Книги2 / 1993 Dutton , Yu -Technology CAD_Computer Simulation

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Base Doping, I/CM '

Figure 7.10: Cutoff frequency versus base doping with NA WB as a parameter.

versus N A using N A WB as a parameter. A typical plot for N A WB = 5 X 1012 cm- 2 is shown in Figure 7.10 which shows that average NA values in the base must exceed 1017 cm-3 to achieve IT = 1GHz. This means that for a fixed N A WB (and hence fixed f3F), the higher N A is the smaller the WB is, which reduces the transit time. However, increasing NA also causes CBE to increase, which is not accounted for in this simple expression. Hence, there will be a fall-off of IT at increasing NA, which is steeper than what is shown in the figure. Although Figure 7.10 cannot be used as an absolute design criterion - obviously the simplified assumption (i.e. uniform N A) cannot agree with the reality - the curve indeed gives a clear picture of the design trends. Hence, extracting a base width, we find WB S 0.5pm for a fixed N A WB = 5 x 1012 em-2, and this number decreases as NA rises from a minimum value of 1017 cm-3 .

With the above design constraints in mind, the base doping profile can be adjusted to maximize the current gain, Early voltage, and cutoff frequency. The dominant controlling factors are N A and WB, as discussed above. The results of a series of PISCES simulations are shown in Figure 7.11. The peak base doping was held constant at 1018 cm-3 and the base width (and profile) was varied as shown in Figure 7.11 (a). Figure 7.11 (b) shows the resulting variation in current gain and iT for these variations. The optimum in terms of the design goals appears to be in the range of 0.21 pm < WB < 0.25 pm. One may note

(a) Doping profile

Wo (II)

(b) f3 and iT dependence on WB

Figure 7.11: Parameter variations with base peak concentration held at

1018 cm-3 .

312 CHAPTER 7. BICMOS TECHNOLOGY

that the steep profile shapes now make the constant-NA approximations described above to be of only limited quantitative value. The simulated experiments were performed using analytic base profiles in SEDAN/PISCES. Based on this design-oriented optimization, one can in turn use SUPREM to seek optimum process conditions to realize the desired profiles. In some instances, the limitations in thermal cycling may restrict one's ability to realize very steep profiles and hence the overall device optimization must be reconsidered. A final critical design factor related to the bipolar device is the role of the emitter. The process suggested in Figure 7.6 uses polysilicon as the doping source for the arsenic. There are both technological advantages and limitations to this approach. On the positive side, the structure can give a self-aligned emitter-base and an extra degree of vertical freedom in achieving shallow junctions. One major drawback is the role of residual interfacial oxide at the poly-bulk interface, which can cause unwanted emitter series resistance. SEDAN III has been used to study such effects and the interested reader is referred elsewhere for further discussion [7.8]. In the remaining discussion, we focus on the conventional emitter transport effects.

In the npn device, the role of high doping effects is quite significant. Figure 7.12 shows a comparison of {3F versus VBE with different physical assumptions imposed in SEDAN/PISCES. The curve labeled "ideal" assumes no special heavy-doping effects. The next two curves include first the bandgap narrowing (BGN) and then the BGN and Auger recombination (AUG) terms. One can see a dramatic reduction of {3F - in fact for this profile input the gain falls below the design specification and hence the process would need to be redesigned. This illustrates the advantage of device simulator as a tool to identify the trade-offs in the emitter design. In certain cases one can even adjust profiles to avoid certain high-doping effects. As technology evolution moves more aggressively towards poly emitter structures, the importance of bipolar modeling will also increase.

The above example has illustrated further the use of process and device modeling to design new processes with multiple (and often conflicting) constraints. In contrast to the triple-diffused process, the buriedepi layer approach offers several new directions for process optimization, although it is necessary to trade-off p-channel and npn device performance. For further examples of process optimization using SUPREM and SEDAN, see [7.5].

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Figure 7.12: Current gain (13) versus base-emitter voltage (VBE) with several physical parameters selectively altered (turned "off''' or "on").

