2008 IMPACT Workshop. Faculty Presentation: CMP. By David Dornfeld, Mechanical Engineering, UC-Berkeley IMPACT CMP 1

Transcription

1 2008 IMPACT Workshop Faculty Presentation: CMP By David Dornfeld, Mechanical Engineering, UC-Berkeley IMPACT CMP 1

2 CMP: Modeling and Fundamental Studies Current Milestones CMP 1. Continue development of comprehensive model of CMP Model building on the abrasive scale, pattern scale capability to integrate additional chemical process elements and coupling elements (including pad/wafer contact elements) to link key influences of chemical and mechanical activity, slurry agglomeration and heating. CMP 2. FEM Analysis of pad-induced effects during planarization- basic Finite element model to predict stress induced by pad on wafer surface including influences of material (geometry and material properties), pad (surface topography and material properties) and process conditions (load, motion) with analysis of pad mechanical behavior, pad/surface interaction and induced stresses in thin films. CMP 3. Assess mechanical properties and behavior of passive films on copper and test patterns Nanomechanical techniques used to measure pertinent properties of the films on copper alone, and test patterns to understand the coupling between the electrochemistry, colloid chemistry and mechanical effects. CMP 4. Develop understanding of agglomeration/dispersion effects on CMP Basic understanding of agglomeration/dispersion effects on CMP including rate of agglomeration as a function of chemistry and the wafer surface hardness as a function of chemistry CMP 5. Mechanics of Nanoscale Lapping and Polishing Mechanics models for material removal at the nanoscale with validation using microprobe-based experiments and stochastic modeling of nanoscale polishing/lapping. IMPACT CMP 2

3 CMP - Faculty Team Kyriakos Komvopoulos Mechanical Engineering UCB Mechanical Phenomena David A. Dornfeld Mechanical Engineering UCB Fiona M. Doyle Materials Science and Engineering UCB Chemical Phenomena Interfacial and Colloid Phenomena Jan B. Talbot Chemical Engineering UCSD IMPACT CMP 3

4 CMP - Student Team Huaming Xu ME-UCB Seungchoun Choi ME UCB Mechanical Phenomena Adrien Monvoison ME-UCB Moneer Helu ME-UCB (NSF) Shantanu Tripathi ME/MSE UCB Chemical Phenomena Interfacial and Colloid Phenomena IMPACT CMP 4 Robin Ihnfeldt Chem Eng UCSD

5 Research Results Seungchoun Choi & Shantanu Tripathi: Use of Confocal Microscopy to Characterize Pad Asperity-Wafer Contacts and Abrasive- Wafer Contacts During CMP Shantanu Tripathi: CMP Modeling as a part of Design for Manufacturing Adrien Monvoisin: Stress Analysis in Low-K dielectric Materials during the CMP Process Robin Ihnfeldt: Effects of slurry chemistry on Cu CMP process Huaming Xu: Mechanics of Nanoscale Lapping and Polishing Details on the posters! IMPACT CMP 5

6 Seungchoun Choi Motivation Use of Confocal Microscopy to Characterize Pad Asperity-Wafer Contacts and Abrasive-Wafer Contacts During CMP Integrated tribo-chemical model of copper CMP considers abrasive and pad properties, process parameters (speed, pressure etc.), and slurry chemistry to predict material removal rates. Information on the abrasive-copper and/or asperity copper interaction force and frequency is needed to complete the integrated tribo-chemical modeling of copper CMP. Confocal reflectance interference contrast microscopy (C-RICM) has been used to study pad-wafer contacts and shows promise for detection of smaller objects such as agglomerated abrasive particles. Information about the pad-asperity contact area and distribution has important applications in modeling the pattern-dependence of CMP, and in DfM. IMPACT CMP 6

