Fabrication of test structures for 4×4 InP/InGaAsP integrated optical matrix

A novel optical switching matrix measuring 1×2 mm2 in size is fabricated. The switching matrix is composed of waveguides, four 1×4 multimode interference (MMI) splitters, 32 total internal refraction mirrors and four 4×1 MMI combiners with the extremely compact size of 1×2 mm2. This integrated device are assessed and loss contribution measured from test structure is presented.

Three band-gap QW intermixing in InP/InGaAs/InGaAsP system for monolithically integrated optical switch

Quantum well intermixing is a key technique for photonic integration. The intermixing of InP/InGaAs/InGaAsP material involving the deposition of a layer of sputtered SiO2 on the semiconductor surface, followed by thermal annealing has allowed good control of the intermixing process and has been used to fabricate extended cavity lasers. This will be used for optimization of the performance of optical switches consisting of passive components, modulators and amplifiers.

Process development on the monolithic fabrication of an ultra-compact 4×4 optical switch matrix on InP/InGaAsP material

We have fabricated an ultra-compact 4×4 optical matrix on InP/InGaAsP material. 1×4 MMI couplers and TIR mirrors are employed to produce a compact 1×2 mm2 device. A CH4/H2/O2 RIE dry etch process has been used to realize two-level dry etching: deep-etch for both the MMI couplers and the mirrors and shallow-etch for the rest of the routing waveguides. It was found that a metal/dielectric bilayer mask is essential for multi-dry-etch processes and high profile verticality. We have found a Ti intermediate mask for the deep-etch process which is removable by SF6 dry-etch before the following shallow process. Dry-etch removal of the intermediate mask is necessary to protect the deep-etched mirror sidewall.

Scaling the Indian Buffet Process via Submodular Maximization

Inference for latent feature models is inherently difficult as the inference space grows exponentially with the size of the input data and number of latent features. In this work, we use Kurihara & Welling (2008)'s maximization-expectation framework to perform approximate MAP inference for linear-Gaussian latent feature models with an Indian Buffet Process (IBP) prior. This formulation yields a submodular function of the features that corresponds to a lower bound on the model evidence. By adding a constant to this function, we obtain a nonnegative submodular function that can be maximized via a greedy algorithm that obtains at least a one-third approximation to the optimal solution. Our inference method scales linearly with the size of the input data, and we show the efficacy of our method on the largest datasets currently analyzed using an IBP model.