Wednesday, 1 June 2016: 17:00
Aqua 303 (Hilton San Diego Bayfront)
J. D. B. Bradley (Massachusetts Institute of Technology, McMaster University), P. Purnawirman (Massachusetts Institute of Technology), E. Shah Hosseini (Massachusetts Institute of Technology, Analog Photonics), Z. Su, E. S. Magden (Massachusetts Institute of Technology), N. Li (Massachusetts Institute of Technology, Harvard University), G. Singh, J. Sun (Massachusetts Institute of Technology), T. N. Adam, G. Leake, D. Coolbaugh (University at Albany), and M. R. Watts (Massachusetts Institute of Technology)
Erbium-doped lasers provide high power, narrow linewidth and thermally stable emission in the important 1.5-µm eye-safe and telecommunications band. By integrating such lasers on a chip we can realize cost-effective, compact and highly robust devices compared to fiber-based platforms. Furthermore, using a silicon-compatible fabrication approach allows for co-integration with silicon electronic/photonic devices and will open new applications for compact microsystems.
The presentation will cover our work on silicon-based erbium-doped aluminum oxide lasers (Al2O3:Er3+). Al2O3:Er3+ has recently received significant attention because of its broad emission, reduced clustering, and higher index, thus potential for more compact devices, compared to Er-doped silica. This has led to numerous demonstrations of amplification and lasing on chips using Al2O3:Er3+ as monolithic gain medium. As an important step towards silicon compatibility and exploiting wafer-scale lithography methods for high resolution cavity features, we have developed a silicon nitride-based Al2O3:Er3+ platform. Using such an approach, we have demonstrated a number of on-chip lasers, including distributed feedback, distributed Bragg reflector and microcavity devices. The talk will cover critical materials and design considerations towards realizing high performance lasers, including the influence of ion-ion clustering in the Al2O3 host, nanoscale film-thickness non-uniformities, and cavity Q factor optimization.