7 Seungchoun Choi 2008 Main Objective Complete comprehensive model of CMP for homogeneous substrate, and start adapting to account for pattern dependence Complete the model that links mechanical and electrochemical characteristics using abrasive-wafer and asperity wafer interactions as the physical link. Investigate whether the surface potential of the pad influences material removal rates Draw on abrasive scale and pattern scale capabilities to extend model to DfM applications. IMPACT CMP 7

8 Seungchoun Choi Slurry chemistry (ph, conc. of oxidizer, inhibitor & complexing agent) Pad properties layers hardness, surface potential, structure Abrasive Type, size & conc. Polishing conditions (pressure P, velocity V) Material being polished Incoming topography Integrated tribo-chemical model 1. Passivation Kinetics 2. Mechanical Properties of Passive Film 3. Abrasive-copper Interaction Frequency & Force The Problem Removal Rate (RR) Planarization, Uniformity, Defects Lack of understanding of abrasive-pad interaction on pad asperities: - Inability to predict and control material removal rate and defects - Limits application to Design for Manufacturing (DfM) and Manufacturing for Design (MfD) IMPACT CMP 8

9 Mechanical Interaction: Frequency & Force* Can be found if we can measure/calculate: - Number, size & distribution of wafer-asperity contact - Number & distribution of abrasives per asperity-wafer contact. wafer Stress (MPa) RR = abrasive particles pad asperity Time (µs) M Cu i( t 0 + t ) dt nf τ IMPACT CMP 9 τ Interval between two abrasive-copper contacts (τ) ρ 0 * Tripathi et al, 2006 Proceedings of VLSI Multilevel Interconnection Conf. Stress (MPa) Muldowney, MRS Symp. Proc. Vol. 816 We can lump multiple abrasive contacts within an asperity. M Cu : Atomic mass of copper ρ : density of copper n : # e - transferred F : Faraday s constant i : oxidation rate Time (ms) t 0 : time immediately after an abrasive-copper interaction t : time since an abrasivecopper interaction, before the next interaction

10 Seungchoun Choi Wafer-Asperity Contact* Pad asperity-wafer contacts were studied by using C-RICM C-RICM image of asperity cluster contact on VP3000 TM pad * C. L. Elmufdi and G. P. Muldowney, MRS Symp. Vol. 914, 2006 IMPACT CMP 10 C-RICM image sequence of VP3000TM pad at increasing applied pressure

11 Schematic depiction of reflecting interfaces in pad asperity-wafer contact area C-RICM utilizes the interference of light at material boundaries to identify contact points between surfaces Rslurry-coverslip Rcoverslip-abrasive Rcoverslip-pad Rpad-abrasive Rslurry-abrasive Rslurry-pad Cover slip abrasive particles pad asperity slurry IMPACT CMP 11

12 Anticipated Experimental Procedure Slurry Abrasives - 40 wt% α-alumina slurry - 150nm average aggregate diameter - 20nm primary particle diameter Copper CMP slurry - Filtered DI water with 1 mm KNO 3, 0.1 M glycine and 0.1 wt % H 2 O 2 + small amount of α-alumina particles ph of slurry will be adjusted using KOH or HNO 3 Copper nano-particles mm to simulate removal of copper surface during CMP - <100 nm in diameter Confocal Microscopy (reflection mode) - Four samples will be prepared for slurry ph of 4, 9 and 10 without Cu particles and ph 7.5 with Cu particles - SMART pads developed under earlier award will be useful for calibrating the images, and providing brightness information for each interface. IMPACT CMP 12

13 Challenges with Confocal Microscopy Resolution: - Determined by rlateral = 0.4 λ / NA where λ is the wavelength of light and NA is the numerical aperture of the objective - In practice, the best horizontal resolution is about 200 nm - Particles smaller than 200 nm cannot be resolved if they are closer than 200 nm each other. Airy Disk: - Due to Fraunhofer diffraction of light passing through a circular aperture - Distinguishing the Airy pattern from real contact points may need substantial image processing technique. Use fluorescent quantum dots, or abrasives to which a fluorescent marker is selectively adsorbed as tracers to assist the optical imaging, if needed IMPACT CMP 13

