(August 16, 2010) — The IDTechEx report, "Active RFID and Sensor Networks 2011-2021," comprehensively analyzes the technologies, players and markets with detailed 10-year forecasts, including tag numbers, unit prices and interrogator numbers and prices. Details of over 75 active RFID implementations are given along with over 100 suppliers and full technology analysis — from printed batteries to Wi-Fi RFID to UWB tags and systems. IDTechEx constructed 10-year forecasts usefully segmented by frequency, application, territory, etc, and illustrated by dozens of tables and figures. The active RFID market will grow to almost 10 times its present size by 2020.
Figure. The report shows the penetration of active RFID into different application sectors over the next ten years.
Market forecasts
The term Active RFID incorporates many technologies including Real Time Locating Systems, Ubiquitous Sensor Networks and Active RFID with ZigBee, RuBee, Ultra Wide Band and WiFi. Active RFID, where a battery drives the tag, is responsible for an increasing percentage of the money spent in the burgeoning RFID market. It will rise from 13% of the total RFID market in 2010 to 25% in 2020, meaning a huge $6.02 billion market. If we include the market for cell phone RFID modules (another form of active RFID), the market is an additional $0.18 billion in 2010 and $1.6 billion in 2020.
Factors for growth
Factors creating this growth will be Real Time Location Systems (RTLS), and ubiquitous RFID sensor systems (also known as wireless sensor networks. Conventional active RFID used where passive solutions are inadequate and RFID modules for mobile phones will make up the rest. The rapid growth of the active RFID market is being driven by such factors as: Much stronger market demand for tracking, locating and monitoring people and things. This is driven by security, safety, cost and customer satisfaction, for example. Important factors are increased competition in consumer goods, the new terrorism, internal theft, threatened epidemics of disease, coping with increasing numbers of elderly persons and consumers demanding better service and more information.
Reduction in cost and size of the tags and systems. With lower power circuits, button batteries are now adequate for most applications and even printed batteries are gaining a place. In future, miniature fuel cells, printed photovoltaics and other power sources will have a place. This will help to overcome constraints of lifetime, cost and size.
Development of Ubiquitous Sensor Networks (USN) where large numbers of active RFID tags with sensors are radio networked in buildings, forests, rivers, hospitals and many other locations.
Availability of open standards — notably ISO 18000-7, IEEE 802.15.4 and NFC.
Leveraging many newly popular forms of short range wireless communication, particularly WiFi and ZigBee and including mesh networks
Use of mobile phones for purchasing, mass transit and interrogating smart posters, etc.
Active RFID sales to 2010
To the beginning of 2010, 772 million active RFID tags have been sold with the vast majority used for car clickers (690 million). Like these, a large percentage of active RFID tags being sold in the future will replace nothing: they will perform new functions. The second biggest use for active RFID to date has been by the military, using 14 million active RFID tags so far. Both sectors have spent over $1 billion on active RFID.
We are now in the decade of most active tags having button batteries and being the size of a matchbox and often incorporating other radio systems, and sometimes being parasitic upon them in some cases. Overlapping this, we are starting the decade or more of active RFID in the form of a label or laminate. This has been triggered by costs of smart active labels and battery assisted passive (BAP) tags coming down, even those incorporating sensors, and their laminar batteries having enhanced power and life. Some will even have displays. That will run in parallel with matchbox-sized and smaller active RFID tags that are exceptionally capable, with such features as Real Time Location Systems (RTLS) and multiple sensing.
Strong investment
Recently, the investment community has taken even more interest in active RFID. Of 27 recent fund raisings by RFID companies studied by IDTechEx, 37% of the companies involved are in active RFID. 22% are in the particularly popular RTLS sector. Recent acquisitions also favor active RFID companies. Indeed the largest exit, for hundreds of millions of dollars, was a company selling active RFID and RTLS systems.
Active RFID a systems business
Companies involved know that this is not like the highest volume uses of passive RFID tags where disposable labels are usually involved and the label cost can be 50% of total cost. Most active RFID (such as RTLS) is more of a systems business.
Active tag price
With over 100 companies now involved in some part of the active RFID value chain, and considerable government financing of research on low cost active RFID, unit prices will strongly erode, creating a strong growth in numbers sold. The price erosion will be more rapid in some years as new technologies come into play such as new microbatteries and printed logic.
Throughout the next ten years, RTLS will dominate the spend on tags but this will consist of many small orders. Mobile phone/cell phone modules will see considerable price erosion as they are increasingly incorporated into the phone circuitry and volumes increase.
In the future, we see active RFID as intimately involved with many short range radio systems and interfaces, including passive RFID.
Analysis of Active RFID implementations
In IDTechEx’s analysis of 75 active RFID case studies from 18 countries, the largest number of projects we have located has been in Logistics with around double the number for each of the nearest contenders – Air Industry, Automotive/Transportation and Healthcare. Added to those as important sectors will be such things as safety of constructions and people monitored by Ubiquitous Sensor Networks in later years. Meanwhile, RTLS is being put in about 50 hospitals yearly, for staff, patients and assets. In the case studies, the items that are tagged were mainly containers, followed by vehicles, conveyances and people and this probably reflects the market as a whole.
(August 17, 2010) — Rohit Pathak, Acropolis Institute of Technology & Research, Indore, M. P., India and Satyadhar Joshi, Shri Vaishnav Institute of Technology & Science, Indore, M. P., India, have analyzed the effect of innovations in nanotechnology on wireless sensor networks (WSN) and have modeled carbon nanotube- (CNT) based sensor nodes from a device prospective. A WSN model has been programmed in Simulink-MATLAB and a library developed. Integration of CNT in WSN for various modules such as sensors, microprocessors, batteries etc has been shown. Also, average energy consumption for the system has been formulated and its reliability has been shown holistically. A proposition has been put forward on the changes needed in existing sensor node structure to improve its efficiency and to facilitate as well as enhance the assimilation of CNT based devices in a WSN. Finally we have commented on the challenges that exist in this technology and described the important factors that need to be considered for calculating reliability. This research will help in practical implementation of CNT based devices and analysis of their key effects on the WSN environment. The work has been executed on Simulink and Distributive Computing toolbox of MATLAB.
The proposal has been compared to the recent developments and past experimental results reported in this field. This attempt to derive the energy consumption and reliability implications will help in development of real devices using CNT, which is a major hurdle in bringing the success from lab to commercial market. Recent research in CNT has been used to model an energy efficient model which will also lead to the development CAD tools. Library for Reliability and Energy consumption includes analysis of various parts of a WSN system which is being constructed from CNT. Nano routing in a CNT system is also implemented with its dependencies.
