Modeling Carbon Diffusion And Its Impact On Boron Diffusion In Silicon .
MODELING CARBON DIFFUSION AND ITS IMPACT ON BORONDIFFUSION IN SILICON AND SILICON GERMANIUM
MODELING CARBON DIFFUSION AND ITSIMPACT ON BORON DIFFUSION IN SILICON ANDSILICON GERMANIUMBySamer Samir Rizkalla RizkB.Sc.A ThesisSubmitted to the School of Graduate Studies inPartial Fulfillment of the Requirementsfor the Degree ofMasters of Applied ScienceMcMaster UniversityHamilton, Ontario, Canada Copyright by Samer Rizk, August 2005
McMASTER UNIVERSITYMASTERS OF APPLIED SCIENCE (2005)(Electrical Engineering)TITLE:Hamilton, Ontario, CanadaModeling Carbon Diffusion and its Impact on BoronDiffusion in Silicon and Silicon GermaniumAUTHOR:Samer Samir Rizkalla Rizk, B.Sc. (Ain Shams University,Cairo, Egypt)SUPERVISOR:Dr. Y aser M. HaddaraNUMBER OF PAGES:xiii, 93ii
ABSTRACTThe integration of silicon germanium (SiGe) in the base of heterojunction bipolartransistors (HBTs) has recently put the alloy into prominence to produce fast-switchingtransistors. However, the thin highly doped SiGe base makes the transistor susceptible tobase dopant outdiffusion during device processing, which results in device performancedegradation. Adding carbon to the base was shown to significantly suppress boronoutdiffusion and help retain the narrow as-grown profile. Dopant behavior in the presenceof various species needs to be well understood and modeled for two reasons: (1) to haveaccurate and predictive process simulators; and (2) to obtain insight into processdevelopment.Modeling carbon diffusion and its role in suppressing boron diffusion in siliconand SiGe has been studied by several groups. While boron diffusion is well-established,different modeling regimes have been developed for carbon diffusion. Each of theexisting studies has focused on subsets of the available experimental data. We present aconsistent and complete model that accounts for carbon and boron diffusion in siliconand SiGe, under equilibrium and non-equilibrium conditions. In our regime, carbondiffusion is modeled according to the kick-out and Frank-Tumbull mechanisms fordiffusion; in addition, we incorporate the carbon clustering phenomenon. To completelymodel boron diffusion, we account for the boron-interstitial clustering (BICs) effect andthe {311} defects that are associated with boron transient enhanced diffusion (TED). Inthe developed model we make use of the well-established literature data for carbondiffusion, as well as boron diffusion and Si self-diffusion. The model was verified byiii
simulating experiments that involve boron and/or carbon diffusion in silicon and SiGeand cover the complete temperature range of 750 - 1070 C. The test structures includepublished experiments in addition to recent experimental results obtained throughcollaboration, and feature diffusion in inert and oxidizing ambients, under rapid thermalannealing (RTA) conditions, as well as in the presence of implant damage. We alsoinvestigated the validation of the model without the inclusion of either the clustering orthe Frank-Turnbull reactions.iv
ACKNOWLEDGMENTSFirst and foremost, I thank God for all and everything."I like a teacher who gives you something to take home to think about besideshomework" - Lilly Tomlin. I would like to express my appreciation to Dr. YaserHaddara for the supervisor and great friend he is. He was and will always be a source ofsupport and advice. During the course of this Master's degree, Dr. Yaser has created afriendly environment in our research group that made this experience a pleasant andunforgettable one. Thank you sir for being a great example.I would also like to thank my fellow graduate students who have taken me as afriend. I am deeply honored and fortunate to know you.Last but not least, I am deeply grateful to my parents Samir and Effat, who havemade the past two years a delightful period of my life through their continuous love andsupport. To my brother George and sister Mariana, love you guys so much. I thank Godfor you all everyday.v
