Force-sensing resistor

Last updated

A force-sensing resistor is a material whose resistance changes when a force, pressure or mechanical stress is applied. They are also known as force-sensitive resistor and are sometimes referred to by the initialism FSR. [1]

Contents

FSR usage

History

The technology of force-sensing resistors was invented and patented in 1977 by Franklin Eventoff. In 1985 Eventoff founded Interlink Electronics, [2] a company based on his force-sensing-resistor (FSR). In 1987, Eventoff received the prestigious International IR 100 award for developing the FSR. In 2001 Eventoff founded a new company, Sensitronics, [3] that he currently runs. [4]

Properties

Force-sensing resistors consist of a conductive polymer, which predictably changes resistance following applying force to its surface. [5] They are normally supplied as a polymer sheet or ink that can be applied by screen printing. The sensing film consists of electrically conducting and non-conducting particles suspended in a matrix. The particles are sub-micrometre sizes formulated to reduce temperature dependence, improve mechanical properties and increase surface durability. Applying a force to the surface of the sensing film causes particles to touch the conducting electrodes, changing the film's resistance. As with all resistive-based sensors, force-sensing resistors require a relatively simple interface and can operate satisfactorily in moderately hostile environments. Compared to other force sensors, the advantages of FSRs are their size (thickness typically less than 0.5 mm), low cost, and good shock resistance. A disadvantage is their low precision: measurement results may differ by 10% and more. Force-sensing capacitors offer superior sensitivity and long-term stability, but require more complicated drive electronics.

Operation principle of FSRs

There are two major operation principles in force-sensing resistors: percolation and quantum tunneling. Although both phenomena co-occur in the conductive polymer, one phenomenon dominates over the other depending on particle concentration. [6] Particle concentration is also referred in the literature as the filler volume fraction . [7] More recently, new mechanistic explanations have been established to explain the performance of force-sensing resistors; these are based on the property of contact resistance occurring between the sensor electrodes and the conductive polymer. Specifically the force induced transition from Sharvin contacts to conventional Holm contacts. [8] The contact resistance, , plays an important role in the current conduction of force-sensing resistors in a twofold manner. First, for a given applied stress , or force , a plastic deformation occurs between the sensor electrodes and the polymer particles thus reducing the contact resistance. [9] [10] Second, the uneven polymer surface is flattened when subjected to incremental forces, and therefore, more contact paths are created; this causes an increment in the effective Area for current conduction . [10] At a macroscopic scale, the polymer surface is smooth. However, under a scanning electron microscope, the conductive polymer is irregular due to agglomerations of the polymeric binder. [11]

To date, no comprehensive model is capable of predicting all the non-linearities observed in force-sensing resistors. The multiple phenomena occurring in the conductive polymer turn out to be too complex such to embrace them all simultaneously; this condition is typical of systems encompassed within condensed matter physics. However, in most cases, the experimental behavior of force-sensing resistors can be grossly approximated to either the percolation theory or to the equations governing quantum tunneling through a rectangular potential barrier.

Percolation in FSRs

The percolation phenomenon dominates in the conductive polymer when the particle concentration is above the percolation threshold . A force-sensing resistor operating based on percolation exhibits a positive coefficient of pressure, and therefore, an increment in the applied pressure causes an increment in the electrical resistance , [12] [13] For a given applied stress , the electrical resistivity of the conductive polymer can be computed from: [14]

where matches for a prefactor depending on the transport properties of the conductive polymer, and is the critical conductivity exponent. [15] Under percolation regime, the particles are separated from each other when mechanical stress is applied; this causes a net increment in the device's resistance.

Quantum tunneling in FSRs

Quantum tunneling is the most common operation mode of force-sensing resistors. A conductive polymer operating on the basis of quantum tunneling exhibits a resistance decrement for incremental values of stress . Commercial FSRs such as the FlexiForce, [16] Interlink [17] and Peratech [18] sensors operate based on quantum tunneling. The Peratech sensors are also referred to in the literature as quantum tunnelling composite.

