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This paper aims to describe the fabrication and characterization of current mirror-integrated microelectromechanical systems (MEMS)-based pressure sensor.

The integrated pressure-sensing structure consists of three identical 100-µm long and 500-µm wide n-channel MOSFETs connected in a resistive loaded current mirror configuration. The input transistor of the mirror acts as a constant current source MOSFET and the output transistors are the stress sensing MOSFETs embedded near the fixed edge and at the center of a square silicon diaphragm to sense tensile and compressive stresses, respectively, developed under applied pressure. The current mirror circuit was fabricated using standard polysilicon gate complementary metal oxide semiconductor (CMOS) technology on the front side of the silicon wafer and the flexible pressure sensing square silicon diaphragm, with a length of 1,050 µm and width of 88 µm, was formed by bulk micromachining process using tetramethylammonium hydroxide solution on the backside of the wafer. The pressure is monitored by the acquisition of drain voltages of the pressure sensing MOSFETs placed near the fixed edge and at the center of the diaphragm.

The current mirror-integrated pressure sensor was successfully fabricated and tested using in-house developed pressure measurement system. The pressure sensitivity of the tested sensor was found to be approximately 0.3 mV/psi (or 44.6 mV/MPa) for pressure range of 0 to 100 psi. In addition, the pressure sensor was also simulated using Intellisuite MEMS Software and simulated pressure sensitivity of the sensor was found to be approximately 53.6 mV/MPa. The simulated and measured pressure sensitivities of the pressure sensor are in close agreement.

The work reported in this paper validates the use of MOSFETs connected in current mirror configuration for the measurement of tensile and compressive stresses developed in a silicon diaphragm under applied pressure. This current mirror readout circuitry integrated with MEMS pressure-sensing structure is new and fully compatible to standard CMOS processes and has a promising application in the development CMOS-MEMS-integrated smart sensors.

This paper aims to describe the fabrication, packaging and testing of a resistive loaded p-channel metal-oxide-semiconductor field-effect transistor-based (MOSFET-based) current mirror-integrated pressure transducer.

Using the concept of piezoresistive effect in a MOSFET, three identical p-channel MOSFETs connected in current mirror configuration have been designed and fabricated using the standard polysilicon gate process and microelectromechanical system (MEMS) techniques for pressure sensing application. The channel length and width of the p-channel MOSFETs are 100 µm and 500 µm, respectively. The MOSFET M1 of the current mirror is the reference transistor that acts as the constant current source. MOSFETs M2 and M3 are the pressure-sensing transistors embedded on the diaphragm near the mid of fixed edge and at the center of the square diaphragm, respectively, to experience both the tensile and compressive stress developed due to externally applied input pressure. A flexible square diaphragm having a length of approximately 1,000 µm and thickness of 50 µm has been realized using deep-reactive ion etching of silicon on the backside of the wafer. Then, the fabricated sensor chip has been diced and mounted on a TO8 header for the testing with pressure.

The experimental result of the pressure sensor chip shows a sensitivity of approximately 0.2162 mV/psi (31.35 mV/MPa) for an input pressure of 0-100 psi. The output response shows a good linearity and very low-pressure hysteresis. In addition, the pressure-sensing structure has been simulated using the parameters of the fabricated pressure sensor and from the simulation result a pressure sensitivity of approximately 0.2283 mV/psi (33.11 mV/MPa) has been observed for input pressure ranging from 0 to 100 psi with a step size of 10 psi. The simulated and experimentally tested pressure sensitivities of the pressure sensor are in close agreement with each other.

This current mirror readout circuit-based MEMS pressure sensor is new and fully compatible to standard CMOS processes and has a promising application in the development CMOS-MEMS-integrated smart sensors.

The purpose of this paper is to suggest a framework for extracting the sequential patterns of maintenance activities and related spare parts information from historical records of maintenance data with pre-defined support or threshold values.

A data mining approach has been adopted for predicting the maintenance activity along with spare parts. It starts with a collection of spare parts and maintenance data, and then the development of sequential patterns followed by formation of frequent spare part groups, and finally, integration of sequential maintenance activities with the associated spare parts.

This study suggests a framework for extracting the sequential patterns of maintenance activities from historical records of maintenance data with pre-defined support or threshold values. A rule-based approach is proposed in this paper to predict the occurrence of next maintenance activity along with the information of spare parts consumption for that maintenance activity.

Presented model can be extended for analyzing the failure maintenance activities and performing root cause analysis that can give more valuable suggestion to maintenance managers to take corrective actions prior to next occurrence of failures. In addition, the timestamp information can be utilized to prioritize the maintenance activity that is ignored in this study.

The proposed model has a high potential for industrial applications and is validated through a case study. The study suggests that the model gives a better approach for selecting spare parts based on their similarity or correlation, considering their actual occurrence during maintenance activities. Apart from this, the clustering of spare parts also trains maintenance manager to learn about the dependency among the spares for group stocking and maintaining the parts availability during maintenance activities.

This study has used the technique of data mining to find dependent spare parts itemset from the database of the company and developed the model for associated spare parts requirement for subsequent maintenance activity.