TABLE OF CONTENTSCHAPTER 1 . 1INTRODUCTION . 11.1 Motivation .31.2 Methodology .71.2.1 Secondary Ion Mass Spectrometry (SIMS) . 81.2.2 FLOOPS-ISE . 101.3 Thesis Organization . 10CHAPTER 2 . 12DIFFUSION IN Si/SiGe . 122.1 Diffusion Mechanisms . 132.1.1 Simple Diffusion Mechanisms . 132.1.2 Complex Diffusion Mechanisms . 142.2 Non-equilibrium Dopant Diffusion by Perturbations in Point DefectConcentrations . 192.3 Boron Diffusion in Si and Strained SiGe .202.3.1 Oxidation Enhanced Diffusion (OED) .222.3.2 Transient Enhanced Diffusion (TED) . 232.4 Carbon Diffusion in Si and SiGe . 252.4.1 Carbon Clustering .262.5 Impact of Carbon Profile on Boron Diffusion Retardation and DevicePerformance .27CHAPTER 3 .32EXISTING APPROACHES FOR MODELING CARBON DIFFUSION . . . . . 323.1 Review of Diffusion Parameters .333.1.1 Silicon Self-Diffusion Parameters .333.1.2 Boron Diffusion Parameters. 353.1.3 Carbon Diffusion Parameters .383.2 Different Modeling Approaches .393.2.1 Kick-out Mechanism Only .403.2.2 Kick-out and Clustering Mechanisms . .413.2.3 Kick-out and Frank-Turnbull Mechanisms . .443.2.4 Kick-out, Frank-Turnbull and Clustering Mechanisms . .463.2.5 Other Approaches .48vi
CHAPTER 4 . . . . . . . . . . . . . . . . . . . 51A CONSISTENT AND COMPLETE MODEL FOR CARBON DIFFUSION ANDITS IMPACT ON BORON DIFFUSION . . . . . . . . . . . 514.1 Modeling Carbon Diffusion .524.1.1 Carbon Diffusion Parameters . 644.2 Modeling Boron Diffusion .644.2.1 Modeling Boron TED .664.3 Silicon Self-Diffusion .56CHAPTER 5 . . . . . . . . 59SIMULATION RESULTS AND DISCUSSION . . . . . . . . 595.1 Boron Diffusion Suppression by Carbon . 695.1.1 Equilibrium Conditions .595.1.2 Non-equilibrium Conditions . 635.2 Carbon Diffusion .725.3 Antimony Diffusion Enhancement by Carbon .755.4 The Carbon Clustering Reaction Reverse Rate . 76CHAPTER 6 . . . . . . . . . . . . . 78MODELING CARBON DIFFUSION WITHOUT THE CLUSTERING ORFRANK-TURNBULL MECHANISMS 786.1 Excluding the Clustering Reaction .786.2 Excluding the Frank-Turnbull Reaction . 81CHAPTER 7 . . . . . . . . . . . . . . . . . 84CONTRIBUTIONS AND FUTURE WORK . . . . . . . . . 847.1 Contributions.857.2 Future Work .86REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88vii
LIST OF FIGURESFigure 1: Schematic band alignments of strained SiGe grown on a Si substrate [4] . 3Figure 2:SIMS measurement technique [20]. . 9Figure 3: Direct interstitial (a), vacancy (b), and ring (c) diffusion mechanisms in adiamond lattice [22] . 14Figure 4: Dopant-vacancy pair diffusion mechanism [22] . 16Figure 5:Interstitial-pair (a), and kick-out (b) diffusion mechanisms [22] . 17Figure 6: The Frank-Turnbull mechanism [22] . 18Figure 7:Schematic diagram of (a) as-grown dopant profiles of a SiGe HBT, (b) baseprofile after annealing showing boron outdiffusion, and (c) conduction banddiagram of the as-grown and annealed HBT showing the parasitic conductionband barriers created due to boron outdiffusion (50] . 29Figure 8: Common-emitter characteristics of transistors without (left) and with (right) alocalized carbon concentration following As implantation and annealing at647oc [9] . 30Figure 9: The FLOOPS-ISE and literature values (Refs. [54] and [55] respectively) forthe silicon interstitial self-diffusion component . 34Figure 10: The FLOOPS-ISE and literature values (Refs. [58] and [55] respectively) forthe silicon vacancy self-diffusion component. . 35Figure 11: Interstitial diffusivities (a), and equilibrium concentrations (b) of FLOOPSISE and as reported by [13], [59], [60], and [48] respectively . 36Figure 12: Vacancy diffusivities (a), and equilibrium concentrations (b) of FLOOPS-ISEand as reported by [13], [48], and [60] respectively. 37Figure 13: Study by Haddara et al. determining boron intrinsic diffusivity and comparingresults with previous work [63] . 38viii
