Material selection for optimum design of MEMS pressure sensors

Abstract: Choice of the most suitable material out of the universe of engineering materials available to the designers is a complex task. It often requires a compromise, involving conflicts between different design objectives. Materials selection for optimum design of a Micro-Electro-Mechanical-Systems (MEMS) pressure sensor is one such case. For optimum performance, simultaneous maximization of deflection of a MEMS pressure sensor diaphragm and maximization of its resonance frequency are two key but totally conflicting requirements. Another limitation in material selection of MEMS/Microsystems is the lack of availability of data containing accurate micro-scale properties of MEMS materials. This paper therefore, presents a material selection case study addressing these two challenges in optimum design of MEMS pressure sensors, individually as well as simultaneously, using Ashby’s method. First, data pertaining to micro-scale properties of MEMS materials has been consolidated and then the Performance and Material Indices that address the MEMS pressure sensor’s conflicting design requirements are formulated. Subsequently, by using the micro-scale materials properties data, candidate materials for optimum performance of MEMS pressure sensors have been determined. Manufacturability of pressure sensor diaphragm using the candidate materials, pointed out by this study, has been discussed with reference to the reported devices. Supported by the previous literature, our analysis re-emphasizes that silicon with 110 crystal orientation [Si (110)], which has been extensively used in a number of micro-scale devices and applications, is also a promising material for MEMS pressure sensor diaphragm. This paper hence identifies an unexplored opportunity to use Si (110) diaphragm to improve the performance of diaphragm based MEMS pressure sensors

Material selection for optimum design of MEMS pressure sensors

Abstract: Choice of the most suitable material out of the universe of engineering materials available to the designers is a complex task. It often requires a compromise, involving conflicts between different design objectives. Materials selection for optimum design of a Micro-Electro-Mechanical-Systems (MEMS) pressure sensor is one such case. For optimum performance, simultaneous maximization of deflection of a MEMS pressure sensor diaphragm and maximization of its resonance frequency are two key but totally conflicting requirements. Another limitation in material selection of MEMS/Microsystems is the lack of availability of data containing accurate micro-scale properties of MEMS materials. This paper therefore, presents a material selection case study addressing these two challenges in optimum design of MEMS pressure sensors, individually as well as simultaneously, using Ashby’s method. First, data pertaining to micro-scale properties of MEMS materials has been consolidated and then the Performance and Material Indices that address the MEMS pressure sensor’s conflicting design requirements are formulated. Subsequently, by using the micro-scale materials properties data, candidate materials for optimum performance of MEMS pressure sensors have been determined. Manufacturability of pressure sensor diaphragm using the candidate materials, pointed out by this study, has been discussed with reference to the reported devices. Supported by the previous literature, our analysis re-emphasizes that silicon with 110 crystal orientation [Si (110)], which has been extensively used in a number of micro-scale devices and applications, is also a promising material for MEMS pressure sensor diaphragm. This paper hence identifies an unexplored opportunity to use Si (110) diaphragm to improve the performance of diaphragm based MEMS pressure sensors

Sensitivity Enhancement of Silicon-on-Insulator CMOS MEMS Thermal Hot-Film Flow Sensors by Minimizing Membrane Conductive Heat Losses