The present paper aims to propose a basic current mirror-sensing circuit as an alternative to the traditional Wheatstone bridge circuit for the design and development of high-sensitivity complementary metal oxide semiconductor (CMOS)–microelectromechanical systems (MEMS)-integrated pressure sensors.

This paper investigates a novel current mirror-sensing-based CMOS–MEMS-integrated pressure-sensing structure based on the piezoresistive effect in metal oxide field effect transistor (MOSFET). A resistive loaded n-channel MOSFET-based current mirror pressure-sensing circuitry has been designed using 5-μm CMOS technology. The pressure-sensing structure consists of three identical 10-μm-long and 50-μm-wide n-channel MOSFETs connected in current mirror configuration, with its input transistor as a reference MOSFET and output transistors are the pressure-sensing MOSFETs embedded at the centre and near the fixed edge of a silicon diaphragm measuring 100 × 100 × 2.5 μm. This arrangement of MOSFETs enables the sensor to sense tensile and compressive stresses, developed in the diaphragm under externally applied pressure, with respect to the input reference transistor of the mirror circuit. An analytical model describing the complete behaviour of the integrated pressure sensor has been described. The simulation results of the pressure sensor show high pressure sensitivity and a good agreement with the theoretical model has been observed. A five mask level process flow for the fabrication of the current mirror-sensing-based pressure sensor has also been described. An n-channel MOSFET with aluminium gate was fabricated to verify the fabrication process and obtain its electrical characteristics using process and device simulation software. In addition, an aluminium gate metal-oxide semiconductor (MOS) capacitor was fabricated on a two-inch p-type silicon wafer and its CV characteristic curve was also measured experimentally. Finally, the paper presents a comparative study between the current mirror pressure-sensing circuit with the traditional Wheatstone bridge.

The simulated sensitivities of the pressure-sensing MOSFETs of the current mirror-integrated pressure sensor have been found to be approximately 375 and 410 mV/MPa with respect to the reference transistor, and approximately 785 mV/MPa with respect to each other. The highest pressure sensitivities of a quarter, half and full Wheatstone bridge circuits were found to be approximately 183, 366 and 738 mV/MPa, respectively. These results clearly show that the current mirror pressure-sensing circuit is comparable and better than the traditional Wheatstone bridge circuits.

The concept of using a basic current mirror circuit for sensing tensile and compressive stresses developed in micro-mechanical structures is new, fully compatible to standard CMOS processes and has a promising application in the development of miniaturized integrated micro-sensors and sensor arrays for automobile, medical and industrial applications.

The purpose of this paper is to use finite element method for optimizing the membrane type double cavity vacuum sealed structure for the best achievable sensitivity in a piezoresistive absolute pressure sensor and its validation using a standard complementary metal oxide semiconductor (CMOS) process.

A double cavity vacuum sealed piezoresistive absolute pressure sensor has been simulated and optimized for its performance and an analytical model describing the behaviour of the sensor has been described. The 1×1 mm sensor chip has two membrane type 100×30×1.7 μm diaphragms consisting of composite layers of plasma enhanced chemical vapour deposition (PECVD) of silicon nitride (Si₃N₄) and silicon dioxide (SiO₂) each hanging over 21 μm deep rectangular cavity. Potassium hydroxide (KOH) based anisotropic etching of single crystal silicon using front side lateral etching technology is used for the fabrication of the sensor. The electrical readout circuitry uses 318 Ω boron diffused low pressure vapour chemical vapour deposition (LPCVD) of polysilicon resistors arranged in the Wheatstone half bridge configuration. The sensing structure is simulated and optimized using COMSOL Multiphysics.

Front-side lateral etching technology has been successfully used for the fabrication of double cavity absolute pressure sensor. A good agreement with the fabricated device for the chosen location of the piezoresistors through simulation has been predicted. The measured pressure sensitivity of two tested pressure sensors is 12.63 and 12.46 mV/MPa, and simulated pressure sensitivity is found to be 12.9 mV/MPa for pressure range of 0 to 0.5 MPa. The location of the piezoresistor has also been optimized using the simulation tools for enhancing the sensor sensitivity to 62.14 mV/MPa. The pressure sensitivity is further enhanced to 92 mV/MPa by increasing the width of the diaphragm to 35 μm.

The simulated and measured pressure sensitivities of the double cavity pressure sensor are in close agreement. Sevenfold enhancement in the pressure sensitivity of the optimized sensing structure has been observed. The proposed front-side lateral etching technology can be adopted for making membrane type diaphragms hanging over vacuum sealed micro-cavities for high sensitivity pressure sensing applications.

The purpose of this paper is to describe the fabrication of a miniaturized membrane type double cavity vacuum‐sealed micro sensor for absolute pressure using front‐side lateral etching technology.

