Computationally efficient multiphysics electro-opto-thermal drift-diffusion models for short pulse and continuous wave excitation applied to MUTC photodetectors
We present two models for analyzing the dynamics of photodetectors. One model applies when the photodetector is illuminated by a train of optical pulses whose duration is short compared to the impulse response; the other model applies when the optical excitation is effectively continuous. The first major contribution of this work is the identification of a quasiadiabatic heating regime governing the device response to subpicosecond to picosecond-scale optical pulse excitation. We demonstrate theoretically that due to the finite optical-to-acoustic phonon relaxation time and the minimal thermal diffusion length during the pulse, lattice heating occurs on a timescale that is significantly faster than macroscopic heat transport. Hence, the heat equation can be mathematically decoupled into a local, diffusionless energy deposition phase and a subsequent diffusive cooling phase, enabling the computationally efficient study of long pulse trains without sacrificing accuracy. The second major contribution of this work is the introduction of a quasi-one-dimensional electro-thermal drift-diffusion model derived by exploiting the symmetry and Gaussian distribution of the laser beam's optical mode. By evaluating the radial diffusion operator exactly at the optical axis, we transform the three-dimensional lateral heat spreading into a linear relaxation term, enabling accurate simulation of the device geometry without the computational cost of a multidimensional mesh. Both models are then applied to high speed modified uni-traveling-carrier (MUTC) photodetectors to quantify self-heating dynamics and the resulting impact on quantum efficiency.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new an...
Marko Ikonen, Petri Kettunen, Nilay V. Oza et al.· EUROMICRO Conference on Soft...· 67 citations· ⚡9
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduOct 15, 2026
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Microsoft Research Blog· microsoft.comOct 12, 2026
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.