14 Seungchoun Choi Future Goals Confocal imaging for various slurry chemistries Complete integrated tribo-chemical model of copper CMP Using the results of this research, determine whether material removal is due to abrasive particles being forced against the wafer by asperities, to convective transport of abrasive particles suspended in the slurry to the wafer surface where they interact, or to a combination of these mechanisms. IMPACT CMP 14

15 Shantanu Tripathi Motivation CMP Modeling as a part of Design for Manufacturing CMP causes non uniform removal on patterned device wafers: defects like dishing & erosion. CMP challenges (from ITRS) Reliably predicting and controlling post-cmp topography dishing & erosion < 10% interconnect height Integration of ultra low-k dielectric materials predicting stresses and damage Designing new planarization processes for new materials and new requirements. Boning et al. MIT IMPACT CMP 15

16 Shantanu Tripathi 2008 Main Objective Continue development of comprehensive model of CMP: Continue development of model building on the abrasive scale, pattern scale capability to integrate additional chemical process elements and include coupling elements for linking key influences of chemical and mechanical activity and slurry agglomeration and heating. Consider pad/wafer contact elements. CMP model validation and design for manufacturing validation: Validate model capability with full scale model verification by simulation and test (with industrial partners). Development of strategies for model-based process optimization. Consider use of model for DfM relative to process variation. IMPACT CMP 16

17 Shantanu Tripathi Challenges Present methods treat CMP process as a black box; are blind to process & consumable parameters Need detailed process understanding For modeling pattern evolution accurately Present methods do not predict small feature CMP well For process design (not based on just trail and error) Multiscale analysis needed to capture different phenomena: At sufficient resolution & speed CMP process less rigid than other processes: possibility of optimizing consumable & process parameters based on chip design MfD & DfM Source of pattern dependence is twofold: Asperity contact area (not addressed yet) Pad hard layer flexion due to soft layer compression (addressed by previous models) IMPACT CMP 17

18 Shantanu Tripathi Present Approach adopted by CAD companies Extensive tests/ measurements required CMP Test-Pattern Wafer Measurements & Parameter Extraction Present Modeling Approach Present methods: Treat CMP process as a black box Lack of process understanding Use trial & error for process design, no process optimization Specific to particular processing conditions Model Calibration Product Design Layout Model Inputs CMP Evolution Model Simulation Full-Chip Prediction Model drawbacks: Do not predict small features correctly Captures only 1 source of pattern dependency Coarse (resolution ~10µm) Design Optimization Helps in dummy fill - Partial product design improvement but no process optimization Optimization should be across process & product: - Need to be able to tune all the available control knobs IMPACT CMP 18

19 Proposed Pattern Evolution Framework Consumables Polishing Conditions before 40sec CMP Material Removal Model τ M Cu RR = i( t0 + t) dt nfτ ρ 0 K MRR( x, y) = ρ( x, y) 0.112μm/0.1681μm STI oxide evolution* Small feature prediction problems Space Discretization: Data Structure Time step evolution Asperity contact area (µm) R Empirically fit, based on pad flexion (scale=mm) *Choi, Tripathi, Dornfeld & Hansen, Chip Scale Prediction of Nitride Erosion in High Selectivity STI CMP, Invited Paper, Proceedings of 11 th CMP-MIC, 2006 IMPACT CMP 19

20 Summary of Current Progress Integrated chemo-mechanical modeling of material removal Data structure for capturing multiscale behavior: tree based multi-resolution meshes Pad/Wafer (~m) Die (~cm) Asperity (~µm) Feature (45nm-10µm) Abrasive contact (10nm) Pattern Evolution Model for HDPCVD STI Chip Layout Pattern density Evolution IMPACT CMP 20

