ability of bonded laser assemblies to pass early reliability tests.22:T4e4,Ring modulators for silicon photonic (SiPho) optical transceivers are extremely thermally sensitive and require thermal tuning for stable operation. This tuning is achieved with integrated heaters and in this work two aspects of the heaters are investigated: firstly, how to thermally model the heater-waveguide system in a SiPho ring modulator. Secondly, how to improve the energy efficiency by adapting the design in order to minimize the thermal tuning power consumed by the heater. Silicon substrate undercut (UCUT) is done in order to achieve out-of-plane thermal isolation and prevent heat flow directly into the Si substrate below the device. Introducing the UCUT increases the heater efficiency (

$\eta _{h}$

) with a factor X3.08. It is shown that the influence of the UCUT cross section on the heater efficiency can be neglected, only the total area and metals significantly influence the thermal behaviour. Comparison between electro-thermal and pure thermal simulation shows very similar behaviour. Finally, the UCUT causes an increase in thermal time constant with factor X3.68.23:T4be,Thermal modeling of photonic integrated circuits (PICs) is required for circuit design and the development of thermal control algorithms. In this work, we present a methodology for obtaining a compact dynamic thermal

$RC$

model of a PIC. PICs are sensitive to thermal coupling, and in this article, it is shown that traditional resistive coupling does not suffice, and a new coupling method is derived. The case study to which the new methodology is applied is a dense wavelength-division multiplexing ring filter with eight channels. With the obtained

$RC$

model, the computational time is reduced from 5.5 h for finite element simulation to < 1 s for a single-step response. Using the compact model, circuit-level analysis of frequency response is carried out. Because of its high computational efficiency, the compact model opens possibilities for simulating thermal tuning algorithms.24:T46f,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 article, a performance benchmarking between three ML methods for thermal simulation is carried out: 1) artificial neural networks (ANNs); 2) proper orthogonal decomposition with radial basis functions (POD-RBFs); and 3) 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 nonlinear dynamics and are tested for inter- and extra-polation prediction accuracy. A computational speed increase with a factor >7500 compared to FE is obtained. Furthermore, ANNs prove to be the best suited for the simulation of nonlinear dynamics. POD-RBF is the best method for minimizing training time and combining the best of both methods in POD-RBF-ANN creates an ML model with a short training phase and highly accurate predictions.

#	Concept	H-Index	Publications	Citations
1	Engineering	4	4	128
2	Applied Physics	4	4	128
3	Electrical Engineering	3	3	76
4	Physics	2	2	56
5	Computer Engineering	1	1	20

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