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Fermi–Dirac statistics is a part of the science of physics that describes the energies of single particles in a system comprising many identical particles that obey the Pauli exclusion principle. It is named after Enrico Fermi and Paul Dirac, who each discovered it independently, although Enrico Fermi defined the statistics earlier than Paul Dirac.[1][2]

Fermi–Dirac (F–D) statistics applies to identical particles with half-odd-integer spin in a system in thermal equilibrium. Additionally, the particles in this system are assumed to have negligible mutual interaction. This allows the many-particle system to be described in terms of single-particle energy states. The result is the F–D distribution of particles over these states and includes the condition that no two particles can occupy the same state, which has a considerable effect on the properties of the system. Since F–D statistics applies to particles with half-integer spin, they have come to be called fermions. It is most commonly applied to electrons, which are fermions with spin 1/2. Fermi–Dirac statistics is a part of the more general field of statistical mechanics and uses the principles of quantum mechanics.

History

Before the introduction of Fermi–Dirac statistics in 1926, understanding some aspects of electron behavior was difficult due to seemingly contradictory phenomena. For example, the electronic heat capacity of a metal at room temperature seemed to come from 100 times fewer electrons than were in the electric current.[3] It was also difficult to understand why the emission currents, generated by applying high electric fields to metals at room temperature, were almost independent of temperature.

The difficulty encountered by the electronic theory of metals at that time was due to considering that electrons were (according to classical statistics theory) all equivalent. In other words it was believed that each electron contributed to the specific heat an amount on the order of the Boltzmann constant k. This statistical problem remained unsolved until the discovery of F–D statistics.

F–D statistics was first published in 1926 by Enrico Fermi[1] and Paul Dirac.[2] According to an account, Pascual Jordan developed in 1925 the same statistics which he called Pauli statistics, but it was not published in a timely manner.[4] Whereas according to Dirac, it was first studied by Fermi, and Dirac called it Fermi statistics and the corresponding particles fermions.[5]

F–D statistics was applied in 1926 by Fowler to describe the collapse of a star to a white dwarf.[6] In 1927 Sommerfeld applied it to electrons in metals[7] and in 1928 Fowler and Nordheim applied it to field electron emission from metals.[8] Fermi–Dirac statistics continues to be an important part of physics.
Fermi–Dirac distribution

For a system of identical fermions, the average number of fermions in a single-particle state i, is given by the Fermi–Dirac (F–D) distribution,[9]

\( \bar{n}_i = \frac{1}{e^{(\epsilon_i-\mu) / k T} + 1} \)

where k is Boltzmann's constant, T is the absolute temperature, \( \epsilon_i \ \) is the energy of the single-particle state i, and \( \mu\ \) is the chemical potential. At T = 0 K, the chemical potential is equal to the Fermi energy. For the case of electrons in a semiconductor, \mu\ is also called the Fermi level.[10][11]

The F–D distribution is only valid if the number of fermions in the system is large enough so that adding one more fermion to the system has negligible effect on \mu\ .[12] Since the F–D distribution was derived using the Pauli exclusion principle, which allows at most one electron to occupy each possible state, a result is that \( 0 < \bar{n}_i < 1 . \) [13]

Fermi–Dirac distribution
Energy dependence. More gradual at higher T. \( \bar{n} = 0.5 \) when \( \epsilon \; = \mu \; \) . Not shown is that \( \mu \ \) decreases for higher T.[14]
Temperature dependence for \epsilon > \mu \ .

Distribution of particles over energy
Fermi function \( F(\epsilon \ ) vs. energy \( \epsilon \ \) , with μ = 0.55 eV and for various temperatures in the range 50K ≤ T ≤ 375K.

The above Fermi–Dirac distribution gives the distribution of identical fermions over single-particle energy states, where no more than one fermion can occupy a state. Using the F–D distribution, one can find the distribution of identical fermions over energy, where more than one fermion can have the same energy.[15]

The average number of fermions with energy \epsilon_i \ can be found by multiplying the F–D distribution \( \bar{n}_i \ \) by the degeneracy \( g_i \ \) (i.e. the number of states with energy \( \epsilon_i \ \) ),[16]

\( \begin{alignat}{2} \bar{n}(\epsilon_i) & = g_i \ \bar{n}_i \\ & = \frac{g_i}{e^{(\epsilon_i-\mu) / k T} + 1} \\ \end{alignat} \)

When \( g_i \ge 2 \ \) , it is possible that \( \ \bar{n}(\epsilon_i) > 1 \) since there is more than one state that can be occupied by fermions with the same energy \( \epsilon_i \ \) .

