Optical interconnect for integrated circuits

This thesis presents the research pertaining to the development of essential components of optical interconnect between dies in a package, involving both guided and free-space propagation of light. In order to pursue such an objective, it required the development of a simpler approach to the design of planar silica lens pairs; develop the technology for fabricating such lens pairs, and modeling the critical factors, like alignment non-idealities, that affect the optical loss of such a scheme involving both guided and free-space propagation. A methodology based on the ABCD matrix method has been developed to design and evaluates the performance of a planar silica lens pair system for a prescribed (‘ideal’) free-space propagation distance. The optical loss of a designed system under various fabrication and experimental imperfections has been calculated and verified against the simulation results obtained from the commercial beam propagation method (BPM) software, BPM_CAD by Optiwave. A two-level optical system comprising of a planar silica lens pair and a pair of 45° micromirror, which is equivalent to a chip to chip optical interconnects in a 3D integrated system, has been theoretically analysed for optical loss due to micromirrors deviation from the ideal 45° and an angular tilt between the two levels. For the implementation of the planar silica lens pair, a hollow cathode PECVD system was used to deposit low stress thick graded index silica film on silicon wafer from a mixture of O2/SiH4/CF4 gases. Technique of depositing low stress thick fluorine doped silica film was developed and films up to 38 µm thickness with very low compressive stress (16 Mpa) were deposited on silicon substrate. Lens front-face curvature was defined by vertical deep oxide etch using a state of art STS–ICP Advanced Oxide Etch (AOE) system. The planar silica lens pair designed for 200 and 500 µm of ‘ideal’ free-space propagation distance were fabricated and optically tested. A successful implementation of such a scheme, involving guided and free-space optical propagation has been demonstrated for the first time. Practical demonstration and optical characterization of in-plane chip to chip optical interconnects has been performed, however, integration of 45° micromirror and practical demonstration of stacked-die optical interconnect based on planar silica lens pair has been left for future work

Passage Re-Ranking in Live QA NLP Pipelines with BERT

Passage ranking and document ranking are two common tasks in NLP. Many state of the art pipelines use BM25 to retrieve passages. The top results of this ranking are then re-ranked using a BERT transformer trained on the MS MARCO Passage data set. This system and variations have proved highly effective. In addition, questions and answers using BERT are also well explored topics. However, these systems are fundamentally limited by speed and resource consumption requirements. Given an arbitrary corpus and a collection of pre-trained models, we would like to prove that it is possible to create a live Question Answering machine without fine tuning for a particular topic. In particular, we employ a BERT re-ranker to find the first acceptable fit to pass to our QA transformer. This approach is fundamentally different from past research in that it is focused on first fit and not best fit. The goal of this research is to allow anyone to employ off the shelf components to create an effective, interactive question answering system

Post-stroke patients’ rehabilitation exercise assessment from Vicon-based skeletal angle displacement using Convolutional Neural Network

Stroke is one of the leading causes of neurological disorders, and around 1 million people suffer from stroke in the United States. Two-thirds of these individuals survive and requires rehabilitation exercise in their daily life to improve their quality of life. Automatically assessing these performed rehabilitation movements is inherent to improving post-stroke patients\u27 overall physical condition. With the recent growth in computer vision research, people are using motion capture systems to perform physical exercises, workouts, and training at their preferred place, as these systems occupy less space but provide flexibility to the users. This work assesses post-stroke patient rehabilitation movement from full-body skeletal joint displacement data sensed through vision-based Vicon sensors for ten exercises. We take advantage of transfer learning to strike the right balance between computation and performance. We propose a convolutional neural network (CNN) and train it using 117-dimensional skeletal angle displacement data from Vicon. This pre-trained convolutional neural network is fine-tuned for each post-stroke exercise movement. We use the publicly available rehabilitation exercise dataset to showcase the effectiveness and efficacy of our proposed simple CNN model. Our pretrained CNN model outperforms existing state-of-the-art complex Spatio Temporal Convolutional NN and achieves an average of 0.005795 MAD and 0.00786944 RMS error