Finally the computations were executed on a HPC setup and the model showed remarkable speedup.
The combination of recent technological advances in electronics, nanotechnology, wireless communications, computing, and networking has hastened the development of Wireless Sensor Networks (WSNs) technology. Since CNT remains the main technology that threatens the CMOS technology due to its immense interesting properties our work has been to realize where the technology stands and the results of energy and reliability modeling. Wireless Sensor and Actor Networks (WSANs) constitute an emerging and pervasive technology that is attracting increased interest for a wide range of applications. WSN see application in various areas like space research, biomedical engineering, military applications such as battlefield surveillance and the quest for making low power, reliable and cheap sensor nodes has been a prime focus in recent years.
Nanotechnology has enabled realization of low power devices such as MEMS devices and CNT based FETs [11-12]. CNT based sensors have shown many benefits over their past counterparts and are suitable candidates in this Nanotechnology driven age [24]. Nanotechnology uses the smallest unit of matter to engineer new materials and devices atom by atom, aiming at achieving superior properties and performance through atomic scale architecture. An improvement in techniques of Nanocharacterization and Nano-fabrication has helped us to pave the way to develop many novel materials that can be applied to various spheres of technology. For example the impact of Nanotechnology on Wireless Communications has been shown by Er. Ping Li in [14]. An Architecture of Quantum-Based Nano-sensor Node for Future Wireless Sensor Networks has been proposed in [10]. WSN with Biomedical Applications has been shown by Zachary Walker describing the importance of Middleware [22]. Miniature Acoustic Communication Subsystem Architecture for Underwater Wireless Sensor Networks has been proposed by Saunvit Pandya [33]. WSN architecture for the Wireless Health Mobile Bio-diagnostic System for physiological studies has been proposed [34]. In our previous work, we have shown Nano based WSN where the importance of CNT and MEMS technology in WSN has been shown [32]. WSN plays a very important role in the overall development of a developing nation, which is being felt in the recent years [4]. Also planetary sensing applications have been proposed in recent years [6]. In this paper we have proposed energy and reliability models for a CNT based WSN. The models were developed using Simulink and Distributive Computing Toolbox, which were tested on a HPC setup.
CNT sensors and nano processors
Research on carbon nanotubes is yielding many results in labs and many theories are being proposed, but many parallel work on areas like reliability, packaging and energy constrains in CNT devices are still not being explained. Realization of CNT based sensors devices can make them a suitable candidate for WSN sensor nodes. Functional CNT can lead to novel device application giving advantages of their unique properties [25]. We know that conductance of CNT depends on the rolling of the graphene sheet which in turn depends upon the chiral vector Ch as given by the equation
Ch =na1 + ma2 (1)
Here n and m are integers and a1, a2 are unit vectors in the bi-dimensional hexagonal lattice of the graphene sheet. The radius of the nanotube being
R = a0(n2 + m2 + nm)1/2 /2π (2)
This is most basics idea of CNT that is known to all. Mathematics of CNT and their latest paper in this regard has been discussed in later part of the work. Hence we can model a sensor dependent on the above parameters as follows: 1. Define m, n and calculate the radius required for the particular sensor as electronic structure (energy band gap structure) depends on the integers m and n. 2. Take note of the impact of working temperature and environmental factors on the reactivity of CNT like hydrogenation, oxygenation, NO2, NH3, CO, O3 as studied in [24, 25]. 3. Effect of elasticity, mechanical motions and effect of other adsorbent on CNT surface. 4. Predicting the reliability of the sensor.
Fig. 1. Interaction of CNT and other molecules.
We know that variations in current conductance properties of CNT make it a useful for detecting gas and chemicals. We can illustrate the variation of current vs. time in a CNT based sensor from the graphs in [24]. The special semiconducting properties of CNTs have been exposited that makes them a suitable candidate for the future development of Nano-processors and Nano-scale circuitry [28, 30-31]. Atashbar et al. [37] has asserted that SWNT (Single Wall Nanotube) based efficient gas sensor using SWNT functionalized with Sodium Dodecyl Sulfate improved the solubility of SWNT in DI water significantly. He proposes that this functionalization reduces the short range attraction forces by introducing repulsive forces of equal strength and this result in the alteration of structural, electronic, and mechanical properties of the nanotubes. We are aware that there is a change in conductance of CNT on absorption of CO, NH3, CO, O3 NO2 and O2 and other gases [24]. Jing Li [18] has proposed a unique and marketable way to develop Nano-scale chemical sensors with polymer-coated CNTs for selective chemical sensing in gas phase. But we need more exploration in coating and doping techniques for broad application coverage. Carbon Nanotube also sees its very important application in biosensors [9, 13]. The main challenge for any engineering application of CNT is its reliability and interconnects. The effect of various gases has on CNT is shown in Fig. 1.
Modeling of low-bias electronic transport in ballistic conductor. There are various models developed for calculating the conductance of CNT. In this section the most applicable model is stated from the literature which is then implemented to calculate the energy consumption and reliability analysis. We know that Electronic transport in ballistic conductors can be assumed as the sum of IL and IR the currents flowing right and left; this forms the basic model of calculating current in CNT based devices where it is capable of carrying high currents [1]
(3)
(4)
Here D(E) is the density of states in units of (states/eV/nm), ν(E) is the electron velocity and f (E) is the Fermi function with Fermi levels EFR in the right lead and ELF in the left lead. These equations are simply expressing the fact that the current at energy E is the product of the number of charges ε D (E) f (E – EF) and their velocity ν(E). This is the current through metallic CNTs which forms the basis of the WSN network based on CNT. The total sum of right and left current in a ballistic system is thus
(5)
The difference of the Fermi functions implies that most of the current will flow between the two Fermi levels. There are two generalizations of the above derivation that need to be considered when describing transport through real systems. The first is that in general there may be several modes that contribute to the current, and each mode will contribute one quantum of conductance. The second point is that, because of scattering processes in the conductor, the electron transmission probability may be less than unity. Putting this together gives the final expressions for the current
(6)
And the total conductance (including spin) is given as
(7)
Thus this part of the system is the observation for conducting CNTs now in our work we have worked on the reliability and energy consumption as shown classically in [1].
CNTsensors. Sensors are the most important applications of CNTs. The sensing mechanism and the charge transfer needs to be modeled for the sensor part of the wireless sensor network. Using the expression for the capacitance per unit length of a CNT we can obtain an expression for the maximum relative change in conductance [27].