Potassium hydroxide‐based anisotropic etching of single crystal silicon is used to realize the cavities under the membrane type diaphragms through channels on the sides. The diaphragms consist of composite layers of plasma‐enhanced chemical vapour deposition (PECVD) of silicon nitride and silicon dioxide. PECVD of silicon dioxide is done for sealing the channels and the cavity in vacuum. Boron thermal diffusion in low‐pressure chemical vapour deposition of polysilicon layer over the membrane is done for realizing resistors. The fabricated device uses Wheatstone half bridge circuit to read the variation of resistance with respect to an applied pressure.

A double cavity vacuum‐sealed absolute pressure micro sensor has been fabricated successfully using front‐side lateral etching technology and has been measured for pressure range of 0‐0.45 MPa. The measured pressure sensitivity of two pressure sensors is 9.28 and 10.44 mV/MPa.

The paper shows that front‐side lateral etching technology is feasible in the fabrication of small vacuum‐sealed cavities and absolute pressure sensors.

This study examines the performance effect of working capital for a large sample of Indian manufacturing firms in light of supply chain disruption, i.e. the COVID-19 pandemic.

This study is based on secondary data collected from the Prowess database on Indian manufacturing firms listed on the Bombay Stock Exchange (BSE) 500. Panel data regression analyses are used to estimate all models. Moreover, this study has employed robust standard errors to consider for heteroscedasticity concerns.

The results challenge the current notion of working capital investment and reveal that higher working capital has a positive and significant impact on firm performance. Further, it highlights that Indian manufacturing firms suffered financially post-COVID-19 as they significantly lack the working capital to run day-to-day operations.

This research contributes to the scant literature by examining the association between working capital financing and firm performance in light of the COVID-19 pandemic, representing typical developing economies like India.

This study aims to examine the relationship between the perception of consumers about corporate social responsibility (CSR) and consumers’ purchasing behaviour in the retail sector. Specifically, this study investigates the impact of perceived CSR on consumer attitude and behaviour and the influence of attitude on the relationship between perceived CSR and purchase behaviour.

In this study for collection of the data, an online questionnaire was distributed among the Indian retail consumers. From the collected primary data set, 249 data points were found fit for analysis. Further, the direct, indirect and moderating effects were evaluated using the structural equation modelling technique.

It is identified that while perceived CSR has a significant influence on consumer purchase behaviour, consumer attitude is having an insignificant impact on the relationship between perceived CSR and purchase behaviour. The findings of this study also show that consumer demographics do not have any moderating impact on the relationship between perceived CSR and purchase behaviour.

The findings of this study are useful to retail managers interested in enhancing CSR. The results of this study suggest that retailers should focus on strengthening consumers’ perceptions about retailers’ CSR initiatives and enhancing co-creation activities. As an extension to this research, further study can include more potential mediators like consumer effectiveness and timing of CSR initiatives.

This study applies stakeholder theory as well as extends the classic theory of planned behaviour model and proposes the establishment of links among consumers’ perceptions about CSR, consumer attitude and behaviour around the retail sector. In addition, this study considers not only overall consumer behaviour but also specific dimensions of consumer behaviour, namely, loyalty, intention and satisfaction.

This study aims to examine the impact of big data management on green service production (GSP) and environmental performance (ENPr) while considering green HRM practices (GHRM) in healthcare emergencies.

The authors collected primary data from major healthcare organizations in India by surveying healthcare professionals. The data analysis through structural equation modelling (PLS-SEM) reveals several significant relationships to extricate the underlying dynamics.

Grounded in the theories of service production and natural resource-based view (NRBV), this study conceptualizes GSP with its three dimensions of green procurement (GP), green service design (GSD) and green service practices (GSPr). The study conducted in India's healthcare sector with a sample size limited to healthcare professionals serving in COVID-19 identifies the positive and significant impact of big data management on GSP and ENPr that organizations seek to deploy in such emergencies. The findings of the study explain the moderating effects of GHRM on GSP-ENPr relationships.

The study was conducted in the healthcare sector in India, and its sample size was limited to healthcare professionals serving in COVID-19. The practical ramifications for healthcare administrators and policymakers are suggested, and future avenues of research are discussed.

This paper develops a holistic model of big data analytics, GP, GSD, GSPr, GHRM and ENPr. This study is a first step in investigating how big data management contributes to ENPr in an emergency and establishing the facets of GSP as a missing link in this relationship, which is currently void in the literature. This study contributes to the theory and fills the knowledge gap in this area.

The purpose of this paper is to understand the customer attitude and perception towards the online brand communities (OBCs) and purchase intention (PI). The paper also analyses the moderating effects of age, income and internet usage on the relationship among the proposed variables.

The study employed a multi-analytical approach through the use of structural equation modelling and neural networks to test the proposed framework. The information was obtained from 236 respondents using a web-based questionnaire survey. Further, moderation analysis, with age, annual family income and weekly internet usage as moderating variables, was conducted on the structural model.

The results of the analysis demonstrate that the multi-group moderating effect for different groups within age, income and internet usage have significant differences for the relationships between the independent variables content perception, perceived usefulness, critical mass, brand attachment and attitude towards brand and the dependent variable, PI.

The study is one of its kind to understand and analyse the role of moderating variables (age, income and internet usage) on OBC-induced PI.