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A wafer-scale CMOS-compatible process for heterogeneous integration of III-V epitaxial material onto silicon for photonic device fabrication is presented. Transfer of AlGaAs-GaAs Vertical-Cavity Surface-Emitting Laser (VCSEL) epitaxial material onto silicon using a carrier wafer process and metallic bonding is used to form III-V islands which are subsequently processed into VCSELs. The transfer process begins with the bonding of III-V wafer pieces epitaxy-down on a carrier wafer using a temporary bonding material. Following substrate removal, precisely-located islands of material are formed using photolithography and dry etching. These islands are bonded onto a silicon host wafer using a thin-film non-gold metal bonding process and the transfer wafer is removed. Following the bonding of the epitaxial islands onto the silicon wafer, standard processing methods are used to form VCSELs with non-gold contacts. The removal of the GaAs substrate prior to bonding provides an improved thermal pathway which leads to a reduction in wavelength shift with output power under continuous-wave (CW) excitation. Unlike prior work in which fullyfabricated VCSELs are flip-chip bonded to silicon, all photonic device processing takes place after the epitaxial transfer process. The electrical and optical performance of heterogeneously integrated 850nm GaAs VCSELs on silicon is compared to their as-grown counterparts. The demonstrated method creates the potential for the integration of III-V photonic devices with silicon CMOS, including CMOS imaging arrays. Such devices could have use in applications ranging from 3D imaging to LiDAR. 

Sensory IoT (Internet of Things) networks are widely applied and studied in recent years and have demonstrated their unique benefits in various areas. In this paper, we bring the sensor network to an application scenario that has rarely been studied - the academic cleanrooms. We design SENSELET++, a low-cost IoT sensing platform that can collect, manage and analyze a large amount of sensory data from heterogeneous sensors. Furthermore, we design a novel hybrid anomaly detection framework which can detect both time-critical and complex non-critical anomalies. We validate SENSELET++ through the deployment of the sensing platform in a lithography cleanroom. Our results show the scalability, flexibility, and reliability properties of the system design. Also, using real-world sensory data collected by SENSELET++, our system can analyze data streams in real-time and detect shape and trend anomalies with a 91% true positive rate. 

Semiconductor cleanrooms are used to fabricate devices with feature sizes that can be much smaller than a dust particle. Hence, any environmental deviations in temperature, or humidity around fabrication instruments may become the root cause of hundreds of transistors failing during the manufacturing. Furthermore, researchers work with dangerous chemicals in cleanrooms and violation of safety may lead to disastrous consequences. Therefore, we have developed an affordable, locally-controlled distributed sensing infrastructure, called SENSELET, for academic cleanrooms. It provides highly effective services for environment sensing around scientific instruments, sensory data collection and visualization, indoor localization, and instrument proximity detection for safety of researchers. 

The recent development of 8-in Gallium Nitride on Silicon (GaN-on-Si) wafers has facilitated cost effective, large-scale manufacturability of GaN-based electronics. Leveraging its wide band gap, capability to support a two dimensional electron gas (2DEG) layer, and strong built-in polarization effects, GaN-based electronic devices have become a viable cost-effective successor to silicon-based devices for high-performance applications where the large bandgap and high breakdown field are required. The advantageous properties of GaN-on-Si material, however, have yet to be utilized for photonic integrated circuit applications. Therefore, the exploration of GaN for efficient on-chip optical modulation and switching applications is examined. In order to effectively characterize GaN’s capabilities for optical modulation and switching, GaN based Mach-Zehnder modulators are designed and fabricated. Through simulating the propagating optical modes supported in a GaN-based Mach-Zehnder structure, the geometry of the device is designed to optimize optical modal overlap with the 2DEG layer while maintaining single-mode performance. Through electrical and optical characterization, the effective electro-optic coefficient and Vπ length are measured. These measurements provide a method of benchmarking GaN-based photonic devices for their optical modulation and switching efficiency. 

The recent development of 8-in Gallium Nitride on Silicon (GaN-on-Si) wafers has facilitated cost effective, large-scale manufacturability of GaN-based electronics. Leveraging its wide band gap, capability to support a two dimensional electron gas (2DEG) layer, and strong built-in polarization effects, GaN-based electronic devices have become a viable cost-effective successor to silicon-based devices for high-performance applications where the large bandgap and high breakdown field are required. The advantageous properties of GaN-on-Si material, however, have yet to be utilized for photonic integrated circuit applications. Therefore, the exploration of GaN for efficient on-chip optical modulation and switching applications is examined. In order to effectively characterize GaN’s capabilities for optical modulation and switching, GaN-based Mach-Zehnder modulators are designed and fabricated. Through simulating the propagating optical modes supported in a GaN-based Mach-Zehnder structure, the geometry of the device is designed to optimize optical modal overlap with the 2DEG layer while maintaining single-mode performance. Through electrical and optical characterization, the effective electro-optic coefficient and Vπ length are measured. These measurements provide a method of benchmarking GaN-based photonic devices for their optical modulation and switching efficiency. 

Recent advances in cyber-infrastructure have enabled digital data sharing and ubiquitous network connectivity between scientific instruments and cloud-based storage infrastructure for uploading, storing, curating, and correlating of large amounts of materials and semiconductor fabrication data and metadata. However, there is still a significant number of scientific instruments running on old operating systems that are taken offline and cannot connect to the cloud infrastructure, due to security and network performance concerns. In this paper, we propose BRACELET - an edge-cloud infrastructure that augments the existing cloud-based infrastructure with edge devices and helps to tackle the unique performance & security challenges that scientific instruments face when they are connected to the cloud through public network. With BRACELET, we put a networked edge device, called cloudlet, in between the scientific instruments and the cloud as the middle tier of a three-tier hierarchy. The cloudlet will shape and protect the data traffic from scientific instruments to the cloud, and will play a foundational role in keeping the instruments connected throughout its lifetime, and continuously providing the otherwise missing performance and security features for the instrument as its operating system ages.