(8)
At the other end of the spectrum, one can consider the impact of a single analyte on the nanotube conductance. Under the assumption that the transferred charge is delocalized over the entire channel length, we can estimate the relative change in conductance to be
(9)
The appearance of the channel length is made explicit in this expression. This equation relates the conductance with channel length so it is an important part of parameter. For detection of analytes of concentration c in a gas or liquid phase, it is useful to relate the surface coverage θ to the analyte concentration. This can be accomplished by considering equilibrium surface coverage with analyte binding energy Eband analyte chemical potential in the gas or liquid μ. The partition function is then given by
(10)
Here zvib is the vibration contribution. The expression for the concentration dependence on the coverage can be combined with that for the threshold voltage shift, to obtain
(11)
Contacts and interconnect. The most important issue that is being talked about is contacts and interconnects in a CNT-based system. This research is also very useful for developing CNT based NOC which is an area of research for the near future. The presence of charge near the interface will change the electrostatic potential and hence the electrostatic potential in the semiconductor (z > 0) is calculated as [2]
(12)
The first term in this equation is the potential due to the image charge in the metal while the second and third terms arise from the charge in the semiconductor. This is the part which needs to be studied in light of reliability and energy consumption. Thus the electrostatic potential hence derived is
(13)
Thus we can see that Vbulk (z) is given above it where the potential attains a constant value at z>>q-1 which is
(14)
Diode of CNT systems. Diode as we see is one of the main things that are used in the circuitry which is being shown in this part. Assuming that the band edge simple tracks the Fermi levels in the leas (i.e. far away from the junction), the diode physics of CNT is shown as [3]
(15)
(16)
Here E∞ c is the energy of the conduction band edge on the n-type side far from the junction. As shown above it is the celebrated ideal diode equation describing rectifying behavior, except that here it was derived under the assumption of ballistic transport. This is important for considering the current in the diode part. I here is the celebrated ideal diode equation describing rectifying behavior, except that here it was derived under the assumption of ballistic transport.
Ohmic contacts and transistor. Temperature has been a major issue that needs to be taken in account. The temperature dependence of the ON state conductance also provides further evidence for the presence of ohmic contacts. Assuming perfect transmission through the contacts and the nanotube, the obtained temperature-dependent ON state conductance as [5]
(17)
Here Δ = EV − EF represents the position of the Fermi level in the valence band. The conductance G monotonically decreases with increasing temperature in agreement with the work done earlier. Thus, even in carbon nanotube field effect transistors without electron–phonon scattering it is expected that the conductance will decrease with increasing temperature, and can be reduced by as much as a factor of two at room temperature compared to its low- temperature value. Expression for conductance in a Schottky barrier nanotube transistors is [2, 7]
(18)
Here
(19)
Here tox is the gate oxide thickness. The much different physics behind the operation of Schottky barrier nanotube transistors has important implications on the scaling of various performances parameters with device dimensions. As discussed above, it was predicted that reducing the thickness of the gate insulator improves the sub threshold swing because it allows the gate to more effectively modulate the band-bending at the contact. Such a behavior has been verified experimentally by fabricating nanotube transistors with gate oxide thickness between 2 and 20nm. Thus this is the study of the CNT based transistor.
CNT electromechanical systems. Once the transmission probability is known for the relevant range of energies, the conductance is calculated from [3]
(20)
Here Tij is transmission probability between bands i and j. This equation signifies the relations of bending vs change in conductance. Impact of bending causes change in bond length which causes change in conductance which is shown. For the metallic nanotubes, a band-gap opens around the Fermi level, and the conductance at the Fermi level follows the relation [26]
(21)
Model for power consumed in a CNT-based WSN
Contemporary work in computation of WSN reliability is pretty generalized and Nano-scale devices based WSN has not been the sole focus of the research done in this area. In our previous work we have shown that MEMS reliability can be calculated using HPC thus making their practical applications possible [38, 37]. Effects of the failure of sensor nodes are studied and no compromise data acquisition methods have been proposed in [21]. Requirement for sustained, reliable and fault-tolerant operations have been conferred and a solution has been proposed by Kaminska in [15]. In this regard, the reliability calculations by probabilistic graph models and algorithm have been demonstrated by Hosam M. F. Abo El Fotoh [17]. Reliability studies in respect to Common Cause Failures have been examined [20]. Modeling and evaluating the reliability of Wireless Sensor Networks as subject to common cause failure has been described in [18]. Data transport and the reliability of data transport protocols have been discussed in [19]. Thus if we can predict the cause of failure then we can modify the protocols in our system accordingly. In Nano domains the failure can be caused due to large number of problems and errors which needs to be modeled and predicted in advance. Ad hoc wireless architecture has been introduced by Kamiska in [15] for the sustainability of self-configuring Wireless Sensor Networks and the routing scheme forwards sensor data along fuzzy and intentionally redundant paths to provide for reliability and fault-tolerance has been proposed. In [23] Zhand Dingxing discusses coverage algorithm based on probability to evaluate point coverage. Reliability in Wireless Sensor Networks using Soft Sensing and Artificial Neural Network methodology has been demonstrated by Rubina Sultan [21]. Optimizing availability and reliability in Wireless Sensor Networks applications by the use of middle wares has been shown in [16]. Thus we need to develop middleware in accordance with the challenges that exist. The CNT memory developed is not considered in our model [29, 35].
Current consumed in all elements can be distributed as follows: Ip = current in processor Is = current in sensor Id = current in diode I em =current in Electro mechanical CNT Idp = CNT diplay device if attached Icon = Contact of metal and CNT Itrs= current in transporting Energy consumed in V12 G1 + V22G2 + V32 G3 + V42G4 + V52G5
(also we need to take into account the Capacitance of the inter connects for the energy consumption: G1 is conductance in processor of FET based on CNT, here it’s a function of L (Length of nanotube) and energy consumed in capacitance: G2 is the conductance in sensor is also depended in L G3 diode does not depend on Length G4 current depends on deformation and bond length not L G5 It depends on L G6 does not depend on length; here capacitive effects may come into picture. Thus total energy consumption is a function of L length of the tube, length of the sensor tube, length of the display tube E = f (I, L1, L2, L3, Number of contacts,) (Assumed for diameter to be constant)
This can be done using MATLAB Distributive Computing Toolbox to calculate energy consumption at various temperatures so that we can model the circuit in the desired way. Using HPC needs to done in an optimum way by correct distribution of the jobs in the work load. Thus to calculate E we need the power of HPC to distribute the various parameters on an HPC setup because the system is complex. Capacitive effects on joining points or inter connects needs to be also taken in account which is ½ CV2. In most the cases it is can be considered in Diode and Contacts. If all of the system is just made of CNT then all the energy consumed in each part will be a function of CNTs parameter such. Our model results are done on HPC but an abstraction level function for overall consumption of the energy can be stated as
P= K1e-k 1 V (Energy used in circuit) + K21/2 CV2 (Energy lost at interconnect) (22)
Fig. 2. Y axis X axis, P vs. V(applied) for a derieved CNT WSN system.