The quantum tunneling operation implies that the average inter-particle separation is reduced when the conductive polymer is subjected to mechanical stress; such a reduction in causes a probability increment for particle transmission according to the equations for a rectangular potential barrier. [19] Similarly, the contact resistance is reduced amid larger applied forces. To operate based on quantum tunneling, particle concentration in the conductive polymer must be held below the percolation threshold . [6]

Several authors have developed theoretical models for the quantum tunneling conduction of FSRs, [20] [21] some of the models rely upon the equations for particle transmission across a rectangular potential barrier. However, the practical usage of such equations is limited because they are stated in terms of electron energy, , that follows a Fermi Dirac probability distribution, i.e., electron energy is not a priori determined or can not be set by the final user. The analytical derivation of the equations for a rectangular potential barrier including the Fermi Dirac distribution was found in the 60`s by Simmons. [22] Such equations relate the current density with the external applied voltage across the sensor . However, is not straightforward measurable in practice, so the transformation is usually applied in literature when dealing with FSRs.

Just as in the equations for a rectangular potential barrier, the Simmons' equations are piecewise regarding the magnitude of , i.e., different expressions are stated depending on and the height of the rectangular potential barrier . The simplest Simmons' equation [22] relates with , when as next:

where is in units of electron volt, , are the electron's mass and charge respectively, and is the Planck constant. The low voltage equation of the Simmons' model [22] is fundamental for modeling the current conduction of FSRs. The most widely accepted model for tunneling conduction has been proposed by Zhang et al. [23] based on such equation. By re-arranging the equation above, it is possible to obtain an expression for the conductive polymer resistance , where is given by the quotient according to the Ohm's law:

When the conductive polymer is fully unloaded, the following relationship can be stated between the inter-particle separation at rest state ,the filler volume fraction and particle diameter :

Similarly, the following relationship can be stated between the inter-particle separation and stress

where is the Young's modulus of the conductive polymer. Finally, by combining all the equations above, the Zhang's model [23] is obtained as next:

Although the model from Zhang et al. has been widely accepted by many authors, [11] [9] it has been unable to predict some experimental observations reported in force-sensing resistors. Probably, the most challenging phenomenon to predict is sensitivity degradation. When subjected to dynamic loading, some force-sensing resistors exhibit degradation in sensitivity. [24] [25] Up to date, a physical explanation for such a phenomenon has not been provided, but experimental observations and more complex modeling from some authors have demonstrated that sensitivity degradation is a voltage-related phenomenon that can be avoided by choosing an appropriate driving voltage in the experimental set-up. [26]

The model proposed by Paredes-Madrid et al. [10] uses the entire set of Simmons' equations [22] and embraces the contact resistance within the model; this implies that the externally applied voltage to the sensor is split between the tunneling voltage and the voltage drop across the contact resistance as next:

By replacing sensor current in the above expression, can be stated as a function of the contact resistance and as next:

and the contact resistance is given by:

where is the resistance of the conductive nano-particles and , are experimentally determined factors that depend on the interface material between the conductive polymer and the electrode. Finally the expressions relating sensor current with are piecewise functions just as the Simmons equations [22] are:

When

When

When

In the equations above, the effective area for tunneling conduction is stated as an increasing function dependent on the applied stress , and on coefficients , , to be experimentally determined. This formulation accounts for the increment in the number of conduction paths with stress:

Although the above model [10] is unable to describe the undesired phenomenon of sensitivity degradation, the inclusion of rheological models has predicted that drift can be reduced by choosing an appropriate sourcing voltage; experimental observations have supported this statement. [26] Another approach to reduce drift is to employ Non-aligned electrodes to minimize the effects of polymer creep. [27] There is currently a great effort placed on improving the performance of FSRs with multiple different approaches: in-depth modeling of such devices in order to choose the most adequate driving circuit, [26] changing the electrode configuration to minimize drift and/or hysteresis, [27] investigating on new materials type such as carbon nanotubes, [28] or solutions combining the aforesaid methods.

