Numerical optimization-based synthesis of pipelined A/D converters

Carley, L.R.; Fung, P.K.; Donehue, P.; Biyabani, A. Ahmed

doi:10.1109/iscas.1992.230566

Cited by 4 publications

(6 citation statements)

References 7 publications

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“…In this situation, the optimal design SNDR decreases by approximately 3 dB in absence of noise (82.15 dB) and only 1.3 dB (79.83 dB) in presence of noise, with respect to Table 5. This shows that unreliable back-end LSBs have a limited influence on the final design, if thermal noise is the limiting factor, as in this case, in full compliance with system constraints imposed through (12).…”

Section: Resultsmentioning

confidence: 93%

“…The formulation (12) and performance models generate a non-linear, non-convex optimization problem that was solved using a simulated annealing engine [29], modified to handle performance model constraints efficiently. The optimization problem (12) can be easily modified if some performance margins are required to improve the robustness of the solution.…”

Section: Optimizationmentioning

confidence: 99%

“…The optimization problem (12) can be easily modified if some performance margins are required to improve the robustness of the solution. The generality of the formulation allows arbitrary cost functions and constraints so many existing approaches to system-level robust design may be adopted [30].…”

Section: Optimizationmentioning

confidence: 99%

“…Furthermore, digital calibration techniques improve overall flexibility and design yield. Compared to traditional pipelined ADC optimization approaches [8][9][10][11][12][13], which mainly focus on analytical models used to determine performance of single stage blocks, we exploit accurate simulation-based performance models that consider important effects as Sample-and-Hold Amplifier (SHA) distortion, charge injection and, finally, the impact of digital calibration. This approach allows avoiding expensive design iterations caused by non feasibility of performance requirements at the bottom level of abstraction resulting from the inaccurate architectural modeling used in standard top-down flows.…”

Section: Introductionmentioning

confidence: 99%

“…Columns report optimizations performed with: 1 , no GDEC and relaxed feasibility constraints; 2 , GDEC included and constraints as in(12). The last column reports the reference design performances…”

mentioning

confidence: 99%

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Platform-based mixed signal design: Optimizing a high-performance pipelined ADC

Nuzzo

Bernardinis

Vincentelli

2006

Analog Integr Circ Sig Process

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We apply Platform-Based Design (PBD) to the power optimization of a 14 bit, 80 MS/s pipelined Analogto-Digital Converter (ADC) in a mixed signal formulation. A platform is a library of components and interconnects, each characterized with a set of behavioral, performance and composition models, that is used to raise the level of abstraction to enable system-level design. PBD is a meetin-the-middle methodology that consists of two phases. The bottom-up phase generates a set of platform libraries that are exploited in the system hierarchy. The top-down phase allows exploring feasible solutions within the platform libraries and selecting the optimum implementation. To evaluate the cost of each implementation, the behavioral models available through platform abstraction are used both for digital and analog components. We provide an example of the use of the methodology and its features for analog circuits by modeling two amplifiers with different topologies as analog components, showing details of the analog characterization process. Then, we create a mixed signal platform library as a combination of an analog and a digital platform (bottom-up phase). The top-down phase performs optimization across the analog/digital boundary to minimize power consump-

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Section: Resultsmentioning

confidence: 93%