Figure 14: Comparison ·between the equilibrium carbon diffusivity according to [44],[68], [47], and [12], respectively . 39Figure 15: As-grown profiles (full circles), SIMS profiles (open diamonds) and simulatedprofiles (solid lines) of a carbon (top) and boron (bottom) doped sampleundergoing a thermal budget of 900 C, 45 min. The solid line in the topfigure represents carbon simulation due to kick-out and including the emissionof interstitial carbon from C-rich carbon clusters, while the dashed linerepresents the simulation without the clustering. Fickian diffusion isrepresented in dotted line [65] . 43Figure 16: SIMS (symbols and gray scattered lines) and simulated (thick solid lines)profiles of B and C after inert RTA anneals of a boron spike in aSio.84sGeoJsCo.oozlayer for 10 sat temperatures of: (a) 1020, (b) 1050, and (c)1070 C. The as-grown carbon box profile has a peak concentration of1.1x1020 cm-3 62Figure 17: Comparison of our simulations (thick solid lines) for the diffusion of B spikesand C with the experimental annealed profiles (symbols) after a thermalbudget of 900 C, 45 min in N2 Data extracted from Ref. [65] . 63Figure 18: Diffusion of B from highly doped substrates in Si with and without C doping.Thick solid lines are the calculated profiles and symbols are SIMS data. Dataextracted from Ref. [46] . 64Figure 19: Simulations (thick solid lines) and annealed (symbols) profiles of B and Cupon implantation and annealing of: (a) reference sample without C, and (b)sample with a SiGeC layer. Data extracted from Ref. [13] . 65Figure 20: Complete suppression of B TED in Si due to a C-rich layer acting as trap forinterstitials. Calculated and annealed profiles are plotted in thick solid linesand symbols respectively. Data extracted from Ref. [45] . 67Figure 21: Simulated (thick solid line) and experimental (symbols) profiles of a B dopingsuperlattice with a C-rich layer, upon implantation and annealing. Dataextracted from Ref. (53] . 68ix
Figure 22: Comparison between simulation (thick solid line) and experiment (symbols)for B peaks diffusion without (a) and with (b) a Sio.79sGeo. 2Co.oos layer, after athermal budget of 850 OC for 30 min in 0 2 Data extracted from [75] . 70Figure 23: Boron OED suppression by a SiGeC layer intervening two boron-doped Silayers upon an oxide ambient anneal. Simulated and annealed profiles areplotted in thick solid lines and symbols respectively. Data extracted from Ref.[76] . 71Figure 24: Experimental (symbols) and calculated (thick solid lines) carbon diffusionprofiles for inert anneals at 850oc for (a)1 hr and (b) 3 hr. Data extractedfrom Ref. [48] . 73Figure 25: Experimental (symbols) and simulated (thick solid lines) carbon diffusionprofiles for inert anneals at 900 OC for (a) 17 min and (b) 1 hr. Data extractedfrom Ref. [48] . 74Figure 26: Diffusion of Sb from highly doped substrates in Si with and without C doping.Thick solid lines are the calculated profiles and symbols are SIMS data. Dataextracted from Ref. [46] . 76Figure 27: Variation of the reverse clustering rate with temperature for the simulatedexperiments . 77Figure 28: The effect of excluding the clustering reaction on the calculated profile (thicksolid line) for C diffusion of a carbon doping superlattice after annealing inN 2 The annealed SIMS profile is plotted in symbols. Data extracted from Ref.[48] . 80Figure 29: The effect of excluding the clustering reaction on the simulated profiles (thicksolid lines) for B and C upon implantation and annealing. Symbols representthe SIMS data. Data extracted from Ref. [13] . 81Figure 30: Simulations without the Ff reaction (thick solid lines) for the diffusion of Bspikes and C at 900 C, 45 min in N 2 Symbols are SIMS data. Data extractedfrom Ref. [65] . 83X
LIST OF TABLESTable I:Relative performance comparisons of various device technologies (Excellent: ;Very good: ; Good: 0; Fair:-; Poor:--) [4] . 4Table II:Literature values for C transport parameters that are used in several studies. 38Table ill: Boron equilibrium diffusivity [63] and the diffusivities of the boron-defectpairs . 56Table IV: Silicon self-diffusion data used in our model. . 58xi
LIST OF COMMON SYMBOLSISilicon self-interstitialvSilicon vacancyGeneral dopant atom occupying a substitutional siteGeneral dopant atom occupying an interstitial siteAIDopant-interstitialcy pairAVDopant-vacancy pairBBoroncCarbonSubstitutional carbon atomInterstitial carbon atomInterstitial-substitutional carbon pair (Cluster)Concentration of X (I, V, B, Ci, Cs, Cl, etc.)c;, c Equilibrium concentration of XFlux of XDiffusivity coefficient of Xv;,v Equilibrium diffusivity of XEffective diffusivity of XFractional interstitial component of diffusionXll