Fig. 3. Graph between Power Consumed vs. Voltage applied vs. Capacitance of the system.
First term is of energy used in circuitry and 2nd term is for the interconnects. The variations can be seen in the figure plotted with the help of derived equation in Fig. 2 and taking all 3 parameters with number of inter connects, current, power the variations are shown in Fig. 3. It can therefore be used in an abstraction way as the formulae above but for accurate calculations we need high computation power, which can be done. Thus using this equation we can calculate the energy consumed in a CNT based Wireless Sensor network. But reliability and performance of the Node in a CNT based Sensor Node depends on the Sensor, Nano-processor, Nano-battery sources. Thus we need to make appropriate changes in the middle ware and Operating System. The energy for such a system can be derived from a MEMS better-less system where energy is induced in transponders, recent proposals shows real prospectus of such a technology maturing [8].
Unified reliability model developed for nano WSN
Probability function density of the failure for the device can be calculated as follows: f(t) is depended on frequency CNT device operate, electrostatic forces and electromagnetic forces it is subjected to, material which defines the strength of the device in various forms. υ= frequency of operation, also the frequency of CNT antenna will have a part in the function κ = stress it is subjected to, this is reaction RA and damping force as described earlier in case of a CNT mechanical sensors η = viscosity of the medium (which is most cases air) for a CNT mechanical sensor ЄC = electro static and effect, like capacitance based which for example shows at the interconnect of Carbon Nanotube metal junction ЄM=electromagnetic forces, like for inductive CNT antenna and transponders there is no mechanical motions but force due to inductance and induced voltages M (ρ, С, r) = material properties of the device, which also is the parameter of the density, head capacity, resistivity, strength and dielectric capabilities Ī=current flowing in the device for example which can be derived of various part of CNT based circuitry To=in some cases temperature may also be a cause of discrepancies which needs to be taken in account f(t) is a function of υ, κ, η, ЄC, ЄM, M (ρ, С, r), Ī,To
We have discussed the physics of these devices which is where we have given the current through various parts of the system.
It is assumed that the function will be exponential with some modification since it’s a standard reliability function used. It is obvious that f(t) will increase with υ, κ, η, ЄC, ЄM, M (ρ, С, r), Ī,To
As these parameters are linked to the failure rate therefore to insert their equivalent they are calculated by operator f’ where increase in any of them will increase the failure of the device, this operator converts the respective value to a function that needs to be inserted in the main failure probability distribution equation. Also it is obvious that the variation will have an exponential distribution for the failure rate distribution which can be derived from the basic principle of exponential distribution of reliability theory. So,
(23)
(24)
We have assumed we are given all parameters and they remain constant throughout the cycle of the CNT device then (f(υ) + f(κ) + f(η) + f(ЄC) + f(ЄM ) + f(M (ρ, С, r)) + f(Ī) + f(To)) = λ is assumed constant for computation that is being done on MATLAB. This formulation developed need to be modified as the exact dependencies of a case specific CNT device for example CNT based RFID or CNT based WSN. For example we need to derive for RFID which has CNT based antenna and transponders.
(25)
where I is the current through the system, n is the number of interconnects, and L is the approximate length of CNT used in the system. The failure probability distribution can be visualized as given in Fig. 4. And assuming only current and no interconnect effects the system behaves as shown in Fig. 5.
Fig. 4. Failure probability distribution f(t) vs I (current) and n (number of inter connects), which is the function derived.
Fig. 5. Failure rate function vs. I (current in the device) with no capacitive effects.
f(t)=( I + n+ L) e -(I + n+ L)(26)
The other model can be assumed in case the system is behaving in alternative way which is not satisfied by the first one can be seen in Fig. 6.
f(t)=( I n L) e -(I+ n L) (27)
Fig. 6. Failure probability distribution f(t) vs. I (current) and n (number of inter connects).
Thus for many CNT based devices in a WSN that are arranged in serious the probability of combined functioning can be calculated by the formulae below
(28)
A series system’s reliability decreases (increases) if the reliability of any unit decreases (increases). A series system’s reliability decreases (increases) if the number of units increases (decreases). A series system’s reliability is worse than the reliability of any of its units. Switching technology has been used very effectively and since at MEMS devices we have MEMS switches to shift to the redundant part of the system we need to analyze the reliability of the system with switch added. In a CNT system we can also use them. Psyst(t)=PSD(t)Pm(t) where Pm(t) is the probability of failure free operation of redundant group, PSD(t) of switching device and Psyst(t) of the system as a whole. A specific reliability function of switching device can be calculated in this way. Other miscellaneous issues that might come into factor are discussed below: Reliability of CNT depends on miscellaneous factors such as: 1. Functional group(s) attached, length and chirality of the CNT molecule 2. Packaging model used 3. Integration with other devices and interconnects 4. Other factors such as temperature and environmental parameters
Developed model for nano routing. Now we need to find a path in which the distance, the load at node (which is defined in terms of n which is also the number of connection is made) and the energy conservations to makes least energy dissipation for routine in a CNT based sensor network. Now this energy loss will be calculated by each node and then it will decide the path of propagation. Nanotechnology has enabled modeling of Nano antennas and MEMS technology enable transducers thus we can see that energy consumption can be greatly minimized. Propagation of waves is an independent area for a WSN and routine methodology formation can be worked as shown below: Here we have calculated the energy loss as a function of distance, load on the device, and the Voltage at which the CNT device is working. We know that Energy at a distance r is Er= k/r2 (this is the relation of the energy received at a distance r and k is assumed constant) by classical propagation theories. Now the energy loss is a function EL=f(lt,nl, Nf)
EL = k/l2t + nl + (K1e-k1V + K21/2 CV2), (29)
lt = length at which the transmission is to be made; Nf = Nano-factor for a CNT which is dependent on the energy conservation formulae derived in the earlier part, which is assumed as the energy conserved in the CNT as a function of Voltage which may be induced by MEMS transducers in this case; nl = load due to n devices at a node, which can also be said as the load complexity and also weighted value can be taken, in some cases it is the interference or the number of nodes surrounding the node that is a part of transmission or the interference nodes. It can also be stated as number of nodes in the path into average number of surrounding nodes (npath*navg). Also the relation can be studied graphically as EL vs. lt vs. V as shown below.
