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White Paper: International Test Solutions

INTRODUCTION Technological demands for 5G connectivity, Internet of Things (IoT), artificial intelligence (AI), wearables, and automobiles (self-driving, electrified, etc.) are key drivers behind the next technology nodes. In today’s commercial landscape, there are extremely high costs associated with the fab investment for the process development. As a result, only a few IDM (integrated device manufacturers) and foundries, such as Intel, Samsung, and TSMC, are actively pursuing Moore’s Law for monolithic silicon wafer scaling. To attain these faster and denser integrated circuits, photolithography has relied on (1) progressively reducing the exposure wavelengths whereby the most advanced nodes are now utilizing Extreme Ultraviolet (EUV) Lithography; and (2) increasing the numerical aperture whereby advanced immersion systems are being implemented. In both fabrication techniques, the depth of focus is absolutely critical, and the entire surface of a clamped wafer substrate must have minimal non-planarity to fall within an extremely tight depth of focus. To maintain the necessary depth of focus, all the critical surfaces within the manufacturing tool chamber must be totally co-planar. For the wafer itself, non-planarity can come from variations in warp, bow, and thickness of the substrate. In each case, the required geometrical properties of the wafer can be attained through various precision polishing processes. Within the tool, a chuck (or table) provides an extremely flat reference surface onto which the wafer will be clamped using vacuum or electrostatic force. A high degree surface flatness of the chuck can be attained through high precision lapping and polish processes. Finally, to assure flatness after clamping, the chucking method and force are optimized based on the wafer substrate material as well as the chuck design. Supply management of substrate material and hardware service steps can be performed to comprehensively address non-planarity issues as part of a regularly scheduled maintenance. Particle contamination is an unpredictable and, arguably, a significant problem for creating critical non-planarity issues during the fabrication process. Of greatest concern is a particle trapped between the backside wafer and wafer chuck surface. The particle contaminant could be crushed or at worst embedded into the wafer or wafer chuck. As a result, there could occur a critical backside leak fault or an out-of-plane distortion (i.e., a “hot-spot”). Unscheduled downtime to properly wet clean the chambers can have dramatic effects on tool and chamber availability, thereby affecting wafer throughput.

White Paper: tier44

INTRODUCTION This document shows organizations using ServiceNow a way to configure a complete DCIM solution using only their ServiceNow environment with native and “Built on NOW” applications. ServiceNow handles an organization’s workflows, user experiences and day to day tasks. Tier44 is a premier ServiceNow technology partner and has worked with PayPal and other organizations on using ServiceNow as a platform and main component of a DCIM solution that has been released on the ServiceNow store for everyone to use and setup. For organizations committed to ServiceNow, this is a significantly more cost-efficient and user-friendly solution than any standalone DCIM product (integrated with ServiceNow or not) can ever provide. This document provides a outline on how to do it yourself, what to ask your ServiceNow implementation partner, and the required and optional components used to build the solution. DCIM, SDDC – WHAT DO THEY MEAN? For everyone who has worked in and around data centers, Data Center Infrastructure Management (DCIM) is a well-known term. It represents an application category used to manage the data center in terms of capacity, space, power and network. Gartner defines DCIM as “tools to monitor, measure, manage and/or control data center utilization and energy consumption of all IT-related equipment (such as servers, storage and network switches) and facility infrastructure components (such as power distribution units [PDUs] and computer room air conditioners [CRACs])”. Another term you may hear is the software-defined data center (SDDC). The SDDC refers to how you manage your IT resources. Traditional servers, storage and networks run operating systems and are provisioned device by device. Software defined equipment is managed by using it as a hardware pool with virtual servers, virtual storage and virtual networks being configured on the fly. This allows IT organizations to reconfigure the use of hardware as needed using automated deployment and management, pool configurations with dynamic workload allocation and eliminates a lot of the individual limits of single hardware configurations. Typical examples are VMware hypervisor based physical servers providing numerous virtual servers as needed. Network and storage virtualization work similarly. Additional terms you might see are Data Center Service Optimization (DCSO), and Data Center Management (DCM), which combines everything. Tier44 calls it “Holistic Data Center Management®”. Regardless of terminology; there is a set of functionality, both on the facility/physical layer and logical layer that needs to be covered. Such functionality includes the management of physical and virtual assets in a single CMDB (configuration management data base), tracking and monitoring of physical and virtual assets including utilization, power, cooling, efficiency, environmental conditions, availability, and cost, along with all the management workflows required to keep the application services up and running reliably at all times. Taking everything together, DCIM for the data center → hardware and SDDC for the hardware → application gives your organization the most flexibility. That is where ServiceNow comes into play.

White Paper: DataFactZ Solutions

Lambda architecture can handle massive quantities of data by providing a single framework. Through Amazon Web Services, we can quickly implement the Lambda Architecture, reduce maintenance overhead and reduce costs. Lambda Architecture also helps in reducing any delay between data collection and availability in dashboards using Apache Spark. This whitepaper discusses about the benefits of enhanced Lambda Architecture in AWS using Apache Spark. Key takeaways from this whitepaper: Traditional Lambda Architecture Three Processing Layers of Lambda Architecture Components of Lambda Architecture on Amazon Web Services