Using pheromone-based communication, the message can be quickly delivered from static nodes to sink nodes.Those approaches can effectively reduce the energy consum
As indicated in recent International Technology Roadmap for Semiconductors reports [1], the future fabrication cost per-unit-area of integrated circuits (IC) will be decreased by moving to larger-diameter semiconductor wafers in the fabrication process, however, this move will require more sophisticated and precise process control mechanisms to ensure that process yields are preserved. Hence, driven by practical future manufacturing requirements, the design of process control mechanisms continues to be an active research topic in the IC manufacturing domain.Plasma etching is a key processing method employed in IC fabrication steps.
By first masking areas of the silicon wafer being processed, subsequent exposure to plasma yields the required etched features on the surface of the wafer. The process is fundamentally complex from a physical and engineering control perspective and sensitive to an array of process parameters [2]. As there is currently an incomplete understanding of the underling physics and chemistry to allow for pre-determined process control, etching processes are often developed empirically [3]. Critical to empirical control (and to the development of further fundamental understanding of the process) is the development of mechanisms for plasma monitoring by sensor data collection and analysis.Generally, there are two types of plasma diagnostic sensors: intrusive sensors and non-intrusive sensors.
One popular intrusive technology is the Langmuir probe [4], which is immersed directly into the plasma. Although direct measurements of targeted plasma parameters may be made, the direct immersion of the probe into the process environment results in changes in the temperature, density, and potential of the plasma and ultimately affects etching process results. Non-intrusive plasma process monitoring technologies include impedance monitoring [5], reflectometry sensing [6] and OES [7]. Due to the abundant information that can be extracted from the data and the direct (although complex) relationship of the data to the etching process, OES is widely applied to IC fabrication [7]. The richness of OES data is also a potential hindrance to effective interpretation and utility of the data.
Of particular concern is data dimensionality. For example, a miniature Ocean Optics USB4000 fibre optic spectrometer, as used in the present work, provides intensity measurements of 2,048 wavelengths from 178.31 nm to 874.27 Anacetrapib nm [8]. Full spectrum samples are typically taken every 0.7 s over typically 40 s of a dynamically changing process and datasets from hundreds of such process runs are taken for statistical analysis.
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