Optimal Power Delivery Strategy in Modern VLSI Design

Department of Electrical EngineeringIn a modern very-large-scale integration (VLSI) designs, heterogeneous architectural structures and various three-dimensional (3D) integration methods have been used in a hybrid manner. Recently, the industry has combined 3D VLSI technology with the heterogeneous technology of modern VLSI called chiplet. The 3D heterogeneous architectural structure is growing attention because it reduces costs and time-to-market by increasing manufacturing yield with high integration rate and modularization. However, a main design concern of heterogeneous 3D architectural structure is power management for lowering power consumption with maintaining the required power integrity from IR drop. Although the low-power design can be realized in front-end-of-line level by reduced power supply complementary metal???oxide???semiconductor technologies, the overall low-power system performance is available with a proper design of power delivery network (PDN) for chip-level modules and system-level architectural structure. Thus, there is a demand for both the coanalysis and optimization for both chip-level and system-level. We analyzed and optimized power delivery on-chip in various 3D integration environments, and we also have proposed a chip-package-PCB coanalysis methodology at the system level. For through-silicon-via (TSV)-based 3D integration circuit (IC), We have investigated and analyzed the voltage noise in a multi-layer 3D stacking with partial element equivalent circuit (PEEC)-based on-chip PDN and frequency-dependent TSV models. We also have proposed a wire-added multi-paired on-chip PDN structure to reduce voltage noise to reduce IR drop. The performance of TSV-based 3D ICs has also been improved by reducing wake-up time through our proposed adaptive power gating strategy with tapered TSVs. For die-to-wafer 3D IC, we have proposed a power delivery pathfinding methodology, which seeks to identify a nearly optimal PDN for a given design and PDN specification. Our pathfinding methodology exploits models for routability and worst IR drop, which helps reducing iterations between PDN design and circuit design in 3D IC implementation. We also have extended the observation to system-level, we have proposed a power integrity coanalysis methodology for multiple power domains in high-frequency memory systems. Our coanalysis methodology can analyze the tendencies in power integrity by using parametric methods with consideration of package-on-package integration. We have proved that our methodology can predict similar peak-to-peak ripple voltages that are comparable with the realistic simulations of high-speed low-power memory interfaces. Finally, we have proposed analysis and optimization methodologies that are generally applicable to various integration methods used in modern VLSI designs as computer-aided-design-based solutions.clos

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

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

Wrinkling behaviour of biaxial non-crimp fabrics during preforming

The necessary lightweighting of the transport sector to meet emission reduction targets can be helped through the expanded use of composites. However, for the high volume production of composites to be cost-effective, it is needed that they can be manufactured through automated liquid composite moulding (LCM). Furthermore, the defects that occur during the initial preforming stage of LCM, notably wrinkles, are a key obstacle preventing automation and adoption of LCM, because wrinkles significantly compromise the component performance, and because there is currently no reliable method for mitigating them. To pave the way towards wrinkling mitigation during preforming, this thesis aims to characterise the wrinkling behaviour of non-crimp fabrics (NCFs) as well as to investigate how the wrinkling severity is affected by the tool geometry. These aims are achieved through both experimental and numerical approaches. Firstly, experimental forming tests are conducted to characterise the mechanisms, severity and variability of wrinkling for a ±45° biaxial NCF during preforming, considering four contrasting benchmark geometries. Secondly, a large dataset of forming simulations for various tool geometries is generated and used to investigate the effect of geometry on wrinkling severity, and to develop a deep learning based surrogate model for rapidly predicting the fabric wrinkling over a given tool geometry. The results demonstrate that two macroscale wrinkling mechanisms exist for this NCF and that the most severe wrinkles occur consistently via lateral fabric compression during material draw-in rather than tow compression at shear-lockup. Furthermore, they show that the wrinkling variability is significant and is especially apparent for multi-layer forming. Additionally, the tool geometry is shown to have a substantial effect on wrinkling with more tapered geometries leading to less severe wrinkling. Lastly, the surrogate model is demonstrated to achieve similar predictions to the finite element simulations but at a much lower computational cost, thus enabling the optimisation of component geometry for minimal wrinkling