Delphi style compact modeling by means of genetic algorithms of system in Package devices using composite sub-compact thermal models dedicated to model order reduction

Abstract

Nowadays a high-level integration with unprecedented functionality and efficient performances is achieved through 3D packaging techniques known as System-In-Package (SIP). The paper describes the reduction process conducted on a realistic SIP module case in order to establish a behavioral thermal network having a large number of power sources. Besides this device has been slightly modified to focus on the recent 3D integration techniques such as the stacking of chip, multi-chips side by side architecture or the embedding of conventional individually-packaged Integrated Circuits (IC). These works compared the prediction of a DELPHI style Compact Thermal Model (CTM)[1][6] to a numerical Detailed Thermal Model with for aim to illustrate the diminution of computation delays, the expected accuracy and some efficient ways to improve it.Then it describes the performance of a novel methodology that nests a set of Sub-Compact Thermal Models (SCTM) within the detailed numerical model, far less grid-intensive, and its ability to preserve the final SIP CTM quality.

Multi-port dynamic compact thermal models of dual-chip package using model order reduction and metaheuristic optimization

“…For CTMs generation, the Delphi's method has been revised to enhance its relevance and effectiveness. The added steps are discussed in [10,14,23]. As mentioned, the steps for the identification of the thermal capacities are given in [12,13].…”

“…The 49-set boundary conditions applied to the package are meant to reflect all the conditions that a component encounters in typical electronic applications. The values of the heat transfer coefficients vary from 1 to 10 9 W.m −2 .K −1 , and are listed in [23].…”

Investigation of Delphi compact thermal model style for modeling surface-mounted Soft Magnetic Composite inductor

“…• subdivisions of the external surfaces [8]. Figure 5 presents the new style of two-source thermal network which is generated from our reduction process flow.…”

“…The two-sources (S) thermal resistances network, is built from a wide set of DELPHI [4][5][6][7][8] or JEDEC-JESD15-4 [1] boundary conditions (BC), applied successively on all external surfaces of the model, which is completed by the superposition principle; (S number +1) x BC number.…”

“…Then a Genetic Algorithm fitting technique [3] [4][5][6][7][8] allows us to determine the best network candidate that properly matches heat flow rates and temperatures of the realistic model, for all its sensitive parts and its external surfaces.…”