Estimating and Enhancing the Yield of Tuneling SRAM Cells by Simulation

As a novel implementation of the static random access memory (SRAM), the tunneling SRAM (TSRAM) uses the negative differential resistance of tunnel diodes (TD’s) and potentially offers considerable improvements in both standby power dissipation and integration density compared to the conventional CMOS SRAM. TSRAM has not yet been realized with a useful bit capacity mainly because the level of uniformity required of the nanoscale TD’s has been demanding and difficult to achieve. In this letter, we propose a Monte Carlo approach for estimating the yield of TSRAM cells and show that by optimizing the cell’s external circuit parameters, we can relax the allowable tolerance of a key device parameter of a resonant-TD-(RTD) based cell by three times.

Multi-Modal Image Retrieval from Construction Databases and Model-Based Systems

In the modern and dynamic construction environment it is important to access information in a fast and efficient manner in order to improve the decision making processes for construction managers. This capability is, in most cases, straightforward with today’s technologies for data types with an inherent structure that resides primarily on established database structures like estimating and scheduling software. However, previous research has demonstrated that a significant percentage of construction data is stored in semi-structured or unstructured data formats (text, images, etc.) and that manually locating and identifying such data is a very hard and time-consuming task. This paper focuses on construction site image data and presents a novel image retrieval model that interfaces with established construction data management structures. This model is designed to retrieve images from related objects in project models or construction databases using location, date, and material information (extracted from the image content with pattern recognition techniques).