Kinji Harada

Silicon pressure sensor integrates resonant strain gauge on diaphragm

Abstract A novel silicon pressure sensor has been developed which will enable high-precision pressure measurement. The sensor, which is based on a new concept, is fabricated from a single silicon crystal and has two resonant strain gauges which are held in vacuum cavities on the surface of the diaphragm to isolate them from the surrounding fluid. The two oscillating frequencies of the resonant strain gauges are differentially modulated by pressure. The sensors measuring principle, features, its amplitude-controlled self-oscillation circuit, and the results of experiments are given.

Three-dimensional micromachining of silicon pressure sensor integrating resonant strain gauge on diaphragm

Abstract A method of fabricating a novel pressure sensor is presented. The sensor has resonant strain gauges built into micro vacuum cavities on the surface of the diaphragm. The resonant strain gauge has a resonator the natural frequency of which is modulated by the strain in the diaphragm surface. The resonator and the vacuum cavity of the strain gauge are fabricated by a self-aligning selective epitaxial method and a hybrid selective etching method; a unique vacuum-sealing technique is used to make the vacuum cavity.

Various applications of resonant pressure sensor chip based on 3-D micromachining

Abstract An accurate and stable resonant pressure sensor fabricated using 3-D micromachining process was developed. Two resonators are located on the surface of the diaphragm and applied pressure is measured from the difference of two resonant frequencies. The resonators are encapsulated into the micro-vacuum cavities in order to isolate them from surrounding fluid and to get stable resonance. Three components, namely, the diaphragm, the resonators, and the vacuum cavities, are all single crystalline and monolithically structured on the 6.8×6.8-mm wide, 0.5-mm thick silicon chip. The resonator, having a high Q -value of 50 000, was obtained owing to the vacuum isolation and resulted in superior characteristics such as resolution, repeatability and long-term stability. In the next place, the developed pressure sensor was successfully applied to the differential pressure transmitter for industrial process, and several further applications were accomplished successively.

Intelligent sensors in process instrumentation

Publisher Summary This chapter discusses the general features of intelligent field instruments and explains several examples of them such as flowmeters, differential pressure transmitters, and a control valve positioner. In addition, the current status of the field bus is described because of its close relationship with intelligent field instruments. The development of intelligent field instruments has made progress with sophisticated signal processing functions and communication functions by using microcomputers. Both measuring characteristics and communication capabilities have been greatly improved—for example, field instruments have become able to make calculations using the multiple sensors signal, to pass accurate judgment on the operational conditions of the process and to acquire knowledge of the control process. The use of microcomputers has been an essential condition of the realization of intelligent field instruments. The following are considered to be general reasons for the provision of intelligent field instruments; (1) Improvements in measurement accuracy (a) linearization of the relationship between input and output signals, (b) automatic zero-point calibration, (c) automatic compensation of errors caused by environmental disturbances such as changes in ambient temperature, and (d) automatic compensation of errors caused by changes in the process condition, such as fluid temperature and fluid pressure in flow measurement, and (2) Improvements in operational capability and maintenance ability (a) remote maintenance operation utilizing digital communication functions, (b) integration of different range sensors by widening sensors rangeability, (c) storage and readout of sensor data and process control data, and (d) self-check and self-learning functions.