Uses

Force-sensing resistors are commonly used to create pressure-sensing "buttons" and have applications in many fields, including musical instruments (such as the Sensel Morph), car occupancy sensors, artificial limbs, foot pronation systems, and portable electronics. They are also used in mixed or augmented reality systems [29] as well as to enhance mobile interaction. [30] [31]

See also

Related Research Articles

<span class="mw-page-title-main">Normal distribution</span> Probability distribution

In statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is

<span class="mw-page-title-main">Pauli matrices</span> Matrices important in quantum mechanics and the study of spin

In mathematical physics and mathematics, the Pauli matrices are a set of three 2 × 2 complex matrices that are Hermitian, involutory and unitary. Usually indicated by the Greek letter sigma, they are occasionally denoted by tau when used in connection with isospin symmetries.

In physics, a Langevin equation is a stochastic differential equation describing how a system evolves when subjected to a combination of deterministic and fluctuating ("random") forces. The dependent variables in a Langevin equation typically are collective (macroscopic) variables changing only slowly in comparison to the other (microscopic) variables of the system. The fast (microscopic) variables are responsible for the stochastic nature of the Langevin equation. One application is to Brownian motion, which models the fluctuating motion of a small particle in a fluid.

In mathematics, a Gaussian function, often simply referred to as a Gaussian, is a function of the base form

A Newtonian fluid is a fluid in which the viscous stresses arising from its flow are at every point linearly correlated to the local strain rate — the rate of change of its deformation over time. Stresses are proportional to the rate of change of the fluid's velocity vector.

<span class="mw-page-title-main">Surface energy</span> Excess energy at the surface of a material relative to its interior

In surface science, surface energy quantifies the disruption of intermolecular bonds that occurs when a surface is created. In solid-state physics, surfaces must be intrinsically less energetically favorable than the bulk of the material, otherwise there would be a driving force for surfaces to be created, removing the bulk of the material. The surface energy may therefore be defined as the excess energy at the surface of a material compared to the bulk, or it is the work required to build an area of a particular surface. Another way to view the surface energy is to relate it to the work required to cut a bulk sample, creating two surfaces. There is "excess energy" as a result of the now-incomplete, unrealized bonding between the two created surfaces.

The Feynman–Kac formula, named after Richard Feynman and Mark Kac, establishes a link between parabolic partial differential equations (PDEs) and stochastic processes. In 1947, when Kac and Feynman were both Cornell faculty, Kac attended a presentation of Feynman's and remarked that the two of them were working on the same thing from different directions. The Feynman–Kac formula resulted, which proves rigorously the real-valued case of Feynman's path integrals. The complex case, which occurs when a particle's spin is included, is still an open question.

In probability theory and statistics, the generalized extreme value (GEV) distribution is a family of continuous probability distributions developed within extreme value theory to combine the Gumbel, Fréchet and Weibull families also known as type I, II and III extreme value distributions. By the extreme value theorem the GEV distribution is the only possible limit distribution of properly normalized maxima of a sequence of independent and identically distributed random variables. Note that a limit distribution needs to exist, which requires regularity conditions on the tail of the distribution. Despite this, the GEV distribution is often used as an approximation to model the maxima of long (finite) sequences of random variables.

<span class="mw-page-title-main">Sheet resistance</span> Electrical resistance of a thin film

Sheet resistance, is the resistance of a square piece of a thin material with contacts made to two opposite sides of the square. It is usually a measurement of electrical resistance of thin films that are uniform in thickness. It is commonly used to characterize materials made by semiconductor doping, metal deposition, resistive paste printing, and glass coating. Examples of these processes are: doped semiconductor regions, and the resistors that are screen printed onto the substrates of thick-film hybrid microcircuits.

The piezoresistive effect is a change in the electrical resistivity of a semiconductor or metal when mechanical strain is applied. In contrast to the piezoelectric effect, the piezoresistive effect causes a change only in electrical resistance, not in electric potential.

