Statistical Thermal Profile Considering Process Variations: Analysis and Applications

Jaffari, Javid; Anis, Mohab

doi:10.1109/tcad.2008.923251

Cited by 34 publications

(25 citation statements)

References 32 publications

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“…The working temperature of CPU is less or equal to 100 C according to the report of Jaffari and Anis [32]. So, it is necessary and important to investigate the stability of TIMs under high temperature in terms of both thermal conductivity and deformation.…”

Section: Thermal Conductivitymentioning

confidence: 99%

Synergic enhancement of thermal properties of polymer composites by graphene foam and carbon black

Zhao

Zhang

et al. 2016

Composites Part B: Engineering

124

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Section: Thermal Conductivitymentioning

confidence: 99%

Synergic enhancement of thermal properties of polymer composites by graphene foam and carbon black

Zhao

Zhang

et al. 2016

Composites Part B: Engineering

124

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“…Therefore, the designer has to deal with the distribution of the temperature rather than a nominal value while addressing the impact of temperature variations on the timing yield. In fact, based on the results discussed in the early version of the thermal analyzer [10], using just the nominal temperature map and ignoring the statistical thermal profile causes an underestimation of the on-die temperature up to 30 1C in 90 nm technology. This temperature difference will be even larger for smaller feature sizes where variations are more significant.…”

Section: Motivation and Workflowmentioning

confidence: 99%

Timing yield analysis considering process-induced temperature and supply voltage variations

Haghdad

Anis

2012

Microelectronics Journal

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“…Its effect becomes more relevant, as V DD scales down. Operationdependent power consumption creates dynamic thermal maps along the die, which also modulate system timing response [10]. Parametric variations tend to increase with device scaling down and with system complexity.…”

Section: Pvt and Aging-induced Variationsmentioning

confidence: 99%

Delay sensing for long-term variations and defects monitoring in safety–critical applications

Vazquez

Champac

Ziesemer

et al. 2011

Analog Integr Circ Sig Process

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The impact of parametric variations on digital circuit performance is increasing in nanometer Integrated Circuits (IC), namely of Process, power supply Voltage and Temperature (PVT) variations. Moreover, circuit aging also impacts circuit performance, especially due to Negative Bias Temperature Instability (NBTI) effect. A growing number of physical defects manifest themselves as delay faults (at production, or during product lifetime). On-chip, on-line delay monitoring, as a circuit failure prediction technique, can be an attractive solution to guarantee correct operation in safety-critical applications. Safe operation can be monitored, by predictive delay fault detection. A delay monitoring methodology and a novel delay sensor (to be selectively inserted in key locations in the design and to be activated according to user's requirements) is proposed, and a 65 nm design is presented. The proposed sensor is programmable, allowing delay monitoring for a wide range of delay values, and has been optimized to exhibit low sensitivity to PVT and aging-induced variations. Two MOSFET models-BPTM and ST-have been used. As abnormal delays can be monitored, regardless of their origin, both parametric variations and physical defects impact on circuit performance can be identified. Simulation results show that the sensor is effective in identifying such abnormal delays, due to NBTI-induced aging and to resistive open defects.

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Statistical Thermal Profile Considering Process Variations: Analysis and Applications

Cited by 34 publications

References 32 publications

Synergic enhancement of thermal properties of polymer composites by graphene foam and carbon black

Synergic enhancement of thermal properties of polymer composites by graphene foam and carbon black

Timing yield analysis considering process-induced temperature and supply voltage variations

Delay sensing for long-term variations and defects monitoring in safety–critical applications

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