Modelling of Thermal Behavior N-Doped Silicon Resistor

Fouad Kerrour, Ali Boukabache, Patrick Pons

Laboratory of Architecture and Systems Analysis, National Centre of Scientific Research, Toulouse, France.
Modeling of Energy Renewable Devices and Nano-Metric Laboratory, University of Mentouri, Constantine, Algeria.

DOI: 10.4236/jst.2012.23019                

Abstract

From the analysis of the frequently models of mobility used in the literature, we determine by an identification method the temperature coefficients α and β of a silicon resistance doped with donor atoms. Their variations show a non linear dependence according to the doping and the existence of a minimal value at particular concentration. Moreover, the comparison between the obtained results and those of a P-type resistance shows that there is a strong similarity in their thermal behaviours, except for a particular couple of α and β.

Keywords

Silicon; TCRs, Mobility; Doping; Temperature

1. Introduction

The increasingly miniaturization and integration require, for the modeling engineers and manufacturers of integrated circuits, to improve the behavioural models of the electronic components. Among the latter, semiconductor resistors are used in all components either as passive elements of an electronic circuit or as a sensor (pressure, temperature, chemical species, etc.). In all cases, and in order to preserve the integrity of the useful signal, their sensitivity to the influence parameters must be sufficiently well-known and controlled, either in an empirical way, or by analytical laws. Whatever the purpose of their use or their operating mode, the thermal resistance behaviour must be apprehended in the most accurate way. In a previous study [1], we showed that the thermal drift of a silicon resistor doped with acceptors atoms could be described by an equation of the second order and that the two temperature coefficients (TCRs) have a strong dependence with the doping concentration. Following the same idea, we modelled the influence of the doping concentration on the temperature coefficients of a resistor doped with donor atoms. Knowing that there are differences between the laws controlling the mobility of the electrons and the holes, one can expect that the resistivity of this material shows different behaviour according to the doping type.

2. Relation Resistivity-Mobility

The expression of a silicon resistance according to the temperature T is given by

(1)

where is a reference temperature; α and β are the first and second order TCRs.

Knowing that [1]:

(2)

where ρ and µ are respectively the silicon resistivity and the carriers’ mobility, it would be sufficient to model the carriers’ mobility behaviour according to temperature and doping to obtain the expressions of the two coefficients (TCRs). So far, no analytical model allows us to describe this dependence. On the other hand, there are several empirical models describing the influence of doping and the temperature on the carriers mobility, among which we used those of Arora [2], Klaassen [3] and Dorckel [4] as is shown in the Appendix. In order to evaluate the total carrier mobility (µ_i for electron and µ_j for hole), we use the Mathiessen’s rule which approximates it at low longitudinal field as the sum of four term, which are the four contributions to the carrier mobility: Lattice mobility (µ_{i, L}); donor mobility (µ_{i, D}); acceptor mobility (µ_{i, A}) and electron-hole scattering mobility (µ_{i, j}), and includes temperature dependence [5,6]:

(3)

Thus, using a limited development of the mobility’s expressions µ = f(N_D, T) taken from these references, we used a method of parameters identification. This method is a term by term comparison of the mobility expression as a function of temperature, with Equation (2), for each concentration. Thus, we obtain the expression of α = f(N_D) and β = f(N_D).

3. Results

The obtained results allowed us to plot the curves in Figures 1 and 2. These figures show that for the three models, there is a strong similarity in the shape of the nonlinear curves α = f(N_D) and β = f(N_D). Moreover, the shapes of these curves are similar to those published in [1], as shown in Figures 3 and 4; moreover the minimum values appear for a given doping.

The comparison between the results previously published for a P-type resistor (Figures 3 and 4) and those studied in this work (Figures 1 and 2) should allow to quantify their respective thermal drifts as a function of the doping concentration in one hand, and on the other hand, to validate or not the non linear approach of their thermal coefficients (TCRs) in a given temperature range. Thus, by using the minimal values of the coefficients α and β taken from the curves in Figures 1-4, and by their substitution in the Equation (2), we plotted the curves of the relative resistance variation as a function of the temperature variation ΔT for the two types of doping

Figure 1. Variations of the first order thermal coefficient α as a function of doping concentration N_D. Klaassen; α_min = 445 ppm/˚C; Arora; α_min = 770 ppm/˚C; Dorckel; α_min = 190 ppm/˚C.

Figure 2. Variations of the second order thermal coefficient β as a function of doping concentration N_D. Dorckel; β_min = –3.80 ppm/˚C; Klaassen; β_min = –5.00 ppm/˚C; Arora; β_min = –13.6 ppm/˚C.

Figure 3. Variations of the first order thermal coefficient α as a function of doping concentration N_A [1]. 0: Bullis [6] α_min = 200 ppm/˚C; 1: Dorckel [4] α_min = 400 ppm/˚C; 2: Arora [2] α_min = 980 ppm/˚C; 3: Klaassen [3] α_min = 1000 ppm/˚C

Figure 4. Variations of the second order thermal coefficient β as a function of doping concentration N_A[1].

(Figure 5 for the type P; Figure 6 for the type N). Since the minimum value for different models do not occur at the same doping concentration for the α parameter than for the β one, then we have choose, for a same doping concentration, particular coefficients couples (α_min, β).

These values are used to evaluate the relative resistance change as a function of the temperature variation ΔT. The minimum values of the first and second order thermal coefficients were determined in order to predict the variation of the P-type and the N-type thermal drift resistance, for a given doping concentration.

Figure 5 shows clearly that for a couple α_min(N_A) and β_min(N_A) for the P-type resistor thermal variations can be considered as linear in a large interval of temperature. Figure 6 shows that the N-type resistor thermal behavior is highly nonlinear, whatever the values of α_min(N_D) and β_min(N_D). Therefore, these results may help the more complicated circuits to choose the resistors doping con

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	A. Boukabache and P. Pons, “Doping Effects on Thermal Behaviour of Silicon Resistor,” Electronics Letters, Vol. 38, No. 7, 2002, pp. 342-343. doi:10.1049/el:20020221
[2]	N. D. Arora and D. J. Roulston, “Electron and Hole Motilities in Silicon as a Function of Concentration and Temperature” IEEE Transactions on Electron Devices, Vol. 29, No. 2, 1982, pp. 292-295. doi:10.1109/T-ED.1982.20698
[3]	D. B. M. Klaassen, “Unified Model for Device Simulation-I-II. Temperature Dependence of Carrier mobility and Life Time A,” Solid State Electronic, Vol. 35, No. 7, 1992, pp.953-967.
[4]	J. M. Dorckel and P. H. Leturcq, “Carrier Motilities in Silicon Semi-Empirically Related to Temperature, Doping and Injection Level,” Solid State Electronic, Vol. 24, No. 9, 1981, pp. 821-825. doi:10.1016/0038-1101(81)90097-6
[5]	C. Lombardi, S. Manzini, A. Saporito and M. Vanzi, “A Physically Based Mobility Model for Numerical Simulation of Non Planar Devices,” IEEE Transacitions Computer-Aided Design, Vol. 7, 1988, pp. 1164-1171.
[6]	T. T. Mnatsakanov, M. E. Levinshtein, L. I. Pomortseva1 and S. N. Yurkov1, “Carrier Mobility Model for Simulation of SiC-Based Electronic Devices,” Semiconductor Science and Technology, Vol. 17, No. 9, 2002, pp. 974-977.