Rigorous statistical process variation analysis for quarter-μm CMOS with advanced TCAD metrology

Aoyama, K.; Kunitomo, H.; Tsuneno, K.; Sato, Hideo; Mori, Kohsuke; Masuda, H.

doi:10.1109/iwstm.1997.629401

Cited by 6 publications

(2 citation statements)

References 3 publications

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“…Means and variances of device parameters can be approximated in this method. This technique though falls short of being sufficiently accurate in deep sub-micron and sub-wavelength technologies due to the Gaussian distribution assumption attributed to device parameters, as device parameters are sharply deviating from Gaussian distributions with newer technologies, as can be seen in [10], [11] and [12]. Inaccurate information regarding the distribution of device parameters provided to the designers may cause a major bottleneck in the design cycle increasing or elongating iterations.…”

Section: Introductionmentioning

confidence: 99%

Forward discrete probability propagation method for device performance characterization under process variations

Topaloglu

Orailoğlu

2005

Proceedings of the 2005 Conference on Asia South Pacific Design Automation - ASP-DAC '05

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Process variations are becoming influential at the device level in deep sub-micron and sub-wavelength design regimes, whereas they used to be a few generations away only influential at circuit level. Process variations cause device performance parameters, such as current or output resistance, to acquire a probability distribution. Estimation of these distributions has been accomplished using Monte Carlo techniques so far. The large number of samples needed by Monte Carlo methods adversely affects the possibility of integrating probabilistic device performance at the circuit level due to run-time inefficiency. In this paper, we introduce a novel technique called Forward Discrete Probability Propagation (FDPP). This method discretizes the probability distributions and effectively propagates these probabilities across a device formula hierarchy, such as the one present in the SPICE3v3 model. Consequently, probability distributions for process parameters are propagated to the device level. It is shown in the paper that with far fewer number of samples, comparable accuracy to a Monte Carlo method is achieved. I. INTRODUCTIONEstimation of the effects of process variations on device performance has long been a concern. The computational complexity of current simulators precludes incorporation of process variations to device performance. This can be attributed to the lack of accurate methods and models for process variations. Designers have been trying to cope with this absence through worstcase analysis, Monte Carlo techniques or through the invocation of Gaussian distribution assumptions. But these approaches can no longer be counted upon to provide sufficiently accurate and fast results, as deep sub-micron silicon technologies rapidly push manufacturers to device parameter characterizations of increased accuracy in order to obviate the increasing number of design iterations.The effects of process variations on device parameters further indicate that the relationships between factors causing process variations and device parameters are deviating from a linear approximation even for a small input domain. This implies that the Gaussian distribution assumption attributed to device performance parameters is no longer accurate. Therefore, a more accurate methodology is necessary to estimate the effects of mismatch on high-level parameters.The paper presents a methodology to deterministically estimate the results of process variations on device parameters

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Section: Introductionmentioning

confidence: 99%

Forward discrete probability propagation method for device performance characterization under process variations

Topaloglu

Orailoğlu

2005

Proceedings of the 2005 Conference on Asia South Pacific Design Automation - ASP-DAC '05

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“…Statistical simulation and analysis is potentially a very important tool in process development and yield improvement [2]. By using TCAD tools combined with reliable calibration, we can simulate individual transistors and predict their performance before manufacturing them.…”

Section: Introductionmentioning

confidence: 99%

Statistical simulation, calibration and analysis of 0.25 CMOS technology

Dong¹,

Belova²,

Aronowitz³

2000 5th International Workshop on Statistical Metrology (Cat.No.00TH8489

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A statistical simulation flow for the characterization and analysis of 0.25 CMOS technology is presented. The simulation tools PDFAB[I],Tsugrem4 and Medici were used in this work The simulation tools were calibrated to the Etest data and their capability to predict were verified. The statistical input parameters were collected from actual manufacturing distributions. Simulation results showed very good agreement with the manufacturing data.

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Forward discrete probability propagation method for device performance characterization under process variations

Topaloglu¹,

Orailoglu²

Proceedings of the ASP-DAC 2005. Asia and South Pacific Design Automation Conference, 2005.

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Rigorous statistical process variation analysis for quarter-μm CMOS with advanced TCAD metrology

Cited by 6 publications

References 3 publications

Forward discrete probability propagation method for device performance characterization under process variations

Forward discrete probability propagation method for device performance characterization under process variations

Statistical simulation, calibration and analysis of 0.25 CMOS technology

Forward discrete probability propagation method for device performance characterization under process variations

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