3D, gate-level monolithic 3D, TSV-based 3D, and traditional 2D designs.PhDCommittee Chair: Lim, Sung Kyu; Committee Member: Bakir, Muhannad; Committee Member: Kim, Hyesoon; Committee Member: Lee, Hsien-Hsin; Committee Member: Mukhopadhyay, Saiba

Evaluation and implementation of a 5-level hybrid DC-DC converter

In this work, a hybrid voltage regulator topology is analyzed, implemented, and evaluated. The common topologies of DC-DC converters have proven to be lacking in some aspects, such as integrability for buck converters, or maximum efficiency for switched-capacitor regulators. The hybrid topology tackles these shortcomings by combining the advantages of switched-capacitor and inductor-based voltage regulators. A 5-level buck converter is evaluated, implemented, and compared to other converter implementations using the same components. The 5-Level Buck converter can achieve 5 different levels, allowing it to cover 4 operation regions, each between 2 levels. Accordingly, it covers a wide range of output voltages. By reducing the voltage difference at the inductor input, the 5-level buck converter can use smaller inductor compared to both 3-level and conventional buck converters which makes it cheaper, smaller in size, and much more efficient. Simulations show proper functionality of the 5-Level topology, while putting restrictions on the inductor size, efficiency, and component footprint (or total converter area). A test PCB is implemented for verification of the functionality and experimental measurements show that for the same switching frequency and inductor size, the 5-level buck converter achieves up to 15% efficiency improvement over a conventional buck converter and a 3-level buck converter at certain output voltage ranges. Peak efficiency of 94% has been achieved by the 5-Level hybrid converter, which includes all external switching and conduction losses. The proposed hybrid topology proved to yield high conversion efficiency even in the face of component size limitations, which indicates potential benefit in using multilevel converters for several off-chip as well as on-chip applications

Enabling Design and Simulation of Massive Parallel Nanoarchitectures

A common element in emerging nanotechnologies is the increasing complex- ity of the problems to face when attempting the design phase, because issues related to technology, specific application and architecture must be evalu- ated simultaneously. In several cases faced problems are known, but require a fresh re-think on the basis of different constraints not enforced by standard design tools. Among the emerging nanotechnologies, the two-dimensional structures based on nanowire arrays is promising in particular for massively parallel architec- tures. Several studies have been proposed on the exploration of the space of architectural solutions, but only a few derived high-level information from the results of an extended and reliable characterization of low-level structures. The tool we present is of aid in the design of circuits based on nanotech- nologies, here discussed in the specific case of nanowire arrays, as best candi- date for massively parallel architectures. It enables the designer to start from a standard High-level Description Languages (HDL), inherits constraints at physical level and applies them when organizing the physical implementation of the circuit elements and of their connections. It provides a complete simu- lation environment with two levels of refinement. One for DC analysis using a fast engine based on a simple switch level model. The other for obtaining transient performance based on automatic extraction of circuit parasitics, on detailed device (nanowire-FET) information derived by experiments or by existing accurate models, and on spice-level modeling of the nanoarray. Re- sults about the method used for the design and simulation of circuits based on nanowire-FET and nanoarray will be presente

Constraint-Aware, Scalable, and Efficient Algorithms for Multi-Chip Power Module Layout Optimization

Moving towards an electrified world requires ultra high-density power converters. Electric vehicles, electrified aerospace, data centers, etc. are just a few fields among wide application areas of power electronic systems, where high-density power converters are essential. As a critical part of these power converters, power semiconductor modules and their layout optimization has been identified as a crucial step in achieving the maximum performance and density for wide bandgap technologies (i.e., GaN and SiC). New packaging technologies are also introduced to produce reliable and efficient multichip power module (MCPM) designs to push the current limits. The complexity of the emerging MCPM layouts is surpassing the capability of a manual, iterative design process to produce an optimum design with agile development requirements. An electronic design automation tool called PowerSynth has been introduced with ongoing research toward enhanced capabilities to speed up the optimized MCPM layout design process. This dissertation presents the PowerSynth progression timeline with the methodology updates and corresponding critical results compared to v1.1. The first released version (v1.1) of PowerSynth demonstrated the benefits of layout abstraction, and reduced-order modeling techniques to perform rapid optimization of the MCPM module compared to the traditional, manual, and iterative design approach. However, that version is limited by several key factors: layout representation technique, layout generation algorithms, iterative design-rule-checking (DRC), optimization algorithm candidates, etc. To address these limitations, and enhance PowerSynth’s capabilities, constraint-aware, scalable, and efficient algorithms have been developed and implemented. PowerSynth layout engine has evolved from v1.3 to v2.0 throughout the last five years to incorporate the algorithm updates and generate all 2D/2.5D/3D Manhattan layout solutions. These fundamental changes in the layout generation methodology have also called for updates in the performance modeling techniques and enabled exploring different optimization algorithms. The latest PowerSynth 2 architecture has been implemented to enable electro-thermo-mechanical and reliability optimization on 2D/2.5D/3D MCPM layouts, and set up a path toward cabinet-level optimization. PowerSynth v2.0 computer-aided design (CAD) flow has been hardware-validated through manufacturing and testing of an optimized novel 3D MCPM layout. The flow has shown significant speedup compared to the manual design flow with a comparable optimization result