21 Multiscale Optimization Example Address WIDNU (Within die non-uniformity) at different levels depending on available flexibility Change pad hardness (tree level 1) Inflexibility: scratch defects, pad supplier Change incoming topography (feature level) Inflexibility: deposition process limitation Dummy fill (chip, array level) Inflexibility: design restrictions Within die non-uniformity Nitride Thinning in STI Change chemical reactions, abrasive concentration (abrasive level) Inflexibility: removal rate requirements Slurry Computational Performance Prediction: New approach based on adaptive meshes Storage Space Speed MIT/Cadence Approach ~10MB µm resolution) ~60s resolution) New Approach * ~0.01MB(@20µm) 100MB(@200nm) ~0.6s (@20µm resolution) *Based on: Udeshi T, Parker E; J. COMPUTING & INFORMATION SCIENCE, MAR 2004 IMPACT CMP 21

22 Shantanu Tripathi Future Goals Continued development of CMP process models Progress of data structure implementation Verification of the proposed DfM approach IMPACT CMP 22

23 Adrien Monvoisin 2008 Main Objective Stress Analysis in Low-K dielectric Materials during the CMP Process Introduction of Low-k dielectric materials (LKD, k<3.5) and Copper to reduce RC interconnects delay: LKD have poor mechanical properties hence delamination and cracks may occur during the CMP process: need to understand these phenomena Create a model on ABAQUS using the Finite Elements Method (FEM model) Set up the Boundary Conditions and the Loads to reproduce the CMP conditions. The model has various parameters such as the Young Modulus of the Low-K dielectric material used, the thickness of the copper layer removed, the pressure applied on the wafer, Analyze the stresses induced by the CMP process From the FEM results, analysis of the Von Mises stresses which can lead to the propagation of cracks in the sub layers polished. Understand the creation and propagation of cracks in low-k interfaces IMPACT CMP 23

24 Adrien Monvoisin Abaqus Model Boundary Conditions: No displacement / rotation at the bottom Periodic Boundary conditions on the sides Loads: Downward constant pressure: 2 psi Horizontal Frictional Force: 0.7 psi Materials: Copper Tantalum Low-K 5-20Gpa IMPACT CMP 24

25 Adrien Monvoisin Abaqus Results Cu thickness = 250nm Cu thickness = 50nm Low-k material Stresses Stresses 5 GPa 190 kpa 177 kpa 20 GPa 67 kpa 62 kpa E=20 Gpa E=5 Gpa E=20 Gpa E=5 Gpa Highest stresses located at the edge. Cracks may first appear at these locations. Higher stresses for low-k with a lower E (poor mechanical Properties) Stresses are higher with Cu layer is thicker (when CMP starts) IMPACT CMP 25

26 Adrien Monvoisin Tradeoff solution Low-k, E=20Gpa Low-k, E=5Gpa Tradeoff solution with 2 layers of low-k stacked: one with a higher E to resist to the stress and one with a lower E underneath, less affected by the stresses induced by CMP to reduce the RC interconnect delay because of its porosity. IMPACT CMP 26

27 Adrien Monvoisin Fatigue Phenomenon Stress on the 2 nd layer more important than those on the 1 st layer: need to consider the fatigue phenomenon The delamination increases with number of layers because of the effect of the stack residual stress and elasticity. Dependence of CMP-induced delamination on number of low-k dielectric films stacked Patrick Leduc IMPACT CMP 27 Masako Kodera

28 Adrien Monvoisin Future Goals Analyze the influence of the damascene process: Coefficient of Thermal Expansion influences the resistance of low-k dielectric materials regarding fractures. Determine what is the theoretical fracture energy for the low-k dielectric that I used and evaluate the driving force caused by CMP. Expand this model to any low-k and investigate the crack propagation (crack path) Confirm these results with SEM experimental tests. Investigate the influence of the fatigue phenomenon. IMPACT CMP 28

