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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Statistical static timing analysis considering the impact of power supply noise in VLSI circuits

Kim, Hyun Sung 02 June 2009 (has links)
As semiconductor technology is scaled and voltage level is reduced, the impact of the variation in power supply has become very significant in predicting the realistic worst-case delays in integrated circuits. The analysis of power supply noise is inevitable because high correlations exist between supply voltage and delay. Supply noise analysis has often used a vector-based timing analysis approach. Finding a set of test vectors in vector-based approaches, however, is very expensive, particularly during the design phase, and becomes intractable for larger circuits in DSM technology. In this work, two novel vectorless approaches are described such that increases in circuit delay, because of power supply noise, can be efficiently, quickly estimated. Experimental results on ISCAS89 circuits reveal the accuracy and efficiency of my approaches: in s38417 benchmark circuits, errors on circuit delay distributions are less than 2%, and both of my approaches are 67 times faster than the traditional vector-based approach. Also, the results show the importance of considering care-bits, which sensitize the longest paths during the power supply noise analysis.
2

Implement Low Power IC Design with Statistical Static Timing Analysis in 90nm CMOS Technology

Ou, Yu-Hao 15 February 2011 (has links)
As the mobile electronic products development are more and more popular such as mobile phone, digital camera, PDA¡Ketc. Each of company releases variable kind of mobile products, and every portable machine has plenty of functions. A low power consumption design is a significant issue which academics and engineers concern. It would be a major progress if the approach which can drop off the power consumption successfully. The mobile electronic products have more application programs than before and the size of LCD increases continuously, so that the power consumption becomes large. Therefore, expanding the life of battery would be a significant issue. Besides, the process technology has improved day by day, and it would influence the supply voltage be declined. It represents the power management would influence the power consumption of circuit directly. Comparing to drop down the entire IC power consumption and not to influence the performance of IC, the thesis employs the algorithm that searches the Critical Path and embeds the Level Converter Logic into digital circuit. It can offer the proper supply voltage to circuits which do not want to bigger supply voltage for reduce power consumption. However, the process variation (Inter-Die or Intra-Die) may transform the original Critical Path, the Critical Path which searches through the static timing analysis would not correct. To conquer this problem, the thesis provides the statistical approach to analysis timing. It would search Path Sensitivity which is exactly equal to the probability that a path is critical. Finally, the logic gate which is designed by us would replace the UMC 90nm standard cell through Cell-Based.
3

Compact variation-aware standard cells for statistical static timing analysis

Aftabjahani, Seyed-Abdollah 09 June 2011 (has links)
This dissertation reports on a new methodology to characterize and simulate a standard cell library to be used for statistical static timing analysis. A compact variation-aware timing model for a standard cell in a cell library has been developed. The model incorporates variations in the input waveform and loading, process parameters, and the environment into the cell timing model. Principal component analysis (PCA) has been used to form a compact model of a set of waveforms impacted by these sources of variation. Cell characterization involves determining equations describing how waveforms are transformed by a cell as a function of the input waveforms, process parameters, and the environment. Different versions of factorial designs and Latin hypercube sampling have been explored to model cells, and their complexity and accuracy have been compared. The models have been evaluated by calculating the delay of paths. The results demonstrate improved accuracy in comparison with table-based static timing analysis at comparable computational cost. Our methodology has been expanded to adapt to interconnect dominant circuits by including a resistive-capacitive load model. The results show the feasibility of using the new load model in our methodology. We have explored comprehensive accuracy improvement methods to tune the methodology for the best possible results. The following is a summary of the main contributions of this work to the statistical static timing analysis: (a) accurate waveform modeling for standard cells using statistical waveform models based on principal components; (b) compact performance modeling of standard cells using experimental design statistical techniques; and (c) variation-aware performance modeling of standard cells considering the effect of variation parameters on performance, where variation parameters include loading, waveform shape, process parameters (gate length and threshold voltage of NMOS and PMOS transistors), and environmental parameters (supply voltage and temperature); and (f) extending our methodology to support resistive-capacitive loads to be applicable to interconnect dominant circuits; and (e) classifying the sources of error for our variational waveform model and cell models and introducing of the related accuracy improvement methods; and (f) introducing our fast block-based variation-aware statistical dynamic timing analysis framework and showing that (i) using compiler-compiler techniques, we can generate our timing models, test benches, and data analysis for each circuit, which are compiled to machine-code to reduce the overhead of dynamic timing simulation, and (ii) using the simulation engine, we can perform statistical timing analysis to measure the performance distribution of a circuit using a high-level model for gate delay changes, which can be linked to their parameter variation.
4

