The solution of the Boltzmann equation presents a rather difficult problem, because
of the large number of variables involved. For a general three-dimensional system
there are six arguments of the distribution function (three components each of 
and 
). If we were to simply discretize the Boltzmann equation, we would
need to represent the distribution function f by an array with six subscripts,
corresponding to the six arguments of the continuous function. If we used only
ten values for each subscript, the f array would have 
elements, and each
evaluation of f would require a corresponding number of operations. Such a
discretization of the Boltzmann equation has in fact been investigated by Aubert,
Vaissiere, and Nougier [39], for the case of a spatially homogeneous
system, so that only the three velocity arguments are required.
By far the most widely used technique for evaluating the Boltzmann description of electron transport has been the Monte Carlo method [40,41,42,43]. With this technique, one does not solve the Boltzmann equation directly, but one rather simulates the motion of classical electrons subjected to a combination of free-flight motion and instantaneous random scattering events. The distribution function is then estimated by statistical averages over long times or many particles. The state of the system is represented by the position and velocity vectors of each of a large number of particles. The velocity and position of each particle are integrated over time until a collision is deemed to have occurred (based upon the value of a randomly-chosen value with an appropriate distribution). Other random values then determine the particular scattering mechanism and the velocity of the electron after the scattering. After the scattering, the free-flight motion of the electron is again integrated until the next collision. This procedure is performed for all particles in the chosen ensemble to evaluate the time-evolution of the device. The openness of the device is modeled by procedures which treat the escape of electrons into and injection of electrons from the contact regions [44,45].
The Monte Carlo technique permits one to include many of the physical effects that influence electron transport, and to include them at an extremely detailed level. Effects which have been incorporated include the detailed energy-band structure of the semiconductor [46], electron-electron interactions at the level of the self-consistent potential or at a more detailed electron-electron collision description [47], and higher-energy events such as impact ionization [45]. However, there is one very significant constraint imposed by the Monte Carlo procedure: only processes describable by a master operator can be modeled.