7.4Summary

The BiCMOS technology examples discussed above represent the natural evolution of mainstream CMOS towards higher performance. From a pedagogical perspective this discussion has allowed us to begin to understand several trade-offs in transistor design - especially for a mixed device technology. For the triple-diffused device approach, the multiple use of boron implants yields unreasonable base performance in the npn device. Also, the n-well constraint adds further performance degradation to the npn. The shift to the buried-epi layer process corrects a majority of the bipolar shortcomings. However, the n-well constraints are still an area of major design trade-offs. The future of such mixed technologies will undoubtedly be driven by more clever means to realize shallow junction devices with self-aligned multiple layer materials. The poly-gate/poly-emitter process used here is only the beginning. For example, novel junction structures using polysilicon, silicides, and even heterojunction emitter and base structures can be expected to emerge as viable technology approaches.

The methodology of device analysis and design presented throughout the text shows a common theme of combining analytical expressions with extensive (but enlightened) use of simulation to quantify the many

complex trade-offs. Device simulation (including coupled process simulation results) is invaluable to visualize and help understand complex phenomena. We have considered examples such as nonuniform doping effects on threshold - including phosphorus pile-up and boron leaching (Figure 1.6 (c)), doping dependence of recombination effects in diodes and transistors (Figure 4.21), and bipolar storage effects in junctions and neutral regions. These examples are representative and in each case they show both technology dependence and the value of using simulation to unravel the problems, quantitatively as well as qualitatively.

In the context of material covered in other chapters, we have tried to unite the classical (analytical) results with numerical examples to both illuminate technology dependences and to see under what conditions they are valid. For example, terms such as quasi-neutrality, complete depletion, strong inversion, and high-level injection, each has a commonly assumed meaning, yet quantitatively these terms are sometimes untractable in terms of rigorous analysis. It is hoped that by means of the examples used here and the juxtapositioning of numerical and analytic results, the reader can help to sharpen his/her awareness of the physical reality.

Finally, the evolution of device technology is moving the field of microelectronics toward new physical frontiers. Different semiconductor materials such as GaAs and SiGe bring different emphasis to transport and electronic effects. Technologies such as Molecular Beam Epitaxy (MBE) and Molecular Organic Chemical Vapor Deposition (MOCVD), are allowing us to alter energy-band properties of devices and create electronic quantum wells. In this spirit of reaching the frontiers of device physics, one quickly realizes that many of our modeling paradigms break down. The notion of complete quantitative numerical models such as SUPREM and SEDAN/PISCES are invaluable as paradigm-busters. Namely, in the context of new process and device design, we can test the models, break them (!), and hopefully learn from and correct the errors in our conceptual thinking. Namely, the computer tools can become "living textbooks" that evolve with our understanding. Our most positive wish is that after reading, understanding, and testing results presented here you can take your own steps in the evolution of technology using TCAD.

7.5Exercises

Design project for npn bipolar transistor based on Stanford BiCMOS process (see Appendix B):

1. Design specification:

Develop, model, and characterize an npn bipolar transistor for the Stanford BiCMOS process.

Device should operate at or below the knee current for HLI Achieve or surpass the following criteria

current gain i3F > 50 Early voltage VA > 30 V

cut-off frequency iT > 8 GHz

open-base breakdown voltage BVCEO > 10 V

2. Assumptions & constraints:

Tc = lOOn

Tb = 200n

AE = 2 x 10-7 cm2 Ac = 6 X 10-7 cm2

3. Design approach

This project involves designing and analyzing an npn bipolar device using PISCES.

Assume a retrograde n-well, approximated as a Gaussian from the surface with peak concentration and characteristic length of your choice. You may incorporate a buried layer in addition to the well itself.

Use an analytical approximation to the emitter.

For the base region, again use an analytical approximation. You can set range, dose, and characteristic length freely.

Since we ultimately want to really build this device in a reasonable variant on the existing CMOSjBiCMOS process, you should consider how each of the profiles and their associated temperature budgets will be integrated into the process.

For similar reasons, you should target to use realistic ion implantation and diffusion as in the CMOS n-well.

7.6References

[7.1J A. R. Alvarez, J. Teplik, D. W. Schucker, T. Hulseweh, H. B. Liang, M. Dydyk, and 1. Rahim, "Second generation BiCMOS gate array technology," Proceedings of BCTM (Bipolar Circuits and Technology Meeting) '87, pp. 113-117,1987.

