Abstract-Electronic textiles (e-textiles), known as computational fabrics, offer an emerging platform for constructing ambient intelligent applications. Computational nodes in e-textiles are driven by batteries. Unlike wireless sensor networks, not each computational node in e-textiles has its own battery. Instead, many computational nodes in e-textiles share a battery. Existing e-textiles use one fixed battery to drive a fixed set of computation nodes (

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Non-uniformity in reticle CDs can cause yield loss and/or performance degradation during chip manufacturing. As a result, CD Uniformity (CDU) across a reticle is a very important specification for photomask manufacturing. In addition the photomask CDU data can be used in a feedback loop to improve and optimize the mask manufacturing process. A typical application is utilizing CDU data to adjust the mask writer dose and compensate for non-uniformity in the CDs, resulting in improved quality of subsequent masks.Mask makers are currently using the CD-SEM for data collection. While the resolution of SEM data ensures its position as the industry standard, an output map of CDU using the reticle inspection tool has the advantage of denser sampling over larger areas on the mask. High NA reticle inspection systems scan the entire reticle at high throughput, and are ideally suited for collecting CDU data on a dense grid.In this paper, we describe the basic theory of a prototype reticle inspection-based CDU tool, and results on advanced memory masks. We discuss possible applications of CDU maps for optimizing the mask manufacturing process or in adjusting scanner dose to improve wafer CD uniformity.