n;Intrinsic carrier concentrationpHole concentrationnElectron concentrationKOKick-out reactionFfFrank-Turnbull reactionClClustering reactionkf,ReactionForward reaction ratekr,ReactionReverse reaction ratexiii
M.A.Sc. - Samer RizkMcMaster University- Electrical EngineeringChapter 1INTRODUCTIONResearch in semiconductor device physics and technology has been sustained forthe last decades and has continually improved the state of the art. To date, thesemiconductor industry has been successful in exponentially shrinking the minimumfeature size thus increasing the number of transistors on a chip, increasing devicebandwidth, and increasing circuit complexity and functionality, as predicted by Moore'sLaw [1]. Several innovations have allowed this continual shrinking from one technologygeneration to the next. The incorporation of the silicon germanium (SiGe) alloy is onesuch development. This has been explored since the early nineties, and has seenincreasing use in mainstream Si-based products over the last few years.Semiconductor alloys provide device engineering with a wide range of energygaps and mobilities, so that materials are available with properties that match specificapplications. They demonstrate superior properties beyond the physical limits of theelectronic properties of silicon, which is the most popular material used today to makeelectronic devices. Gallium arsenide (GaAs) and indium phosphide (InP) are compoundsemiconductors that have found application m niche markets due to their superior1
M.A.Sc. - Samer RizkMcMaster University- Electrical Engineeringperformance. By contrast, SiGe is attractive because of its compatibility withconventional Si technology.Being a low cost microelectronic technology for integrated circuits, silicontechnology has dominated the semiconductor industry for over 30 years [2], and theprocessing of silicon has become a mature technology. SiGe can be integrated withtraditional Si technology allowing the introduction of fast switching heterojunctionbipolar transistors (HBTs) with SiGe forming the base material in a BiCMOS process, orbeing incorporated in MOSFETs with SiGe forming the channel to enable devicemanufacturers to overcome challenges to the further scaling of CMOS.As device dimensions continue to shrink controlling the profiles of dopantsincorporated in the host material becomes critical. Significant degradation of deviceperformance is known to occur due to the diffusion of dopants over short distances. Thus,the fabrication of microelectronic devices, and consequently their performances are basedon tightly controlling the dopants' profiles in spatially restricted host regions.Theintroduction of the SiGe alloy in local device regions further complicates the problembecause it adds the need to control the diffusion of Ge atoms in the host alloy as well asdopant atoms in the alloy. As well, the behavior of dopants must now be understood andcontrolled in a background of varying composition. Modeling dopant diffusion istherefore fundamental in order to have accurate and predictive process simulators, andphysics-based models for dopant diffusion are required to provide process engineers withinsight into process development, not just as a tool to empirically model a process flowafter the fact.2
McMaster University -Electrical EngineeringM.A.Sc. - Samer Rizk1.1MotivationThe advent of SiGe HBTs represents the first practical bandgap-engineering of Si-based transistors. When Ge is introduced into Si, the SiGe alloy features a bandgapsmaller than that of Si, making Si suitable for bandgap engineering. This is due to the factthat Ge has a larger lattice constant than Si, and consequently a smaller bangap (0.66 eVversus 1.12 eV). Furthermore, the lattice constant mismatch produces compressive strainin SiGe alloys, which results in an additional bandgap reduction. The net bandgapshrinkage in SiGe alloys is approximately 7.5 meV per 1 %of Ge [3]. This "band offset"occurs primarily in the valence band of the SiGe base, making it conductive for use innpn transistors, as illustrated in Figure 1.Strained SiGeSi Substrate11. - Figure 1: Schematic band alignments of strained SiGe grown on a Si substrate [4].In addition, the introduced compressive strain lifts the conduction and valence banddegeneracies at the band extremes, which results in reducing the density of states, henceimproving the carrier mobilities with respect to silicon. Moreover, to achieve highfrequency performance, the base region of a transistor must be thin, which makes SiGe a3