When a quasi-continuum of energies \( \epsilon \ \) has an associated density of states \( g( \epsilon ) \ \) (i.e. the number of states per unit energy range per unit volume [17]) the average number of fermions per unit energy range per unit volume is,

\( \bar { \mathcal{N} }(\epsilon) = g(\epsilon) \ F(\epsilon) \)

where\( F(\epsilon) \ \) is called the Fermi function and is the same function that is used for the F–D distribution\( \bar{n}_i \) ,[18]

\( F(\epsilon) = \frac{1}{e^{(\epsilon-\mu) / k T} + 1} \)

so that,

\( \bar { \mathcal{N} }(\epsilon) = \frac{g(\epsilon)}{e^{(\epsilon-\mu) / k T} + 1} . \)

Quantum and classical regimes

The classical regime, where Maxwell–Boltzmann (M–B) statistics can be used as an approximation to F–D statistics, is found by considering the situation that is far from the limit imposed by the Heisenberg uncertainty principle for a particle's position and momentum. Using this approach, it can be shown that the classical situation occurs if the concentration of particles corresponds to an average interparticle separation \( \bar{R} \) that is much greater than the average de Broglie wavelength \( \bar{\lambda} \) of the particles,[19]

\( \bar{R} \ \gg \ \bar{\lambda} \ \approx \ \frac{h}{\sqrt{3mkT}} \)

where h is Planck's constant, and m is the mass of a particle.

For the case of conduction electrons in a typical metal at T=300K (i.e. approximately room temperature), the system is far from the classical regime since \( \bar{R} \) \approx \bar{\lambda}/25 \) . This is due to the small mass of the electron and the high concentration (i.e. small \( \bar{R}) \) of conduction electrons in the metal. Thus F–D statistics is needed for conduction electrons in a typical metal.[19]

Another example of a system that is not in the classical regime is the system that consists of the electrons of a star that has collapsed to a white dwarf. Although the white dwarf's temperature is high (typically T=10,000K on its surface[20]), its high electron concentration and the small mass of each electron precludes using a classical approximation, and again F–D statistics is required.[6]

Two derivations of the Fermi–Dirac distribution
Derivation starting with canonical distribution

Consider a many-particle system composed of N identical fermions that have negligible mutual interaction and are in thermal equilibrium.[12] Since there is negligible interaction between the fermions, the energy \( E_R \) of a state R of the many-particle system can be expressed as a sum of single-particle energies,

\( E_R = \sum_{r} n_r \epsilon_r \; \)

where n_r is called the occupancy number and is the number of particles in the single-particle state r with energy \( \epsilon_r \;. \) The summation is over all possible single-particle states r.

The probability that the many-particle system is in the state R, is given by the normalized canonical distribution,[21]

\( P_R = \frac { e^{-\beta E_R} } { \displaystyle \sum_{R'} e^{-\beta E_{R'}} } \)

where \( \beta\; = 1/kT \), k is Boltzmann's constant, T is the absolute temperature, \( e\scriptstyle -\beta E_R \) is called the Boltzmann factor, and the summation is over all possible states R' of the many-particle system. The average value for an occupancy number \( n_i \; \) is[21]

\( \bar{n}_i \ = \ \sum_{R} n_i \ P_R \) \)

Note that the state R of the many-particle system can be specified by the particle occupancy of the single-particle states, i.e. by specifying\( n_1,\, n_2,\, ... \; \) so that

\( P_R = P_{n_1,n_2,...} = \frac{ e^{-\beta (n_1 \epsilon_1+n_2 \epsilon_2+...)} } {\displaystyle \sum_{{n_1}',{n_2}',...} e^{-\beta ({n_1}' \epsilon_1+{n_2}' \epsilon_2+...)} } \)

and the equation for \( \bar{n}_i \) becomes

\( \begin{alignat} {2} \bar{n}_i & = \sum_{n_1,n_2,\dots} n_i \ P_{n_1,n_2,\dots} \\ \\ & = \frac{\displaystyle \sum_{n_1,n_2,\dots} n_i \ e^{-\beta (n_1\epsilon_1 + n_2\epsilon_2 + \cdots + n_i\epsilon_i + \cdots)} } {\displaystyle \sum_{n_1,n_2,\dots} e^{-\beta (n_1\epsilon_1 + n_2\epsilon_2 + \cdots + n_i\epsilon_i + \cdots)} } \\ \end{alignat} \)

where the summation is over all combinations of values of \( n_1,n_2,...\; \) which obey the Pauli exclusion principle, and \( n_r = 0 \) or 1 for each r. Furthermore, each combination of values of \( n_1,n_2,...\; \) satisfies the constraint that the total number of particles is N,

\( \sum_{r} n_r = N \; . \)

Rearranging the summations,

\( \bar{n}_i = \frac {\displaystyle \sum_{n_i=0} ^1 n_i \ e^{-\beta (n_i\epsilon_i)} \quad \sideset{ }{^{(i)}}\sum_{n_1,n_2,\dots} e^{-\beta (n_1\epsilon_1+n_2\epsilon_2+\cdots)} } {\displaystyle \sum_{n_i=0} ^1 e^{-\beta (n_i\epsilon_i)} \qquad \sideset{ }{^{(i)}}\sum_{n_1,n_2,\dots} e^{-\beta (n_1\epsilon_1+n_2\epsilon_2+\cdots)} } \)

where the \( ^{(i)} on the summation sign indicates that the sum is not over \( n_i \) and is subject to the constraint that the total number of particles associated with the summation is \( N_i = N-n_i \) . Note that \( \Sigma^{(i)} \) still depends on \( n_i \) through the\( N_i \) constraint, since in one case \( n_i=0 \) and \( \Sigma^{(i)} \) is evaluated with \( N_i=N \) , while in the other case \( n_i=1 \) and \( \Sigma^{(i)} \) is evaluated with \( N_i=N-1 \) . To simplify the notation and to clearly indicate that \( \Sigma^{(i)} \) still depends on \( n_i \) through \( N-n_i \) , define

