Possible acception criteria for structure functions

Szalai, Albin; Székely, V.

doi:10.1016/j.mejo.2011.08.010

Cited by 17 publications

(4 citation statements)

References 4 publications

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“…This has the effect of smoothing the poles, making them finite as described in [17]. The smaller δ is, the more accurate the calculations become.…”

Section: Inverse Calculationsmentioning

confidence: 99%

“…In effect, calculating theoretical thermal impedances with very high accuracy becomes computationally demanding, requiring calculation times of up to several minutes on an ordinary desktop computer. In the literature, it is stated that the angle, δ, should not exceed 5° [17]. Typically, the time constant spectrum is sampled at approximately 30 points per decade [18,19].…”

Section: Inverse Calculationsmentioning

confidence: 99%

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Optimization-Based Network Identification for Thermal Transient Measurements

2021

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Network identification by deconvolution is a proven method for determining the thermal structure function of a given device. The method allows to derive the thermal capacitances as well as the resistances of a one-dimensional thermal path from the thermal step response of the device. However, the results of this method are significantly affected by noise in the measured data, which is unavoidable to a certain extent. In this paper, a post-processing procedure for network identification from thermal transient measurements is presented. This so-called optimization-based network identification provides a much more accurate and robust result compared to approaches using Fourier or Bayesian deconvolution in combination with Foster-to-Cauer transformation. The thermal structure function obtained from network identification by deconvolution is improved by repeatedly solving the inverse problem in a multi-dimensional optimization process. The result is a non-diverging thermal structure function, which agrees well with the measured thermal impedance. In addition, the associated time constant spectrum can be calculated very accurately. This work shows the potential of inverse optimization approaches for network identification.

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“…This has the effect of smoothing the poles, making them finite as described in [17]. The smaller δ is, the more accurate the calculations become.…”

Section: Inverse Calculationsmentioning

confidence: 99%

Section: Inverse Calculationsmentioning

confidence: 99%

Optimization-Based Network Identification for Thermal Transient Measurements

2021

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“…As it was mentioned in the beginning of this section the concept of extracting information from thermal transients (heating or cooling curves) was already introduced (14) R thja = T j P years ago and was described in a number of publications Kohári et al 2007;Bognár 2005;Székely and Bien 1988;JEDEC 2005;Székely 1998;Rencz and Székely 2002;Lasance and Poppe 2014;Rencz et al 2005;Szalai and Székely 2011), therefore, in this paper only a brief outline of that procedure is given.…”

Section: Measurementmentioning

confidence: 99%

Modelling of the flow-rate dependent partial thermal resistance of integrated microscale cooling structures

2016

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path(s) with the smallest thermal resistance. In the case of packaged semiconductor devices, the dissipated energy is mainly conducted through the semiconductor die towards the backside of the die, then through several TIM (thermal interface material) layers to the passive or active cooling device (e.g.: heat sinks, heat-pipes, etc.). In order to ensure the lowest thermal resistance path the number of thermal interfaces (material transitions, mating assembly surfaces) has to be minimized. In System-in-Package (SiP) or System-on-Package (SoP) (Tummala 2007) structures the different dies are stacked on each other forming a real 3D structure. Between the dies high resistivity layers (made of some kind of glass) are formed to ensure electrical insulation. These layers also have high thermal resistivity (not to mention the thermal interfaces formed at the sides of these interposer layers), therefore the temperature elevation is higher as if the whole 3D structure was made of silicon.One way to decrease the overall thermal resistance of such a system is to bring the cooling structures closer to the dissipation sources (from now on simply referred to as junction). The thermally optimal solution is to integrate the cooling solutions into the 3D stack. Such integrated cooling solutions include integrated micro-refrigerators structures attached close to the hot-spot locations. In these applications high heat-fluxes (up to a few 100 W/cm 2 ) can be achieved with localized cooling (Jeffrey 2006). Papers Tang et al. (2010); Sabry (2011) present the method and the results of forming a microscale channel structure with circulating fluid inside. Other papers, Prechtl and Kurtz (2004) and Vladimirova (2011) describe microscale channel structure inside the die itself.Though these papers present promising new integrated cooling techniques but they do not mention how the physical realization is optimized to achieve the best possible heat transfer and they do not address how the design of these Abstract As microscale cooling structures are integral parts of modern chip level cooling concepts, the understanding the behaviour of different assemblies is desirable. This way novel co-design concepts can be created where the conventional IC design steps are extended with thermal design capabilities resulting in a SiP/SoP design flow. The first step in this process is to create a general formula which describes the heat transfer mechanism and can be implemented in IC CAD environments as a compact model. This paper presents an analytical study of an integrated microscale channel based cooling structure with the above motivation in mind. A closed analytical formula is given to calculate the partial thermal resistance corresponding to the heat transfer represented by the coolant which can be used in electro-thermal co-simulation. The analyses based on numerical CFD simulations and thermal transient characterizations of a realized structure are also discussed and the results are compared against the analytical ones.

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“…However, the question remains as to which the most suitable procedure is. Responding to these challenges, a method for verifying the accuracy of implementations was presented by Szalai and Székely [11]. While this procedure allows to evaluate the general suitability of an implementation, an absolute measure of accuracy is needed for a fair comparison between the different approaches.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Performance Comparison of Thermal Structure Function Computations

2021

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The determination of thermal structure functions from transient thermal measurements using network identification by deconvolution is a delicate process as it is sensitive to noise in the measured data. Great care must be taken not only during the measurement process but also to ensure a stable implementation of the algorithm. In this paper, a method is presented that quantifies the absolute accuracy of network identification on the basis of different test structures. For this purpose, three measures of accuracy are defined. By these metrics, several variants of network identification are optimized and compared against each other. Performance in the presence of noise is analyzed by adding Gaussian noise to the input data. In the cases tested, the use of a Bayesian deconvolution provided the best results.

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Possible acception criteria for structure functions

Cited by 17 publications

References 4 publications

Optimization-Based Network Identification for Thermal Transient Measurements

Optimization-Based Network Identification for Thermal Transient Measurements

Modelling of the flow-rate dependent partial thermal resistance of integrated microscale cooling structures

Quantitative Performance Comparison of Thermal Structure Function Computations

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