Machine olfaction is the automated simulation of the sense of smell. An emerging application in modern engineering, it involves the use of robots or other automated systems to analyze air-borne chemicals. Such an apparatus is often called an electronic nose or e-nose. The development of machine olfaction is complicated by the fact that e-nose devices to date have responded to a limited number of chemicals, whereas odors are produced by unique sets of odorant compounds. The technology, though still in the early stages of development, promises many applications, such as: quality control in food processing, detection and diagnosis in medicine, detection of drugs, explosives and other dangerous or illegal substances, disaster response, and environmental monitoring.

In statistics, the multivariate t-distribution is a multivariate probability distribution. It is a generalization to random vectors of the Student's t-distribution, which is a distribution applicable to univariate random variables. While the case of a random matrix could be treated within this structure, the matrix t-distribution is distinct and makes particular use of the matrix structure.

<span class="mw-page-title-main">Shallow water equations</span> Set of partial differential equations that describe the flow below a pressure surface in a fluid

The shallow-water equations (SWE) are a set of hyperbolic partial differential equations that describe the flow below a pressure surface in a fluid. The shallow-water equations in unidirectional form are also called Saint-Venant equations, after Adhémar Jean Claude Barré de Saint-Venant.

<span class="mw-page-title-main">Half-normal distribution</span> Probability distribution

In probability theory and statistics, the half-normal distribution is a special case of the folded normal distribution.

In quantum field theory, a non-topological soliton (NTS) is a soliton field configuration possessing, contrary to a topological one, a conserved Noether charge and stable against transformation into usual particles of this field for the following reason. For fixed charge Q, the mass sum of Q free particles exceeds the energy (mass) of the NTS so that the latter is energetically favorable to exist.

In statistics, the Matérn covariance, also called the Matérn kernel, is a covariance function used in spatial statistics, geostatistics, machine learning, image analysis, and other applications of multivariate statistical analysis on metric spaces. It is named after the Swedish forestry statistician Bertil Matérn. It specifies the covariance between two measurements as a function of the distance between the points at which they are taken. Since the covariance only depends on distances between points, it is stationary. If the distance is Euclidean distance, the Matérn covariance is also isotropic.

<span class="mw-page-title-main">Stokes' theorem</span> Theorem in vector calculus

Stokes' theorem, also known as the Kelvin–Stokes theorem after Lord Kelvin and George Stokes, the fundamental theorem for curls or simply the curl theorem, is a theorem in vector calculus on . Given a vector field, the theorem relates the integral of the curl of the vector field over some surface, to the line integral of the vector field around the boundary of the surface. The classical theorem of Stokes can be stated in one sentence: The line integral of a vector field over a loop is equal to the surface integral of its curl over the enclosed surface. It is illustrated in the figure, where the direction of positive circulation of the bounding contour ∂Σ, and the direction n of positive flux through the surface Σ, are related by a right-hand-rule. For the right hand the fingers circulate along ∂Σ and the thumb is directed along n.

Conductivity near the percolation threshold in physics, occurs in a mixture between a dielectric and a metallic component. The conductivity and the dielectric constant of this mixture show a critical behavior if the fraction of the metallic component reaches the percolation threshold.

For certain applications in linear algebra, it is useful to know properties of the probability distribution of the largest eigenvalue of a finite sum of random matrices. Suppose is a finite sequence of random matrices. Analogous to the well-known Chernoff bound for sums of scalars, a bound on the following is sought for a given parameter t:

Charge transport mechanisms are theoretical models that aim to quantitatively describe the electric current flow through a given medium.

