Design, Extraction, and Optimization Tool Flows and Methodologies for Homogeneous and Heterogeneous Multi-Chip 2.5D Systems

Chip and packaging industries are making significant progress in 2.5D design as a result of increasing popularity of their application. In advanced high-density 2.5D packages, package redistribution layers become similar to chip Back-End-of-Line routing layers, and the gap between them scales down with pin density improvement. Chiplet-package interactions become significant and severely affect system performance and reliability. Moreover, 2.5D integration offers opportunities to apply novel design techniques. The traditional die-by-die design approach neither carefully considers these interactions nor fully exploits the cross-boundary design opportunities. This thesis presents chiplet-package cross-boundary design, extraction, analysis, and optimization tool flows and methodologies for high-density 2.5D packaging technologies. A holistic flow is presented that can capture all parasitics from chiplets and the package and improve system performance through iterative optimizations. Several design techniques are demonstrated for agile development and quick turn-around time. To validate the flow in silicon, a chip was taped out and studied in TSMC 65nm technology. As the holistic flow cannot handle heterogeneous technologies, in-context flows are presented. Three different flavors of the in-context flow are presented, which offer trade-offs between scalability and accuracy in heterogeneous 2.5D system designs. Inductance is an inseparable part of a package design. A holistic flow is presented that takes package inductance into account in timing analysis and optimization steps. Custom CAD tools are developed to make these flows compatible with the industry standard tools and the foundry model. To prove the effectiveness of the flows several design cases of an ARM Cortex-M0 are implemented for comparitive study

Physical design of USB1.1

In earlier days, interfacing peripheral devices to host computer has a big problematic. There existed so many different kinds’ ports like serial port, parallel port, PS/2 etc. And their use restricts many situations, Such as no hot-pluggability and involuntary configuration. There are very less number of methods to connect the peripheral devices to host computer. The main reason that Universal Serial Bus was implemented to provide an additional benefits compared to earlier interfacing ports. USB is designed to allow many peripheral be connecting using single standardize interface. It provides an expandable fast, cost effective, hot-pluggable plug and play serial hardware interface that makes life of computer user easier allowing them to plug different devices to into USB port and have them configured automatically. In this thesis demonstrated the USB v1.1 architecture part in briefly and generated gate level net list form RTL code by applying the different constraints like timing, area and power. By applying the various types design constraints so that the performance was improved by 30%. And then it implemented in physically by using SoC encounter EDI system, estimation of chip size, power analysis and routing the clock signal to all flip-flops presented in the design. To reduce the clock switching power implemented register clustering algorithm (DBSCAN). In this design implementation TSMC 180nm technology library is used

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

Design Methodologies and Architecture Solutions for High-Performance Interconnects

ABSTRACT In Deep Sub-Micron (DSM) technologies, interconnects play a crucial role in the correct functionality and largely impact the performance of complex System-on-Chip (SoC) designs. For technologies of 0.25µm and below, wiring capacitance dominates gate capacitance, thus rapidly increasing the interconnect-induced delay. Moreover, the coupling capacitance becomes a significant portion of the on-chip total wiring capacitance, and coupling between adjacent wires cannot be considered as a second-order effect any longer. As a consequence, the traditional top-down design methodology is ineffective, since the actual wiring delays can be computed only after layout parasitic extraction, when the physical design is completed. Fixing all the timing violations often requires several time-consuming iterations of logical and physical design, and it is essentially a trial-and-error approach. Increasingly tighter time-to-market requirements dictate that interconnect parasitics must be taken into account during all phases of the design flow, at different level of abstractions. However, given the aggressive technology scaling trends and the growing design complexity, this approach will only temporarily ameliorate the interconnect problem. We believe that in order to achieve gigascale designs in the nanometer regime, a novel design paradigm, based on new forms of regularity and newly created IP (Intellectual Property) blocks must be developed, to provide a direct path from system-level architectural exploration to physical implementation

Standard cell optimization and physical design in advanced technology nodes

Integrated circuits (ICs) are at the heart of modern electronics, which rely heavily on the state-of-the-art semiconductor manufacturing technology. The key to pushing forward semiconductor technology is IC feature-size miniaturization. However, this brings ever-increasing design complexities and manufacturing challenges to the $340 billion semiconductor industry. The manufacturing of two-dimensional layout on high-density metal layers depends on complex design-for-manufacturing techniques and sophisticated empirical optimizations, which introduces huge amounts of turnaround time and yield loss in advanced technology nodes. Our study reveals that unidirectional layout design can significantly reduce the manufacturing complexities and improve the yield, which is becoming increasingly adopted in semiconductor industry [61, 89]. The lithography printing of unidirectional layout can be tightly controlled using advanced patterning techniques, such as self-aligned double and quadruple patterning. Despite the manufacturing benefits, unidirectional layout leads to more restrictive solution space and brings significant impacts on the IC design automation ow for routing closure. Notably, unidirectional routing limits the standard cell pin accessibility, which further exacerbates the resource competitions during routing. Moreover, for post-routing optimization, traditional redundant-via insertion has become obsolete under unidirectional routing style, which makes the yield enhancement task extremely challenging. Regardless of complex multiple patterning and design-for-manufacturing approaches, mask optimization through resolution enhancement techniques remains as the key strategy to improve the yield of the semiconductor manufacturing processes. Among them, Sub-Resolution Assist Feature (SRAF) generation is a very important method to improve lithographic process windows. Model-based SRAF generation has been widely used to achieve high accuracy but it is time-consuming and hard to obtain consistent SRAFs. This dissertation proposes novel CAD algorithms and methodologies for standard cell optimization and physical design in advanced technology nodes, which ultimately reduces the design cycle and manufacturing cost of IC design. First, a standard cell pin access optimization engine is proposed to evaluate the pin accessibility of a given standard cell library. We further propose novel pin access planning techniques and concurrent pin access optimizations to efficiently resolve the routing resource competitions, which generates much better routing solutions than state-of-the-art, manufacturing-friendly routers. To systematically improve the manufacturing yield in the post-routing stage, a global optimization engine has been introduced for redundant local-loop insertion considering advanced manufacturing constraints. Finally, we propose the first machine learning-based framework for fast yet consistent SRAF generation with the high quality of results.Electrical and Computer Engineerin

