Magnetic Field Effects on Carrier Transport

  • Arokia Nathan
  • Henry Baltes
Part of the Computational Microelectronics book series (COMPUTATIONAL)


The domain of magnetic signals ranges from the very weak biomagnetic fields (∼ 10 fT) to the very high fields associated with superconducting coils (∼ 10 T) (see [1–6]). As a measure of the field strength H, we use the related magnetic induction B whose unit is 1 tesla = 1 Vs/m2 and is related to the field strength as: B = μ0 H in vacuum, where μ0 is the free space permeability. In this very large span of over 15 orders of magnitude in field strength, the lower limit of field strengths (< 1 μT) requires relatively sophisticated detection devices and techniques [4], such as the flux-gate magnetometer, fiber optic magnetometer, nuclear magnetic resonance, and the superconducting quantum interference device, while the higher field strengths can be resolved by semiconductor magnetic sensors. Our discussion on the modeling issues will be restricted to the latter. Here, the signals are associated with geomagnetism (30–60 μT), magnetic storage media (∼ 1 mT), permanent magnets for contactless sensing (5–100 mT), and current carrying conductors (∼1 mT at 10 A) [6]. These signals lend themselves to two categories of direct and indirect applications [1–3]. Direct applications include measurement of the geomagnetic field, reading of magnetic storage media, identification of magnetic patterns in cards and banknotes, and control of magnetic apparatus. In indirect applications, a non-magnetic signal is detected via the magnetic field which is used as an intermediate carrier. Examples include voltage-free current detection and watt-hour meters, and contactless sensors, based on mechanical displacement of a permanent magnet, for detection of linear or angular displacement and velocity.


Carrier Transport Hall Mobility Hall Coefficient Magnetic Field Dependence Magnetic Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag/Wien 1999

Authors and Affiliations

  • Arokia Nathan
    • 1
  • Henry Baltes
    • 2
  1. 1.Dept. of Electrical and Computer EngineeringUniversity of WaterlooWaterlooCanada
  2. 2.Physical Electronics LaboratoryETH HoenggerbergZürichSwitzerland

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