References

  1. FSR Definitions
  2. "Interlink Electronics".
  3. Physics and Radio-Electronics. "Force Sensitive Resistor".
  4. Sensitronics
  5. "Tactile Sensors". Archived from the original on April 24, 2001.
  6. 1 2 Stassi, S; Cauda, V; Canavese, G; Pirri, C (March 14, 2014). "Flexible Tactile Sensing Based on Piezoresistive Composites: A Review". Sensors. 14 (3): 5296–5332. Bibcode:2014Senso..14.5296S. doi: 10.3390/s140305296 . PMC   4003994 . PMID   24638126.
  7. Bloor, D; Donnelly, K; Hands, P; Laughlin, P; Lussey, D (August 5, 2005). "A metal-polymer composite with unusual properties" (PDF). Journal of Physics D. 38 (16): 2851. Bibcode:2005JPhD...38.2851B. doi:10.1088/0022-3727/38/16/018. hdl: 20.500.11820/53811f2f-2093-43a7-9f35-854338273c94 . S2CID   84833095.
  8. Mikrajuddin, A; Shi, F; Kim, H; Okuyama, K (April 24, 2000). "Size-dependent electrical constriction resistance for contacts of arbitrary size: from Sharvin to Holm limits". Materials Science in Semiconductor Processing. 2 (4): 321–327. doi:10.1016/S1369-8001(99)00036-0.
  9. 1 2 Kalantari, M; Dargahi, J; Kovecses, J; Mardasi, M; Nouri, S (2012). "A New Approach for Modeling Piezoresistive Force Sensors Based on Semiconductive Polymer Composites" (PDF). IEEE/ASME Transactions on Mechatronics. 17 (3): 572–581. doi:10.1109/TMECH.2011.2108664. S2CID   44667583.
  10. 1 2 3 4 Paredes-Madrid, L; Palacio, C; Matute, A; Parra, C (September 14, 2017). "Underlying Physics of Conductive Polymer Composites and Force Sensing Resistors (FSRs) under Static Loading Conditions". Sensors. 17 (9): 2108. Bibcode:2017Senso..17.2108P. doi: 10.3390/s17092108 . PMC   5621037 . PMID   28906467.
  11. 1 2 Wang, L; Ding, T; Wang, P (June 30, 2009). "Influence of carbon black concentration on piezoresistivity for carbon-black-filled silicone rubber composite". Carbon. 47 (14): 3151–3157. Bibcode:2009Carbo..47.3151L. doi:10.1016/j.carbon.2009.06.050.
  12. Knite, M; Teteris, V; Kiploka, A; Kaupuzs, J (August 15, 2003). "Polyisoprene-carbon black nanocomposites as tensile strain and pressure sensor materials". Sensors and Actuators A: Physical. 110 (1–3): 142–149. doi:10.1016/j.sna.2003.08.006.
  13. Yi, H; Dongrui, W; Xiao-Man, Z; Hang, Z; Jun-Wei, Z; Zhi-Min, D (October 24, 2012). "Positive piezoresistive behavior of electrically conductive alkyl-functionalized graphene/polydimethylsilicone nanocomposites". J. Mater. Chem. C. 1 (3): 515–521. doi:10.1039/C2TC00114D.
  14. Basta, M; Picciarelli, V; Stella, R (October 1, 1993). "An introduction to percolation". European Journal of Physics. 15 (3): 97–101. Bibcode:1994EJPh...15...97B. doi:10.1088/0143-0807/15/3/001. S2CID   250782773.
  15. Zhou, J; Song, Y; Zheng, Q; Wu, Q; Zhang, M (February 2, 2008). "Percolation transition and hydrostatic piezoresistance for carbon black filled poly(methylvinylsilioaxne) vulcanizates". Carbon. 46 (4): 679–691. Bibcode:2008Carbo..46..679Z. doi:10.1016/j.carbon.2008.01.028.
  16. Tekscan, Inc. "FlexiForce, Standard Force \& Load Sensors Model A201. Datasheet" (PDF).
  17. Interlink Electronics. "FSR400 Series Datasheet" (PDF).
  18. Peratech, Inc. "QTC SP200 Series Datasheet. Single Point Sensors" (PDF).
  19. Canavese, G; Stassi, S; Fallauto, C; Corbellini, S; Cauda, V (June 23, 2013). "Piezoresistive flexible composite for robotic tactile applications". Sensors and Actuators A: Physical. 208: 1–9. doi:10.1016/j.sna.2013.11.018. S2CID   109604106.