McMaster University- Electrical EngineeringM.A.Sc. - Samer Rizknatural candidate for use in the base region, since practical SiGe layers must be very thinto ensure stable, defect-free layers. In SiGe npn HBTs, the bases can be heavily doped toreduce the base resistance, and hence increase the maximum oscillation frequencyf max Heavily doped bases also result in reducing the minimum noise figure [5],[6],[7]. Inconclusion, SiGe HBT technology is far superior to Si bipolar transistors; they havehigher de-current gainf3, higher maximum frequency f max , higher cut-off frequency JT,lower gate delays, and better broad-band noise characteristics [3]. Table I presents arelative performance comparison of various device technologies for RFICs including theSiGe HBT and Si BJT.Table 1: Relative performance comparisons of various device technologies (Excellent: ; Very good: ; Good: 0; Fair: -; Poor: --) [4].Performance MetricFrequency Response1/f and phase noiseBroadband NoiseLinearityOutput conductanceTranconductance/areaPower dissipationCMOS integrationIC costSiGeHBTSiBJT 0 0 0 0Ill-VSiCMOS MESFETIll-VHBTIll-VHEMT0 0 0 0N/A However, the key feature of SiGe HBTs, the thin heavily doped base, makes ithighly susceptible to base dopant (boron) outdiffusion during device processing.Diffusion of base dopant in SiGe HBTs into the neighboring silicon emitter and collectorresults in significant degradation in device performance [8]. For example, while typical4
M.A.Sc. - Samer RizkMcMaster University- Electrical Engineeringbase doping in HBTs is in the range of 1019 cm-3, the emitter doping level is kept below5x10 18 cm-3 to avoid tunneling [8]. Any base dopant outdiffusion will therefore not becompensated by the n emitter dopant. Hence, outdiffusion will lead to the broadening ofthe base profile pushing the pn base-emitter junction into the silicon. This introducesparasitic electron barriers in the conduction band that lead to reducing the collectorcurrent which is exponentially dependent on the barrier height. Only a few nanometersshift may cause severe device degradation, decreasing the gain, Early voltage and speed[8],[9]. Base dopant outdiffusion is typically caused by transient enhanced diffusion(TED) due to arsenic emitter and/or extrinsic boron implantation and anneal, which aretypical steps in a BiCMOS process flow. Attempting to maintain low thermal budgets andeliminating implantation and annealing to minimize boron outdiffusion would imposestringent limits on the integration of SiGe into base line silicon technology.It is therefore a key issue to SiGe HBT technology to retain the narrow high-concentration as-grown base profile of the SiGe base. The optimization of the Ge profilein SiGe HBTs is not sufficient to suppress boron outdiffusion. Undoped SiGe spacerlayers grown on either side of the doped SiGe base have been studied to accommodatefor the pn junction shift into the silicon regions [10]. However, the "critical thickness" ofthe SiGe strained films poses a limitation on the thickness of these undoped spacer layers.Recently, it has been shown that the incorporation of substitutional carbon into Si/SiGehas the effect of highly suppressing boron diffusion [11],[12],[13]. Original research ofadding carbon to compressive-strained SiGe was motivated by carbon's ability to reducestrain and enhance thermal stability [ 14]. Suppression of boron diffusion in the base of5
