Thermal Aspects of Silicon Photonic Interposer Packages

Oprins, Herman; Ban, Yoojin; Cherman, Vladimir; Campenhout, Joris Van

doi:10.1109/eurosime48426.2020.9152630

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…This metric is used to compare the simulation with the measurement result of the heater efficiency. The thermal boundary conditions are natural convection (h=20 W/m2-K) on the top-facing surfaces, adiabatic sidewalls, and a thermal resistance (Rth=8 K/W 15 ) on the bottom face of the PIC, representing the PCB below the die. The material properties used in the simulations are summarized in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Thermal modeling of hybrid three-dimensional integrated, ring-based silicon photonic–electronic transceivers

Coenen,

Kim,

Oprins

et al. 2023

J. Optical Microsystems

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Co-packaged optics for high performance computing or other data center applications requires dense integration of silicon photonic integrated circuits (PICs) with electronic integrated circuits (EICs). This work discusses the impact of three-dimensional (3D) hybrid integration on the thermal performance of Si ring-based photonic devices in wavelength-division multiplexing PICs. A thermal finite element model of the EIC-PIC assembly is developed and calibrated with thermo-optic device measurements, before and after integration of an electrical driver on top of the PIC by means of microbump flip-chip bonding. Both measurements and simulations of the thermal tuning efficiency and crosstalk between silicon photonic devices show that the EIC can have a significant impact on the thermal performance of the integrated heaters in the PIC by acting as an undesired heat spreader. This heat spreading lowers the heater efficiency with 43.3% and increases the thermal crosstalk between the devices by up to 44.4% compared with a PIC-only case. Finally, it is shown that these negative thermal effects of 3D integration can largely be mitigated by a thermally aware design of the microbump array and the back-end-of-line interconnect, guided by the calibrated thermal simulation model.

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Section: Methodsmentioning

confidence: 99%

Thermal modeling of hybrid three-dimensional integrated, ring-based silicon photonic–electronic transceivers

Coenen,

Kim,

Oprins

et al. 2023

J. Optical Microsystems

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show abstract

“…T HE increasing complexity and size scaling in today's nanoelectronics and -photonics comes with the need for multiscale analysis from nanometer to centimeter scales [1] and temporal analysis from nanosecond to second scales. Widely used modelling tools such as finite element models for thermal simulation [2] are lacking in this regard as the number of required elements and hence the computational time rapidly increases for capturing details at the smallest scales [3]. In order to solve this problem, there have been many efforts in recent studies to make the simulation process more efficient [4].…”

Section: Introductionmentioning

confidence: 99%

Benchmarking of Machine Learning Methods for Multiscale Thermal Simulation of Integrated Circuits

Coenen

Oprins

Degraeve

et al. 2023

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Multiscale thermal analysis in integrated systems is required for capturing both device-level and circuit-level dynamics. Traditional analysis with finite element (FE) models can be accelerated by using machine learning (ML) methods. In this paper a performance benchmarking between three ML methods for thermal simulation is carried out: Artificial Neural Networks (ANNs), Proper Orthogonal Decomposition with Radial Basis Functions (POD-RBF) and finally POD-RBF-ANN is used as a hybrid ML method. The (dis)advantages of the different methods are demonstrated for the thermal simulation of a multiscale photonic chip. The ML models are trained with FE data for both linear and non-linear dynamics and are tested for inter-and extrapolation prediction accuracy. A computational speed increase with factor >7500 compared to FE is obtained. Furthermore, ANNs prove to be the best suited for the simulation of non-linear dynamics. POD-RBF is the best method for minimizing training time and combining the best of both methods in POD-RBF-ANN creates a ML model with short training phase and highly accurate predictions.

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Circuit-level Thermal Modelling of Silicon Photonic Transceiver Array using Machine Learning

Coenen

Oprins

Wolf

2022

2022 21st IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (iTherm)

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Thermal Aspects of Silicon Photonic Interposer Packages

Cited by 3 publications

References 17 publications

Thermal modeling of hybrid three-dimensional integrated, ring-based silicon photonic–electronic transceivers

Thermal modeling of hybrid three-dimensional integrated, ring-based silicon photonic–electronic transceivers

Benchmarking of Machine Learning Methods for Multiscale Thermal Simulation of Integrated Circuits

Circuit-level Thermal Modelling of Silicon Photonic Transceiver Array using Machine Learning

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