Minimizing conductive heat losses in Micro-Electro-Mechanical-Systems (MEMS) thermal (hot-film) flow sensors is the key to minimize the sensors’ power consumption and maximize their sensitivity. Through a comprehensive review of literature on MEMS thermal (calorimetric, time of flight, hot-film/hot-film) flow sensors published during the last two decades, we establish that for curtailing conductive heat losses in the sensors, researchers have either used low thermal conductivity substrate materials or, as a more effective solution, created low thermal conductivity membranes under the heaters/hot-films. However, no systematic experimental study exists that investigates the effect of membrane shape, membrane size, heater/hot-film length and M e m b r a n e (size) to H e a t e r (hot-film length) Ratio (MHR) on sensors’ conductive heat losses. Therefore, in this paper we have provided experimental evidence of dependence of conductive heat losses in membrane based MEMS hot-film flow sensors on MHR by using eight MEMS hot-film flow sensors, fabricated in a 1 µm silicon-on-insulator (SOI) CMOS foundry, that are thermally isolated by square and circular membranes. Experimental results demonstrate that: (a) thermal resistance of both square and circular membrane hot-film sensors increases with increasing MHR, and (b) conduction losses in square membrane based hot-film flow sensors are lower than the sensors having circular membrane. The difference (or gain) in thermal resistance of square membrane hot-film flow sensors viz-a-viz the sensors on circular membrane, however, decreases with increasing MHR. At MHR = 2, this difference is 5.2%, which reduces to 3.0% and 2.6% at MHR = 3 and MHR = 4, respectively. The study establishes that for membrane based SOI CMOS MEMS hot-film sensors, the optimum MHR is 3.35 for square membranes and 3.30 for circular membranes, beyond which the gain in sensors’ thermal efficiency (thermal resistance) is not economical due to the associated sharp increase in the sensors’ (membrane) size, which makes sensors more expensive as well as fragile. This paper hence, provides a key guideline to MEMS researchers for designing the square and circular membranes-supported micro-machined thermal (hot-film) flow sensors that are thermally most-efficient, mechanically robust and economically viable

A Map-Based Recommendation System and House Price Prediction Model for Real Estate

In 2015, global real estate was worth $217 trillion, which is approximately 2.7 times the global GDP; it also accounts for roughly 60% of all conventional global resources, making it one of the key factors behind any country’s economic growth and stability. The accessibility of spatial big data will help real estate investors make better judgement calls and earn additional profit. Since location is deemed necessary for real estate and consequent decision-making, digital maps have become a prime resource for real estate purchases, planning and development. Personalisation can assist in making judgments by identifying user desires and inclinations, which can then be recorded or captured as a user performs some interactions with a digital map. A personalised real estate portal can use this information to suggest properties, assist homeowners and provide valuable real estate analytics. This article presents a novel framework for recommending real estate to users. By monitoring user interactions through an online real estate portal, the framework can make personalised recommendations of real estate based on content, collaboration and location. The effectiveness of the recommendations was tested by the user feedback mechanism through a method of mean absolute precision, and the results show that 79% precise suggestions were generated, i.e., out of 5 recommendations produced, users were interested in at least 3. Along with that, a separate house price prediction model based on neural networks and classical regression techniques was also implemented to assist users in making an informed decision regarding prospects of real estate purchase

An SOI CMOS-Based Multi-Sensor MEMS Chip for Fluidic Applications

An SOI CMOS multi-sensor MEMS chip, which can simultaneously measure temperature, pressure and flow rate, has been reported. The multi-sensor chip has been designed keeping in view the requirements of researchers interested in experimental fluid dynamics. The chip contains ten thermodiodes (temperature sensors), a piezoresistive-type pressure sensor and nine hot film-based flow rate sensors fabricated within the oxide layer of the SOI wafers. The silicon dioxide layers with embedded sensors are relieved from the substrate as membranes with the help of a single DRIE step after chip fabrication from a commercial CMOS foundry. Very dense sensor packing per unit area of the chip has been enabled by using technologies/processes like SOI, CMOS and DRIE. Independent apparatuses were used for the characterization of each sensor. With a drive current of 10 µA–0.1 µA, the thermodiodes exhibited sensitivities of 1.41 mV/°C–1.79 mV/°C in the range 20–300 °C. The sensitivity of the pressure sensor was 0.0686 mV/(Vexcit kPa) with a non-linearity of 0.25% between 0 and 69 kPa above ambient pressure. Packaged in a micro-channel, the flow rate sensor has a linearized sensitivity of 17.3 mV/(L/min)−0.1 in the tested range of 0–4.7 L/min. The multi-sensor chip can be used for simultaneous measurement of fluid pressure, temperature and flow rate in fluidic experiments and aerospace/automotive/biomedical/process industries