Random Local Delay Variability : On-chip Measurement And Modeling

Das, Bishnu Prasad 06 1900 (has links)
This thesis focuses on random local delay variability measurement and its modeling. It explains a circuit technique to measure the individual logic gate delay in silicon to study within-die variation. It also suggests a Process, Voltage and Temperature (PVT)-aware gate delay model for voltage and temperature scalable linear Statistical Static Timing Analysis (SSTA). Technology scaling allows packing billions of transistors inside a single chip. However, it is difficult to fabricate very small transistor with deterministic characteristic which leads to variations. Transistor level random local variations are growing rapidly in each technology generation. However, there is requirement of quantification of variation in silicon. We propose an all-digital circuit technique to measure the on-chip delay of an individual logic gate (both inverting and non-inverting) in its unmodified form based on a reconfigurable ring oscillator structure. A test chip is fabricated in 65nm technology node to show the feasibility of the technique. Delay measurements of different nominally identical inverters in close physical proximity show variations of up to 28% indicating the large impact of local variations. The huge random delay variation in silicon motivates the inclusion of random local process parameters in delay model. In today’s low power design with multiple supply domain leads to non-uniform supply profile. The switching activity across the chip is not uniform which leads to variation of temperature. Accurate timing prediction motivates the necessity of Process, Voltage and Temperature (PVT) aware delay model. We use neural networks, which are well known for their ability to approximate any arbitrary continuous function. We show how the model can be used to derive sensitivities required for voltage and temperature scalable linear SSTA for an arbitrary voltage and temperature point. Using the voltage and temperature scalable linear SSTA on ISCAS 85 benchmark shows promising results with average error in mean delay is less than 1.08% and average error in standard deviation is less than 2.65% and errors in predicting the 99% and 1% probability point are 1.31% and 1% respectively with respect to SPICE.
5

Modeling and Analysis of Large-Scale On-Chip Interconnects

Feng, Zhuo 2009 December 1900 (has links)
As IC technologies scale to the nanometer regime, efficient and accurate modeling and analysis of VLSI systems with billions of transistors and interconnects becomes increasingly critical and difficult. VLSI systems impacted by the increasingly high dimensional process-voltage-temperature (PVT) variations demand much more modeling and analysis efforts than ever before, while the analysis of large scale on-chip interconnects that requires solving tens of millions of unknowns imposes great challenges in computer aided design areas. This dissertation presents new methodologies for addressing the above two important challenging issues for large scale on-chip interconnect modeling and analysis: In the past, the standard statistical circuit modeling techniques usually employ principal component analysis (PCA) and its variants to reduce the parameter dimensionality. Although widely adopted, these techniques can be very limited since parameter dimension reduction is achieved by merely considering the statistical distributions of the controlling parameters but neglecting the important correspondence between these parameters and the circuit performances (responses) under modeling. This dissertation presents a variety of performance-oriented parameter dimension reduction methods that can lead to more than one order of magnitude parameter reduction for a variety of VLSI circuit modeling and analysis problems. The sheer size of present day power/ground distribution networks makes their analysis and verification tasks extremely runtime and memory inefficient, and at the same time, limits the extent to which these networks can be optimized. Given today?s commodity graphics processing units (GPUs) that can deliver more than 500 GFlops (Flops: floating point operations per second). computing power and 100GB/s memory bandwidth, which are more than 10X greater than offered by modern day general-purpose quad-core microprocessors, it is very desirable to convert the impressive GPU computing power to usable design automation tools for VLSI verification. In this dissertation, for the first time, we show how to exploit recent massively parallel single-instruction multiple-thread (SIMT) based graphics processing unit (GPU) platforms to tackle power grid analysis with very promising performance. Our GPU based network analyzer is capable of solving tens of millions of power grid nodes in just a few seconds. Additionally, with the above GPU based simulation framework, more challenging three-dimensional full-chip thermal analysis can be solved in a much more efficient way than ever before.

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