M.A.Sc. - Samer RizkMcMaster University- Electrical EngineeringSit-x-yGexCy HBTs via uniform and localized low carbon concentrations (-1020 cm-3) wasshown to significantly enhance HBT technology performance [9],[15],[16].The focus of this work is modeling the suppression of boron diffusion in Si/SiGedue to the incorporation of localized substitutional carbon concentrations. Several studiesexist that model the suppression of boron diffusion due to carbon. Each of these studieshas focused on a subset of the available experimental data and sample structures. Severalstudies have considered the ability of carbon to reduce enhanced boron diffusion due toion implantation or oxidation. Different approaches for incorporating the effect of carbonhave been adopted. In this work, we present a consistent and complete model thataccounts for carbon diffusion and its effect on boron diffusion. In the developed modelwe make use of the well-established published data for carbon diffusion, as well as borondiffusion and silicon self-diffusion. The model successfully accounts for boron andcarbon behaviors in a wide range of sample structures and experimental conditions overthe complete temperature range of 750- 1070 OC in inert and oxidizing ambients, as wellas in the presence of implant damage. The samples presented include a variety ofexperimental structures, in addition to recent experimental results obtained throughcollaboration. The model successfully accounts for the boron diffusion reduction underrapid thermal annealing (RTA), as used in typical BiCMOS technologies (1000-1100 OC)for dopant activation. It also accounts for the suppression of boron transient enhanceddiffusion (TED) and oxidation enhanced diffusion (OED).6
McMaster University- Electrical EngineeringM.A.Sc. - Samer Rizk1.2MethodologyThis section presents the methodology and procedural flow typically followed tostudy impurity behavior in semiconductor materials, with focus on samples relevant tothis research work. The study of dopant diffusion in a sample structure includesexperimental and simulation work.Dopants are normally introduced into test structures during wafer growth. Growthtechniques include CVD (chemical vapor deposition) and MBE (molecular beamepitaxy). Ion implantation, which is a typical BiCMOS process step, is known to result inthe creation of point defect (primarily interstitial) damage in the implant region. Toobserve the effect of ion implantation on dopant diffusion in some samples, the samplesundergo an implantation step.Test structures with grown-in dopants, implanted orunimplanted, are then subjected to annealing in either inert or oxidizing ambients atdifferent temperatures. Conventional annealing methods include furnace annealing andrapid thermal annealing. Dopant concentration profiles in unannealed and annealedsamples are then measured for comparison between the initial and final profiles,respectively. Typical techniques for measuring concentration profiles include SIMS(secondary ion mass spectrometry), TOF-SIMS (time-of-flight secondary ion massspectrometry), and RBS (Rutherford backscattering spectrometry). SIMS is the techniqueused for most samples studied in this work, and will be described later.To simulate the experimental results, we performed lD simulations using theFLOOPS-ISE software [17], which is a reliable and widely-used process simulator.FLOOPS-ISE features advanced physical models for semiconductor fabrication7
M.A.Sc. - Samer RizkMcMaster University- Electrical Engineeringprocesses, as discussed in Chapter 2. In addition, it provides flexibility to define newmaterials and models (fu
Modeling carbon diffusion and its role in suppressing boron diffusion in silicon and SiGe has been studied by several groups. While boron diffusion is well-established, different modeling regimes have been developed for carbon diffusion. Each of the existing studies has focused on subsets of the available experimental data. We present a
EMA 5001 Physical Properties of Materials Zhe Cheng (2016) 4 Self-Diffusion & Vacancy Diffusion Diffusion of Vacancy vs. Substitutional Atoms Continue from p. 7 2 Therefore, Diffusion coefficient of vacancy vs. substitutional atom For self-diffusion 2 The relationship between jump frequency is Since the jump distance is the same
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Diffusion - Chapter 7 SILICON VLSI TECHNOLOGY Fundamentals, Practice and Modeling . Upper Saddle River NJ 1 DIFFUSION - Chapter 7 Doping profiles determine many short-channel characteristics in MOS devices. Resistance impacts drive current. Scaling implies all lateral and vertical . boron diffusion process (say for the well .
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