Fig. 7. Simulink Model of CNT based nano-WSN.
Model of the system in light of recent developments. We have shown that since CNTs which are used in many parts of the sensor nodes, Nano-processors [20], therefore, it is necessary to study the reliability and effect of various parameters on CNT based devices is the motivation behind the work. We have shown the importance of functional CNT and its realistic applications in chemical sensors and other Nano-electronic devices [36]. HPC can be useful for optimization of complex computations which has been shown in [39, 40]. The sensor software has to be modified for CNT specific computations and in case of detection of erroneous readings by the node in CNT based calculations; corrective measures are needed to be incorporated into the software to counter these readings. An algorithm 1 is the algorithm for the functioning of the sensor node:
Algorithm 1. Algorithm for the functioning of the sensor node.
Start Step 1: Input from CNT Sensors Step 2: ADC converters Step 3: Data sent to CNT based Nano-processors Step 4: Computation of data to study the reliability of the signal and the various aspects of occurrence of discrepancy in the readings of the functional CNT sensors End
Modifications needed in current Operating System for CNT based WSN: 1. Minimizing the inconsistency in the readings of CNT sensor nodes due to functional CNTs. 2. Inclusion of correction for the CNT based Nano battery source.
Modeling of CNT based devices in a WSN environment such as CNT Sensors, CNT electronics, CNT-based power sources can be done in this way. Since CNT is the main ingredient of devices, its reliability is of paramount importance. We have corroborated that the reliability of CNT-based sensor node depends upon functionalization of the CNT molecule, application, interconnects and packaging.
VHDL-AMS (VHSIC hardware description language Analog and Mixed-Signal extensions) modeling can be done as substantiated in [26]. The Simulink model shown in Fig. 7. is derived from the various parts of a WSN CNT that has been shown in part II. The programs given in Code 1 and Code 2 are the conversion of the complex mathematical equations into MLATAB format.
Code. 1. Demo code of above Simulink model.
function y = CNT_Sensor() % This block supports the Embedded MATLAB subset, it is used for calculation for equation 6 and equation 7in simulink. dgs =3; di=1g=1;e=1;alpha = 1;h=1;d=1;pi=3.14;k=1;T=1; L=1;epsi=1; dgs= g ( (pow(e,2) * alpha * ln((4*h)/d) ) / (2 * pi * epsi * k * T * ln(10)) ) * (1/L); di=dgs*V
Code. 2. Demo code of above Simulink model.
function y = CNT_Transistor() % This block supports the Embedded MATLAB subset, it is used for calculation for equation 16 and equation 17 in simulink. e = 2.7;h=1;V=1;Vg=1; G = ((4 * (e^2))/h) * (e ^ (-(1/3)* ((V/Vg)^2))); pi=3.14;Eg=1;alpha=1;gamma=1;tox=1; V = (((pi * (Eg^3))/(12 * alpha * gamma))^(1/2)) * (tox^(1/2));
Code. 3. Demo code for distribution of Computation.
Fig. 8. Configuration and status of the HPC setup.
The detailed implementation can be seen in [38]. The configuration of the status of the HPC setup is shown in Fig. 8. Due to symmetry of computation and distributions of various computations on the HPC setup, we got various results. When we fed the reliability computations we got maximum speedup (Fig. 9.), followed by I (current) in various (Fig. 10.), and the least in energy consumptions. Shown below are the speed-up results for various (Fig. 11) calculations that are performed on the HPC set-up in figure 9, 10 and 11. The explanation of the graphs can be predicted from the fact that symmetry of computation and limits used has the most effect on the speed-up. Virtual reality can also be used for taking the analysis in Virtual reality domain with the help of HPC setup.
Fig 9. Speed up which was obtained in reliability computations.
Fig. 10. Speedup in current (I) computations in various parts of the CNT based WSN.
Fig. 11. Minimum speed up obtained in energy consumptions in various parts of a CNT based WSN.
Conclusion
We have shown how novel nanotechnology-enabled devices can be used in a WSN environment. We have addressed the challenges that need to be confronted in CNT based WSN. We have substantiated integration of CNT based devices in WSN including sensors, micro processors, etc. We corroborated the challenges that exist on modeling of CNT based devices for a WSN sensor node and build a reliability model to accurately predict reliability. The modeling of CNT based nodes can be done in packages like Simulink in MATLAB which has been used in this work. We have derived a formulation for energy consumption of CNT based WSN system because energy is the main issue of concern secondly we have derived the reliability equation for the system. Developing a reliability, nano routing and reliability is helpful accelerating time to market for CNT based WSN. Routing plays an important role in CNT based devices where interconnects are very inefficient. Implementation has been done on an HPC setup and comparisons between various calculations have been reported. Control engineering can also play an important role in expanding the work, which is an area of future work. The transmission line equivalent needs to be modified as the new nano scale physics that is currently being developed which is introduced in this paper. To model such systems we have to use complex modeling aspects for which HPC is an eminent need.
Fig. 12. Energy consumption in Nano routing of a CNT based WSN.
Rohit Pathak, Acropolis Institute of Technology & Research, can be reached at [email protected].
Satyadhar Joshi, Shri Vaishnav Institute of Technology & Science, can be contacted at [email protected]
Originally published by Sensors & Transducers Journal (ISSN 1726-5479), Vol. 118, Issue 7, July 2010: Sensor Networks and Wireless Sensor Network. Rohit Pathak, Satyadhar Joshi, Modeling Energy & Reliability of a CNT based WSN on an HPC Setup, Sensors & Transducers, Vol. 118, Issue 7, July 2010, pp.28-45 (http://www.sensorsportal.com/HTML/DIGEST/P_640.htm).
(August 13, 2010) — Cell phones and a slew of emerging devices will power the market for consumer electronics and cell phone Microelectromechanical (MEMS)sensors toward solid, uninterrupted growth in 2010 and beyond, according to the market research firm iSuppli Corp.
Revenue for MEMS sensors and actuators used in consumer electronics and mobile handsets is projected to reach $1.5 billion in 2010, up a solid 22.9% from $1.3 billion last year. “Unlike most industries, the consumer and mobile MEMS market did not suffer a decline last year — even at the height of the global economic downturn — and growth ranging from 17% to as much as 28% will continue during the next four years,” said Jérémie Bouchaud, principal analyst for MEMS and sensors at iSuppli.
Revenue forecast for consumer and mobile MEMS, 2009-2014 (in Billions of U.S. Dollars)
The figure presents iSuppli’s forecast for the consumer and mobile MEMS market from 2009 to the end of the forecast period in 2014.