Timing-Constrained Global Routing with RC-Aware Steiner Trees and Routing Based Optimization

In this thesis we consider the global routing problem, which arises as one of the major subproblems in the physical design step in VLSI design. In global routing, we are given a three-dimensional grid graph G with edge capacities representing available routing space, and we have to connect a set of nets in G without overusing any edge capacities. Here, each net consists of a set of pins corresponding to vertices of G, where one pin is the sender of signals, while all other pins are receivers. Traditionally, next to obeying all edge capacity constraints, the objective has been to minimize wire length and possibly via (edges in z-direction) count, and timing constraints on the chip were only modeled indirectly. We present a new approach, where timing constraints are modeled directly during global routing: In joint work with Stephan Held, Dirk Mueller, Daniel Rotter, Vera Traub and Jens Vygen, we extend the modeling of global routing as a Min-Max Resource Sharing Problem to also incorporate timing constraints. For measuring signal delays we use the well-established Elmore delay model. One of the key subproblems here is the computation of Steiner trees minimizing a weighted sum of routing space usages and signal delays. For k pins, this problem is NP-hard to approximate within o(log k), and even the special case k = 2 is NP-hard, as was shown by Haehnle and Rotter. We present a fast approximation algorithm with strong approximation bounds for the case k = 2. For k > 2 we use a multi-stage approach based on modifying the topology of a short Steiner tree and using our algorithm for the two-pin case for computing new connections. Moreover, we present a layer assignment algorithm that assigns z-coordinates to the edges of a given two-dimensional tree. We also discuss the topic of routing based optimization. Here, the starting point is a complete routing, and timing optimization tools make changes that require incremental adaptations of the underlying routing. We investigate several aspects of this problem and derive a new routing flow that includes our timing-aware global router and routing based optimization steps. We evaluate our results from this thesis in practice on industrial 14nm microprocessor designs from IBM. Our theoretical results are validated in practice by a strong performance of our timing-aware global routing framework and our new routing flow, yielding significant improvements over the traditional global routing method and the previously used routing flow. Therefore, we conclude that our approaches and results from this thesis are not only theoretically sound but also give compelling results in practice

Dynamic Power Management for Neuromorphic Many-Core Systems

This work presents a dynamic power management architecture for neuromorphic many core systems such as SpiNNaker. A fast dynamic voltage and frequency scaling (DVFS) technique is presented which allows the processing elements (PE) to change their supply voltage and clock frequency individually and autonomously within less than 100 ns. This is employed by the neuromorphic simulation software flow, which defines the performance level (PL) of the PE based on the actual workload within each simulation cycle. A test chip in 28 nm SLP CMOS technology has been implemented. It includes 4 PEs which can be scaled from 0.7 V to 1.0 V with frequencies from 125 MHz to 500 MHz at three distinct PLs. By measurement of three neuromorphic benchmarks it is shown that the total PE power consumption can be reduced by 75%, with 80% baseline power reduction and a 50% reduction of energy per neuron and synapse computation, all while maintaining temporary peak system performance to achieve biological real-time operation of the system. A numerical model of this power management model is derived which allows DVFS architecture exploration for neuromorphics. The proposed technique is to be used for the second generation SpiNNaker neuromorphic many core system

Improved Physical Design for Manufacturing Awareness and Advanced VLSI

Increasing challenges arise with each new semiconductor technology node, especially in advanced nodes, where the industry tries to extract every ounce of benefit as it approaches the limits of physics, through manufacturing-aware design technology co-optimization and design-based equivalent scaling. The increasing complexity of design and process technologies, and ever-more complex design rules, also become hurdles for academic researchers, separating academic researchers from the most up-to-date technical issues.This thesis presents innovative methodologies and optimizations to address the above challenges. There are three directions in this thesis: (i) manufacturing-aware design technology co-optimization; (ii) advanced node design-based equivalent scaling; and (iii) an open source academic detailed routing flow.To realize manufacturing-aware design technology co-optimization, this thesis presents two works: (i) a multi-row detailed placement optimization for neighbor diffusion effect mitigation between neighboring standard cells; and (ii) a post-routing optimization to generate 2D block mask layout for dummy segment removal in self-aligned multiple patterning.To achieve advanced node design-based equivalent scaling, this thesis presents two improved physical design methodologies: (i) a post-placement flop tray generation approach for clock power reduction; and (ii) a detailed placement approach to exploit inter-row M1 routing for congestion and wirelength reduction.To address the increasing gap between academia and industry, this thesis presents two works toward an open source academic detailed routing flow: (i) a complete, robust, scalable and design ruleaware dynamic programming-based pin access analysis framework; and (ii) TritonRoute – the open source detailed router that is capable of delivering DRC-clean detailed routing solutions in advanced nodes.This thesis concludes with a summary of its contributions and open directions for future research