  20. Li, C; Thostenson, E; Chou, T-W (November 29, 2007). "Dominant role of tunneling resistance in the electrical conductivity of carbon nanotube–based composites". Applied Physics Letters. 91 (22): 223114. Bibcode:2007ApPhL..91v3114L. doi:10.1063/1.2819690.
  21. Lantada, A; Lafont, P; Muñoz, J; Munoz-Guijosa, J; Echavarri, J (September 16, 2010). "Quantum tunnelling composites: Characterisation and modelling to promote their applications as sensors". Sensors and Actuators A: Physical. 164 (1–2): 46–57. doi:10.1016/j.sna.2010.09.002.
  22. 1 2 3 4 5 Simmons, J (1963). "Electrical tunnel effect between dissimilar electrodes separated by a thin insulating Film". Journal of Applied Physics. 34 (9): 2581–2590. Bibcode:1963JAP....34.2581S. doi:10.1063/1.1729774.
  23. 1 2 Xiang-Wu, Z; Yi, P; Qiang, Z; Xiao-Su, Y (September 8, 2000). "Time dependence of piezoresistance for the conductor-filled polymer composites". Journal of Polymer Science Part B: Polymer Physics. 38 (21): 2739–2749. Bibcode:2000JPoSB..38.2739Z. doi:10.1002/1099-0488(20001101)38:21<2739::AID-POLB40>3.0.CO;2-O.
  24. Lebosse, C; Renaud, P; Bayle, B; Mathelin, M (2011). "Modeling and Evaluation of Low-Cost Force Sensors". IEEE Transactions on Robotics. 27 (4): 815–822. doi:10.1109/TRO.2011.2119850. S2CID   14491353.
  25. Lin, L; Liu, S; Zhang, Q; Li, X; Ji, M; Deng, H; Fu, Q (2013). "Towards Tunable Sensitivity of Electrical Property to Strain for Conductive Polymer Composites Based on Thermoplastic Elastomer". ACS Applied Materials & Interfaces. 5 (12): 5815–5824. doi:10.1021/am401402x. PMID   23713404.
  26. 1 2 3 Paredes-Madrid, L; Matute, A; Bareño, J; Parra, C; Gutierrez, E (November 21, 2017). "Underlying Physics of Conductive Polymer Composites and Force Sensing Resistors (FSRs). A Study on Creep Response and Dynamic Loading". Materials. 10 (11): 1334. Bibcode:2017Mate...10.1334P. doi: 10.3390/ma10111334 . PMC   5706281 . PMID   29160834.
  27. 1 2 Wang, L; Han, Y; Wu, C; Huang, Y (June 7, 2013). "A solution to reduce the time dependence of the output resistance of a viscoelastic and piezoresistive element". Smart Materials and Structures. 22 (7): 075021. Bibcode:2013SMaS...22g5021W. doi:10.1088/0964-1726/22/7/075021. S2CID   108446573.
  28. Cao, X; Wei, X; Li, G; Hu, C; Dai, K (March 10, 2017). "Strain sensing behaviors of epoxy nanocomposites with carbon nanotubes under cyclic deformation". Polymer. 112: 1–9. doi:10.1016/j.polymer.2017.01.068.
  29. Issartel, Paul; Besancon, Lonni; Isenberg, Tobias; Ammi, Mehdi (2016). "A Tangible Volume for Portable 3D Interaction". 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct). IEEE. pp. 215–220. arXiv: 1603.02642 . doi:10.1109/ismar-adjunct.2016.0079. ISBN   978-1-5090-3740-7.
  30. Besançon, Lonni; Ammi, Mehdi; Isenberg, Tobias (2017). "Pressure-Based Gain Factor Control for Mobile 3D Interaction using Locally-Coupled Devices". Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. New York, New York, USA: ACM Press. pp. 1831–1842. doi:10.1145/3025453.3025890. ISBN   978-1-4503-4655-9.
  31. McLachlan, Ross; Brewster, Stephen (2015). "Bimanual Input for Tablet Devices with Pressure and Multi-Touch Gestures". Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services. New York, New York, USA: ACM Press. pp. 547–556. doi:10.1145/2785830.2785878. ISBN   978-1-4503-3652-9.