Description
The low-power, high-integrability and ultrabroadband operation of vertical-cavity surface-emitting lasers (VCSELs) motivates their dominance of the automotive and smartphone semiconductor light source mass markets as key elements for several breakthrough applications such as LiDARs, 3D cameras, and intradatacenter optical transceivers. Having the final goal of supporting the R&D departments of VCSEL industrial key players, this activity is focused on the development of a portfolio of simulation tools, including device-level multiphysics models, advanced rate equation descriptions, and experiment-driven behavioural models.
At device level, the activity regards the development of in-house electro-opto-thermal numerical simulators, including 3D vectorial mode solvers, stimulated and spontaneous emission models for quantum well active regions, a heat conduction simulator and a drift-diffusion carrier transport model augmented with quantum corrections and with photon rate equations to allow for coupling with the optical model.
Posing the attention to VCSEL dynamics, the activity is focused on the implementation of models based on coupled nonlinear equations describing the evolution of the modal electric field amplitudes and carrier densities in a VCSEL. Such model allows to simulate coherent effects arising from the competition of the transverse modes, and to quantify their impact to high data rate optical communications in terms of standard experimentally-accessible figures of merit such as relative intensity noise (RIN) spectra, intensity modulation (IM) response, and eye diagram.
For what concerns behavioural modeling, the activity involves the development of a machine learning-based technique aimed at extracting the parameters of rate equation models from experimental results and/or complex models as the previous ones, allowing to synthesize digital twins of the VCSEL to be employed in telecommunication-system-level simulations.
ERC sectors
- PE7_3 Simulation engineering and modelling
- PE7_5 (Micro- and nano-) electronic, optoelectronic and photonic components
- PE7_6 Communication systems, wireless technology, high-frequency technology
- PE6_11 Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)