MEMS sensors and actuators are employed in a variety of additional sectors, including data processing — e.g., printers, projectors, copy machines — automotive, and other high-value markets embracing the industrial, medical, wired communications and aerospace-defense segments.
Nonetheless, consumer and mobile MEMS — already among the largest MEMS markets — are projected to become the biggest MEMS space by 2014. Here, sensors find their way into everyday devices such as laptops, MP3 players, remote controllers and portable navigation devices.
In particular, new consumer products will drive existing and future opportunities, iSuppli believes. The MEMS accelerometers and gyroscopes used for e-books and slate tablets like the iPad from Apple Inc will amount to $105 million in 2014, compared to an almost negligible $3 million in 2009.
Also helping spur expansion of the consumer and mobile MEMS market are various new emerging devices coming into fruition in 2010 and 2011. Among them are 3-axis gyroscopes, pico-projectors, and RF MEMS switches and varactors, iSuppli data show. All told, new MEMS devices will bring an additional $1.3 billion by 2014, up from a mere $33 million in 2009.
Cell phones, however, remain the dominant segment for consumer and mobile MEMS. In 2010, MEMS sensors and actuators in mobile handsets are forecasted to reach $821.4 million, making up 53.1% — well over half—of the market.
Cell phones will continue to be the largest user of consumer and mobile MEMS over the next few years, ahead of consumer projectors, laptops and hard disc drives, game controllers and digital still cameras.
Accelerometers remain the chief MEMS device, netting $557.1 million in revenue for 2010. While their use in gaming controllers and cell phones has either reached saturation or is close to doing so, accelerometers will increase their penetration in laptops and netbooks, and are sure to gain greater exposure in booming categories like e-books and slate tablets.
Other MEMS devices finishing strongly this year, in descending order, are gyroscopes, BAW filters, microphones and MEMS-based displays for projectors.
By 2014, MEMS displays will leap into third place, pushing microphones and BAW filters into the fourth and fifth spots, respectively.
Learn more about the MEMS market with the report from Bouchaud and Dixon, entitled: New Killer Products Keep Consumer MEMS Bubbling. For more information, visit www.isuppli.com.
Multiple configurations of the Probe System for Life.
Manual Device Characterization
150 mm Semiautomatic System
Optoelectronic Configuration
Double Sided Probe Configuration
(August 11, 2010) — SemiProbe has developed proprietary technologies awarded a patent by the United States Patent and Trademark Office. The Probe System for Life allows the company and users to configure test and inspection systems that meet unique requirements usually served by custom products.
For traditional test systems, Probe System for Life allows the user to configure the station with exactly what they need and can budget for instead of a set configuration prepared by a probe system vendor. For facilities with multiple users, the system also allows the user to reconfigure the station in minutes for another application.
In addition to wafer testing, the system has been configured to test laser bars, singulated die, packaged parts, microfluidic slides, as well as device testing in controlled environments such as vacuum, cryogenic and ultra-high temperature (1,000°C). Special applications such as Double Sided Probing, Back Side Probing, Simultaneous Double Sided Probing and Visual Inspection have all been engineered and are available as standard components.
The Probe System for Life System allows users to perpetually upgrade their system as their needs change and budgets grow. Systems can be field upgraded from manual, to semi and fully automatic operation, in addition to changes in wafer size capability, environment, optics and instrument integration. Specific accessories to make probing easier for a specific application can also be added quickly and easily.
Since introducing the Probe System for Life, Semiprobe has sold systems to customers on 5 continents. Users completing a field upgrade to increase wafer size capability or automation often save up to 70% over the traditional requirement to start with a new probe system, according to the company.
SemiProbe is a global supplier of innovative probing and inspection equipment for microelectronics, photovoltaics, optoelectronics, MEMS, biotechnology, chemistry, microfluidics, and nanotechnology. More information about SemiProbe may be found at http://www.semiprobe.com
(August 9, 2010) — Technologies used to project images have been relentlessly miniaturized over the past decade. As a result many companies are now actively looking into embedding these pico projectors into an ever-expanding range of consumer products, from cellphones to mobile TV, according to In-Stat. "Pico Projectors: One Reason Bigger Isn’t Better," provides worldwide market share and shipment forecasts of pico projector module adoption into a variety of mobile and hand held devices.
Forecast shipment growth of CE devices with embedded pico projector modules will increase to over 20 million devices by 2014, with mobile handsets share of that market moving from its current level of 15% to over 90% by 2014.
MEMS components are an enabling technology of pico projectors.
iSuppli stated that a bright spot in 2009 for MEMS devices was the pico projector (Read the analysis here.)
“Although the integration of ‘pico‘ projectors will occur across the entire CE device spectrum, the biggest push will come from the mobile handset segment,” says Frank Dickson, VP Mobile Internet Research. “The reality is that the mobile handset market is measured in billions, creating massive opportunities for component manufactures. For pico projectors, what makes it even more attractive is that the market is hypercompetitive, with manufacturers always aggressively looking to add new features to create differentiation. Pico projectors is definititely a significant differentiating feature.”
While stand-alone/accessory pico projectors (which plug into a device, such as a cell phone, iPod or laptop) dominate the market, there is clear movement from “plug-in” to “embedded.” Standalone/accessory projectors market share will decline from roughly 37% in 2010 to less than 4% over the five year forecast period.
While the number of personal consumer electronics leveraging pico projection will most certainly increase over the next five years, its overall share will reduce due to the size of the mobile handset market. The number of companies developing pico projectors for integration and/or as standalone products continues to expand, with 18 vendors now claiming to have the best technological solution including; 3M, ADM (aka Explay), bTendo, Digislide, Display Photonics Systems, Himax, Lite Blue Optics, Lite-On Technology, Maradin Technologies, Mezmeriz, Micron Displaytech, Microvision, Mirrorcle Technologies, Mitsumi Electric, Nippon Signal, Opus Microsystems, Syndiant, and Texas Instruments.
The recent In-Stat research, "Pico Projectors: One Reason Bigger Isn’t Better", #IN1004722WH, provides worldwide market share and shipment forecasts of pico projector module adoption into a variety of mobile and hand held devices including:
Handsets
Notebooks
Personal CE Products (Digital Cameras, Digital Camcorders, Digital Clocks)
This research is part of In-Stat’s Mobile & Computing Devices service, which provides analysis and forecasts of the market for mobile communications and computing devices, including cell phones, smartphones, MIDs, tablets, mini-notes/netbooks, and notebooks. In-Stat’s market intelligence combines technical, market and end-user research and database models to analyze the Mobile Internet and Digital Entertainment ecosystems.