[7.2J R. A. Curtis, D. D. Smith, and T. 1. Bowman, "A 12 ns 256K BiCMOS sRAM," Digest ISSCC (Int. Solid State Circuits Conf.)

'88, pp. 186-187, 1988.

[7.3J J. Miyamoto, S. Saitoh, H. Momose, H. Shibata, K. Kanzaki, and S. Kohyama, "A 1.0 pm N-well CMOS/Bipolar technology for VLSI circuits," IEDM Technical Digest, pp. 10-13, Dec. 1983.

[7.4] T.-Y. Chiu, and et. al., "Non-overlapping super self-aligned BiCMOS with 87ps low power ECL," IEDM Technical Digest, pp.752755, Dec. 1988.

[7.5J A. R. Alvarez, ed., BiCMOS Technology and Applications. Boston: Kluwer Academic Pub., 1989.

[7.6J G.W. McIver, R.W. Miller, T.G. O'Shaughnessy, "A monolithic 16 x 16 digital multiplier," Digest ISSCC, paper WPM6.1, pp. 5455, 1974.

[7.7J Robert Elkind, Jay Lessert, James Peterson, and Gregory Taylor, "A sub 10 nS bipolar 64 bit integer/floating point processor implemented on two circuits," Proceedings of BCTM '87, pp. 101-104, 1987.

[7.8] Z. Yu, "Numerical model and analysis of transistors with polysilicon emitters," Ph.D. dissertation, Stanford University, 1985.

[7.9] Z. Yu, D. Chen, R. Goossens, R.W. Dutton, P.V. Voorde, and S.Y. Oh, "Accurate modeling and numerical techniques in simulation of impact-ionization effects on BJT characteristics," IEDM Tecllnical Digest, pp. 901-904, Dec. 1991.

Appendix A

Numerical Methods in

Process/Device Simulation

A.I Introduction

The central issue in Ie process and device simulation is to find the spatial distribution of mobile, charged/neutral particles as a function of time. The particles here refer to either carriers (electrons and holes) in device simulation or neutral and ionized impurity atoms in process simulation. This time-variant, spatial distribution gives directly the doping profile in process simulation. For device simulation (and sometimes for process simulation too), one needs also to know the electrostatic potential distribution to obtain terminal I - V characteristics and device parameters, which are of most interest to users.

The distribution of mobile particles is governed mainly by the continuity principle, one of the fundamental physical laws in nature. For charged particles, the distribution is also governed by the Poisson's equation. The continuity principle is actually a manifestation of matter conservation that states semi-quantitatively that the rate of density change for one species of particles at one position in space is equal to the sum of the net incoming flow of particles and the net generation rate of particles at this position. Mathematically, the aforementioned incoming flow at one position is rigorously expressed by the negative of the divergence of the particle flow, whereas the net generation rate is simply the generation rate subtracted by the recombination rate. Thus by using conventionally defined particle flow, F (not to be confused with

the flux, which usually refers to the particle flow through a certain cross section but not a unit area), one can express the continuity principle in the following partial differential equation:

where C is the particle density (or equally called the concentration), a function of space and time, and u is the net recombination rate, which equals r - 9 where r is the recombination and 9 the generation rate, respectively. This equation is called the continuity equation, and is also referred to as Fick's second law.

To solve the continuity equation for particle density, we need first to express quantities F, g, and r using C. The expressions for the generation/recombination rates are relatively straightforward as are seen in previous chapters. The expression for the particle flow, however, varies in different physical processes under study and also depends on the level of physical models used. In most practical cases in Ie process/device simulation, because of the large population of particles (impurity atoms, electrons, and holes) and the numerous events of particle collision/scattering occurring within the physical structure, the particle flow is often expressed using the diffusion-drift (abbreviated DD) model. The particle flow in this model consists of two parts: that due to the diffusion which is proportional to the gradient of the particle density and that due to the drift caused by the external driving force. For charged particles, the DD expression for the flow is

"-" sign for those with negative charge. Because both the diffusivity and mobility are a reflection of how frequently the scattering events occur (or inversely the relaxation time), D and /-l are related by certain relationships, and in this case they obey the Einstein relationship,

D/ /-l = kT/ q.

The above DD expression of the particle flow is usually applied to the transport in the bulk part of a single material. For particle transport across the interface of different materials or at the surface of the simulation region, other physical mechanisms may become important