Partially and fully automated semiconductor manufacturing facilities around the world have employed automated real-time dispatchers (RTD) as a critical element of their factory management solutions. The success of RTD is attributable to a detailed and extremely accurate data base that reflects the current state of the factory, consistently applied dispatching policies and continuous improvement of these dispatching policies.
However, many manufactures are now reaching the benefit limits of pure dispatching-based or other "heuristic-only" solutions. A new solution is needed that combines locally optimized short-interval schedules with RTD policies to target further reductions in product cycle time.
This paper describes an integrated solution that employs four key components:
1. real-time data generation,
2. simulation-based prediction,
3. locally optimized short-interval scheduling, and
4. schedule-aware real-time dispatching.
The authors describe how this solution was deployed in lithography and wet / diffusion areas, and report the resulting improvements measured.
Micro-Doppler refers to Doppler scattering returns produced by non rigid-body motion. Micro-Doppler gives rise to
many detailed radar image features in addition to those associated with bulk target motion. Targets of different classes
(for example, humans, animals, and vehicles) produce micro-Doppler images that are often distinguishable even by nonexpert
observers. Micro-Doppler features have great potential for use in automatic target classification algorithms.
Although the potential benefit of using micro-Doppler in classification algorithms is high, relatively little experimental
(non-synthetic) micro-Doppler data exists. Much of the existing experimental data comes from highly cooperative
targets (human or vehicle targets directly approaching the radar). This research involved field data collection and
analysis of micro-Doppler radar signatures from non-cooperative targets. The data was collected using a low cost Xband
multiple frequency continuous wave (MFCW) radar with three transmit frequencies. The collected MFCW radar
signatures contain data from humans, vehicles, and animals. The presented data includes micro-Doppler signatures
previously unavailable in the literature such as crawling humans and various animal species. The animal micro-Doppler
signatures include deer, dog, and goat datasets. This research focuses on the analysis of micro-Doppler from noncooperative
targets approaching the radar at various angles, maneuvers, and postures.
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