Read more about electronics design, engineering, and manufacturing at www.electroIQ.com
(August 5, 2010) — IDTechEx announced "RFID Forecasts, Players & Opportunities 2011-2021," which shows RFID as a $5.63 billion market in 2010.
In 2010 the value of the entire RFID market will be $5.63 billion, up from $5.03 billion in 2009. This includes tags, readers and software/services for RFID cards, labels, fobs and all other form factors. $3.27 billion of the total $5.63 billion is spent on non-car-like structures: from RFID labels to active tags.
Figure. Total RFID market by territory 2011-2021 (2016 data shown). Source: IDTechEx 2010.
Which sectors are booming and which are under performing? This report examines each sector in turn. Those doing well in numbers sold are sometimes much less impressive in dollars taken and vice versa. Highly profitable ‘niche’ markets are analyzed. Major players now and in the future in the various parts of the value chain are identified and the big orders and milestones now and in the future are analysed. Of course, not everyone will want to serve the severely price constrained, highest volume markets are also considered. For them, IDTechEx examines many niches of at least one billion dollars potential that are emerging and many smaller opportunities where there is even less competition. They include: Passports in the face of new terrorism resulting in new laws; Livestock and food traceability in the face of new laws, bioterrorism, avian flu, BSE, fraud with subsidies etc.; Intermodal containers (Smart and Secure Tradelanes and other initiatives); Retail apparel; Healthcare; Those in prison and on parole; Ubiquitous Sensor Networks (USN), for warning of natural disasters, military and other purposes.
In retail, RFID is seeing rapid growth for apparel tagging; that application alone demands 300 million RFID labels in 2010. RFID in the form of tickets used for transit will demand 380 million tags in 2010. The tagging of animals (such as pigs, sheep and pets) is now substantial as it becomes a legal requirement in many more territories, with 178 million tags being used for this sector in 2010. This is happening in regions such as China and Australasia. In total, 2.31 billion tags will be sold in 2010 versus 1.98 billion in 2009. Most of that growth is from passive UHF RFID labels.
Analysis is broken out by market, including in-depth historic data. Over 200 companies are profiled in this report. We give detailed ten year forecasts of the volumes of tags required, their value and the total market value for the following market segments: Passive RFID: Drugs, Other Healthcare, Retail apparel, Consumer goods, Tires, Postal, Books, Manufacturing parts/tools, Archiving (documents/samples), Military, Retail CPG Pallet/case, Smart cards/payment key fobs, Smart tickets, Air baggage, Conveyances/Rollcages/ULD/Totes, Animals/Livestock, Vehicles, People (excluding other sectors), Passport page/secure documents, Other tag applications and Active RFID/battery-assisted: Pharma/Healthcare, Cold retail supply chain, Consumer goods, Postal, Manufacturing parts, tools, Archiving (samples), Military, Retail CPG Pallet/case, Shelf Edge Labels, Conveyances/Rollcages/ULD/Totes, Vehicles, People (excluding other sectors), Car clickers, Other tag applications. RFID is a growing market for MEMS devices.
RFID revenues are given separately by application type for 2005 to 2021, for both active and passive tags. Forecasts have taken into account the global economic slow down. Looking at the range of applications, the biggest projects, which tend to be government led are usually profitable for suppliers involved. For example, governments mandate tagging passports or cattle. Governments do not need a fast return on investment. In industry, RFID is being applied where it can demonstrate a fairly rapid return on investment.
Analysis includes detailed ten-year projections for EPC vs non-EPC, high-value niche markets, active vs passive, readers, standards, markets by frequency, markets by geographical region, label vs non label, chip vs chipless, markets by application, tag format and tag location. Cumulative sales of RFID are analyzed as are the major players and unmet opportunities. It covers the emergence of new products, legal and demand pressures and impediments for the years to come.
The report results from extensive research including interviews with RFID adopters and solution providers in the various applicational RFID markets, giving insight into the total RFID industry and what is really happening. Purchasers receive an electronic PDF and (optional) printed copy of this report, a separate functional spreadsheet of the forecasts, and access to report updates throughout the year. Learn more at www.IDTechEx.com/forecast
(August 3, 2010) — Murata Electronics North America debuted an ultra-thin waterproof piezoelectric speaker. With a thickness of 0.9mm, this 19.5 x 14.1mm speaker enables greater design freedom for the rapidly growing and evolving mobile market.
Specific speaker characteristics include an average sound pressure level of 92.0 ±3.0dB (1400Hz ±20%, 5Vrms sine wave, 10cm) and a capacitance of 0.9μF ±30%. The speaker achieves IPX7 grade waterproof protection without a costly waterproof acoustic membrane. Using just ordinary acoustic mesh and double sided tape to seal the speaker to the front cavity, this waterproof speaker application allows for decreased application costs, thin size, and good sound performance. The high torque nature of the speaker’s piezoelectric motor also makes it idea for operation in very small and thin back cavities where dynamic speakers have difficulties. As such, these features make the speaker ideal for mobile phones, music players, digital still cameras, digital video cameras, IC recorders, e-books and other mobile equipment.
There have been numerous indicators that demonstrate the growing trend towards waterproofing mobile equipment. For example, of the 50 new Japanese mobile phone models announced in late 2010, almost one in four were waterproof. “We developed this waterproof speaker based on feedback from our customers and market trends,” said Peter Tiller, senior group product manager, Murata Electronics North America. “Too often we hear of consumers losing a phone or camera due to accidental submersion in water. We hope our new speaker will allow more mobile consumer products to be waterproof and survive life’s little accidents.”
July 26, 2010 – The "Extreme Electronics" stage in the back corner of Moscone’s South Hall was packed all week long, offering discussions and presentations ranging from MEMS (see Pete’s writeup) to sensors to energy harvesting to flexible electronics. The talks we stood in on (no easy-access seats were available) were worth it.
MEMS had a big presence, both among exhibitors and the aforementioned presentation stage. Yole Développement sees a $6.5B MEMS device market in 2009 swelling to >$16B by 2015, and an even bigger surge in units: 3.2B in 2009, and 10B in 2015. iSuppli sees MEMS growing 11% this year to $6.B and expanding to $9.8B by 2014, a 10.7% CAGR; units will rise from 3.44B in 2009 to 4.14B in 2010, and 8.5B units by 2014 (a 19.5% CAGR). MEMS demand is so hot that even companies with internal MEMS fabs (e.g. Delphi, Conti) are exploring foundry sources, noted Yole’s Jean Christophe Eloy.
