High Performance, Continuously Tunable Microwave Filters using MEMS Devices with Very Large, Controlled, Out-of-Plane Actuation

Software defined radios (SDR) in the microwave X and K bands offer the promise of low cost, programmable operation with real-time frequency agility. However, the real world in which such radios operate requires them to be able to detect nanowatt signals in the vicinity of 100 kW transmitters. This imposes the need for selective RF filters on the front end of the receiver to block the large, out of band RF signals so that the finite dynamic range of the SDR is not overwhelmed and the desired nanowatt signals can be detected and digitally processed. This is currently typically done with a number of narrow band filters that are switched in and out under program control. What is needed is a small, fast, wide tuning range, high Q, low loss filter that can continuously tune over large regions of the microwave spectrum. In this paper we show how extreme throw MEMS actuators can be used to build such filters operating up to 15 GHz and beyond. The key enabling attribute of our MEMS actuators is that they have large, controllable, out-of-plane actuation ranges of a millimeter or more. In a capacitance-post loaded cavity filter geometry, this gives sufficient precisely controllable motion to produce widely tunable devices in the 4-15 GHz regime.Comment: 12 pages 14 figures 2 table

Mems device with large out-of-plane actuation and low-resistance interconnect and methods of use

Source: United States Patent and Trademark Office, www.uspto.gov”The present application is directed to a MEMS device. The MEMS device includes a substrate having a first end and a second end extending along a longitudinal axis, the Substrate including an electrostatic actuator. The device also includes a movable plate having a first end and a second end. The device also includes a thermal actuator having a first end coupled to the first end of the substrate and a second end coupled to the first end of the plate. The actuator moves the plate in relation to the substrate. Further, the device includes a power source electrically coupled to the thermal actuator and the Substrate. The application is also directed to a method for operating a MEMS device

Can Causal Induction Be Reduced to Associative Learning

A number of researchers have recently claimed that higher-order human learning, such as categorization and causal induction, can be explained by the same principles as govern lower order learning, such as classical conditioning in animals. An alternative view is that people often impose abstract causal models on observations, rather than simply associating inputs with outputs. W e report three experiments using a multiple-cue learning paradigm in which models based on associative learning versus abstract causal models make opposing predictions. We show that different causal models can yield radically different learning from identical observations. In particular, we compared people's abilities to learn when the positive cases were defined by a linear cue-combination rule versus a rule involving a within-category correlation between cues. The linear structure was more readily learned when the cues were interpreted as possible causes of an effect to be predicted, whereas the correlated structure was more readily learned when the cues were interpreted as the effects of a cause to be diagnosed. The results disconfirm all associative models of causal induction in which inputs are associated with outputs without regard for causal directionality