\( \( Z_i(N-n_i) \equiv \ \sideset{ }{^{(i)}}\sum_{n_1,n_2,...} e^{-\beta (n_1\epsilon_1+n_2\epsilon_2+\cdots)} \; \)

so that the previous expression for \( \bar{n}_i can be rewritten and evaluated in terms of the \( Z_i,

\( \begin{alignat} {3} \bar{n}_i \ & = \frac{ \displaystyle \sum_{n_i=0} ^1 n_i \ e^{-\beta (n_i\epsilon_i)} \ \ Z_i(N-n_i)} { \displaystyle \sum_{n_i=0} ^1 e^{-\beta (n_i\epsilon_i)} \qquad Z_i(N-n_i)} \\ \\ & = \ \frac { \quad 0 \quad \; + e^{-\beta\epsilon_i}\; Z_i(N-1)} {Z_i(N) + e^{-\beta\epsilon_i}\; Z_i(N-1)} \\ & = \ \frac {1} {[Z_i(N)/Z_i(N-1)] \; e^{\beta\epsilon_i}+1} \quad . \end{alignat} \)

The following approximation[22] will be used to find an expression to substitute for \(Z_i(N)/Z_i(N-1) \) .

\( \begin{alignat} {2} \ln Z_i(N- 1) & \simeq \ln Z_i(N) - \frac {\partial \ln Z_i(N)} {\partial N } \\ & = \ln Z_i(N) - \alpha_i \; \end{alignat} \)

where \( \alpha_i \equiv \frac {\partial \ln Z_i(N)} {\partial N} \ . \)

If the number of particles N is large enough so that the change in the chemical potential \mu\; is very small when a particle is added to the system, then \( \alpha_i \simeq - \mu / kT \ \) .[23] Taking the base e antilog[24] of both sides, substituting for\( \alpha_i \,, \) and rearranging,

\( Z_i(N) / Z_i(N- 1) = e^{-\mu / kT } \, . \)

Substituting the above into the equation for \( \bar {n}_i \) , and using a previous definition of \( \beta\; \) to substitute 1/kT for \( \beta\;, \) results in the Fermi–Dirac distribution.

\( \bar{n}_i = \ \frac {1} {e^{(\epsilon_i - \mu)/kT }+1} \)

Derivation using Lagrange multipliers

A result can be achieved by directly analyzing the multiplicities of the system and using Lagrange multipliers.[25]

Suppose we have a number of energy levels, labeled by index i, each level having energy εi and containing a total of ni particles. Suppose each level contains gi distinct sublevels, all of which have the same energy, and which are distinguishable. For example, two particles may have different momenta (i.e. their momenta may be along different directions), in which case they are distinguishable from each other, yet they can still have the same energy. The value of gi associated with level i is called the "degeneracy" of that energy level. The Pauli exclusion principle states that only one fermion can occupy any such sublevel.

The number of ways of distributing ni indistinguishable particles among the gi sublevels of an energy level, with a maximum of one particle per sublevel, is given by the binomial coefficient, using its combinatorial interpretation

\( w(n_i,g_i)=\frac{g_i!}{n_i!(g_i-n_i)!} \ . \)

For example, distributing two particles in three sublevels will give population numbers of 110, 101, or 011 for a total of three ways which equals 3!/(2!1!). The number of ways that a set of occupation numbers ni can be realized is the product of the ways that each individual energy level can be populated:

\( W = \prod_i w(n_i,g_i) = \prod_i \frac{g_i!}{n_i!(g_i-n_i)!}. \)

Following the same procedure used in deriving the Maxwell–Boltzmann statistics, we wish to find the set of ni for which W is maximized, subject to the constraint that there be a fixed number of particles, and a fixed energy. We constrain our solution using Lagrange multipliers forming the function:

\( f(n_i)=\ln(W)+\alpha(N-\sum n_i)+\beta(E-\sum n_i \epsilon_i). \)

Using Stirling's approximation for the factorials, taking the derivative with respect to ni, setting the result to zero, and solving for ni yields the Fermi–Dirac population numbers:

\( n_i = \frac{g_i}{e^{\alpha+\beta \epsilon_i}+1}. \)

By a process similar to that outlined in the Maxwell-Boltzmann statistics article, it can be shown thermodynamically that \( \beta = \frac{1}{kT} and \alpha = - \frac{\mu}{kT} \) where \( \mu \) is the chemical potential, k is Boltzmann's constant and T is the temperature, so that finally, the probability that a state will be occupied is:

\( \bar{n}_i = \frac{n_i}{g_i} = \frac{1}{e^{(\epsilon_i-\mu)/kT}+1}. \)