Department of Nanophotonics, Integration, and Neural Technology (NINT)
Advances in silicon (Si) photonics have led to an unprecedented scale of photonic integration and a rapidly maturing foundry ecosystem for mass manufacturing on large (200- and 300-mm) wafers. While today’s Si photonics are focused on 1310/1550nm (O- and C-band) data/telecommunications wavelengths, achieving similar levels of photonic integration at shorter, submicrometer wavelengths (visible and near-infrared, λ < 1000nm) remains an open challenge.
Extending the wavelength range of Si photonics holds the potential to unlock new applications – enabling transformative microsystems solutions for display (miniaturized light engines for augmented/virtual reality), neurotechnologies (implantable microchip-based optical tools for neuroscientists), biosensing (compact sensors for health monitoring, biomarker detection), quantum information (scalable optical addressing systems for diamond NV centers, trapped ions/neutral atoms), and new data communication approaches. Realizing this potential will require the development of short-wavelength integrated photonics platforms with both active functionalities (optical modulation, photodetection, and light generation) and advanced passive functionalities (e.g., low-loss waveguides and fiber-to-chip couplers, high-performance wavelength (de)multiplexers and optical filters, and beam shaping devices).
Our research program aims to realize advanced and mass-manufacturable Si photonics for submicrometer wavelengths (λ=400-1000nm). Among the applications being investigated, a central theme of our research is the development of implantable Si-photonics-enabled tools for neuroscientists. These tools integrate multiple on-chip functions: visible-spectrum nanophotonic circuitry for photostimulation, microelectrodes for recording of brain activity, and microfluidics for chemical delivery. Using wafer-scale foundry manufacturing, we aim to disseminate this technology within the neuroscience community – toward new investigations and understanding of neural circuits.