Better manufacturing technology for MEMS is pushing prices down, Eloy said. In 2000, accelerometers were 10mm2 in size, consumed 0.1mW, cost >$3.00, and were manufactured on 4-6in wafers. In 2010, devices are ~2-3mm2, made on 6-8in. wafers, consume 0.05mW, and cost $0.70. By 2020, MEMS devices will measure 1-2mm2, consume <0.05mW, cost <$0.4, and be manufactured mostly on 8-in. wafers (and will utilize 3D integration). "MEMS production is back on the fast track," said Jérémie Bouchaud, director and principal analyst for MEMS and sensors at iSuppli."
Also fueling growth in MEMS is applications for consumer electronic devices and mobile handsets, which "bulldozed their way through the economic crisis," Bouchaud said. Inkjet printers will stay the dominant-selling MEMS device through 2014, ending the period with $2B/year.
High-brightness LEDs held the "Extreme Electronics" stage for every slot on Wednesday, reflecting that sector’s growing interest from semiconductor firms and suppliers seeking yet another new high-growth business. (HB-LED processing is something that suppliers will need to better understand, pointed out one industry watcher. E.g. wafers can sit up to half a day in a chamber vs. typical tool-to-tool flows for semiconductor manufacturing. And sapphire wafers are about to get much bigger — think 300mm.)
And of course everything Intersolar was right next door in the West Hall (exhibits) and Intercontinental (sessions), where traffic was even heavier (it barely thinned out as you went to the top of the three exhibit floors.) One question we heard, though, somewhat rhetorically: What happens if (when?) the solar side gets any bigger? How many solar panel demos can you fit in one expo center? We’ve heard Intersolar and SEMI remain committed to having a colocated show, so the question will be how to give Intersolar enough room to flex its muscles.
Semiconductors are everywhere
Bernie Meyerson’s Tuesday keynote identified high-level real-world applications where enabling technologies can make a fundamental difference in people’s lives, from managing urban traffic to pre-diagnosing sudden onset of diseases. At a SST-hosted breakfast on Wednesday (July 14), Andrew Thompson of Proteus Biomedical, developer of "intelligent" pharmaceutical devices that can be swallowed to monitor and relay body functions, preached for the marriage of information and technology and medicine. Among the planet’s 6-7B humans, there are roughly 5B cell phones in use — while only 3B people have shoes, he said. And the Internet reaches more people than water or electricity — it’s the world’s most important utility. (His grandmother witnessed the invention of everything from flight to refrigerators to TVs, he said, so surely we can come up with something.)
But it’s getting the message across to the masses (and influencers) outside our industry that’s the next big goal. We heard several times that the semiconductor industry (and tech in general) needs charismatic, intelligent advocacy to help Wall Street really understand the broad impact and potential of how what we do.
But eager ears are certainly out there — and maybe in surprising places. At our hotel this year, the concierge surprised us by revealing quite a bit more than a passing knowledge. Turns out he’s a U.Penn-pedigreed Ph.D — patented, with ahandful of publishedpapers — with a wide background in everything from narrow bandgap semiconductors to IR detectors and sensors to solar panels and nickel-hydride batteries. He’s still tracking what goes on in the industry, and has a keen interest to get back into the game after a hiatus. We’ve got his contact information if anyone’s interested.
July 23, 2010 – With the US reeling from record unemployment, many are wondering how the MEMS job market is fairing. Jason Weigold, founder and president of MEMStaff, shared his observations based on clients’ hiring and consulting needs. With 15 years of MEMS engineering experience and 4 years of MEMS staffing experience, Weigold is able to offer a unique perspective.
When MEMS industry was in its infancy, companies developing MEMS products hired top engineers from the traditional semiconductor industry because few experienced MEMS engineers existed. These employees leveraged their prior high-volume background coupled with general problem solving skills to address MEMS hurdles.
Since then, multiple companies have successfully commercialized MEMS devices. Examples include Texas Instruments, Analog Devices, Bosch, Freescale, ST Microelectronics, and Knowles, who collectively have shipped over 4 billion MEMS devices. Weigold estimates that the experienced, directly MEMS-related talent pool currently employed at these companies alone is over 1500 people.
Now that multiple companies have successfully brought high-volume MEMS devices to market, MEMS employers can demand more specific MEMS experience. "Any good semiconductor engineer who encounters problems in MEMS can determine a root cause and find a solution," Weigold says. "However, there exist people today who have who have already encountered and worked through many MEMS problems, and can therefore foresee their occurrence, and take steps to prevent them from ever occurring. This is essential to companies encountering shrinking market windows with increased competition, and those trying to achieve milestones in a timely manner for continued investment."
According to Weigold, MEMS hiring has definitely increased compared to a year ago, with companies seeking talent with experience in a specific product — prior work with MEMS RF devices or gyroscopes, for example. Solid MEMS design experience & MEMS process integration skills have been in demand, especially those with DRIE and wafer bonding know-how. Those with test and packaging experience are also at an advantage. Employers are actively seeking those who have already seen a MEMS product successfully commercialized. A large number of strong MEMS engineers being hired are not US citizens, in part because the American pool to choose from is already taken. This raises questions as to why American talent is not pursuing careers in engineering. In addition, there appears to be a current surplus of middle management and fresh graduates in the MEMS field.
Weigold will be sharing additional insight and best practices on how to acquire MEMS talent at the 2010 Commercialization of Micro-Nano Systems (COMS) Conference in Albuquerque, New Mexico, at the end of August. From his perspective, "acquisition and retention of top employees is essential in a field seeing increased competition, more stringent specifications, lower selling prices, and tightening timelines."
Neha K. Choksi is an independent consultant based in Mountain View, CA. She has worked for a variety of MEMS companies including as director of product engineering at Silicon Microstructures and as a consultant focusing on commercialization and high-volume production of MEMS devices. E-mail: Choksi [at] gmail.
(July 15, 2010) — In response to requests from R&D and academic communities, ElectroChemical Systems, Inc. (ECSI) FIBRotools is introducing two products for development of advanced MEMS/nano and high-density interconnect (HDI) fabrication technologies: IKoCLASSIC-SL and IKoCLASSIC-MG.
Advanced MEMS, nano, and HDI fabrication requires fewer processing steps and improved efficiency and product quality, noted ECSI. The IKoCLASSIC-SL enables electroplating of micro and nano-structures on doped silicon (Si) substrates without the seed layer as a precursor.
Actuators, field generators and micro-motors call for MEMS and nano structures with magnetic capability. IKoCLASSIC-MG enables electroplating of such structures under a magnetic field.