29 Robin Ihnfeldt 2008 Main Objectives Effects of slurry chemistry on Cu CMP process Colloidal behavior measured by zeta potential and agglomerate size distribution - effects of chemical additives and presence of copper Studied effects of slurry chemistry on copper surface hardness and etch rate Used agglomerate size distribution, nanohardness and etch rates in model of CMP IMPACT CMP 29

30 Measuring Nanohardness and Etch Rates Hardness Measurements - TriboScope Nanomechanical Testing system, Hysitron Inc nm Cu sputter deposited on 30 nm Ta on 1 cm 2 silicon wafer pieces 10 min exposure in 100 ml of solution (without abrasives), removed, dried with air and measured Maximum applied load varied from μn Etch Rates - wafer pieces weighed before and after immersion in solution IMPACT CMP 30

31 Nanohardness Before Chemical Exposure Hw (GPa) Indentation Depth (nm) Hardness technique Material Value Nanohardness (GPa) Cu ± 0.3 Ta ± 1.5 Si 3 12 ± 2 Cu 2 O* 17 ± 5 *as measured in this study Average Hw=2.6 GPa 1 D. Beegan, S. Chowdhury, and M. T. Laugier, Surface and Coatings Technology, 210, 5804 (2007). 2 A. Jindal and S.V. Babu, J. Electrochemical Soc., 151 (10), G709-G716 (2004). 3 M. Ueda, C. M. Lepienski, E.C. Rangel, N. C. Cruz, and F. G. Dias, Surface and Coatings Technology, 156, 190 (2002). 4 A. Szymanski and J. M. Szymanski, Hardness Estimation of Minerals Rocks and Ceramic Materials, Elsevier Science Publishers B. V., New York, NY (1989). Nanohardness versus indentation depth Nanohardness near surface (<20nm) is >Cu metal indicating copper oxide At >30nm, hardness of Cu metal Hardness technique Material Value Moh's hardness 4 Cu(OH) Cu metal 3 CuO 3.5 Cu 2 O 4 Ta 6.5 Si 6.5 IMPACT CMP 31

32 Robin Ihnfeldt Effect of ph on Hardness Hw (GPa) Indentation Depth (nm) ph 2.9 ph 8.3 ph 11.7 *M. Pourbaix, Atlas of Electrochemical Equilibria in Aqueous Solutions, National Association of Corrosion Engineers, Houston, Texas (1974). Near surface (<40nm) hardness increases as the ph increases Consistent with potential-ph equilibrium diagrams which indicate that copper oxides are more stable at higher ph* Nanohardness is that of Cu metal for >70nm IMPACT CMP 32

33 Robin Ihnfeldt Effect of Additives on Hardness Nanohardness versus indentation depth after exposure to aqueous solutions with 0.1M glycine and 2wt% H 2 O 2 at various ph Possible Surface Reactions: passivation 0.1M Glycine, 2.0wt% H2O2 ph 8.3 2Cu + H 2 O -> Cu 2 O + 2H + + 2e 0.1M Glycine, 2.0wt% H2O2 ph 10.0 Cu 2 O + H 2 O -> 2CuO + 2H + + 2e Hw (GPa) Indentation Depth (nm) complex formation Cu 2 O+4HL ->2CuL 2 (s)+h 2 O + 2H + + 2e CuO + 2HL -> CuL 2 (s) + H 2 O dissolution CuL 2 (s) -> CuL 2 (l) decomposition H 2 O 2 + e - > OH* + OH - At ph 8.3 the film is very soft (possibly porous) and etch rate is large (56 nm/min) H 2 O 2 decomposition occurs faster at higher ph.* At ph 10.0, large etch rate, 33 nm/min, indicating possibly a thick passivation layer forms which inhibits Cu-glycine complex formation. * G. Xu, H. Liang, J. Zhao, and Y. Li, J. Electrochemical Soc., 151, (10) G688 (2004). IMPACT CMP 33

34 Robin Ihnfeldt Conclusions Surface hardness is very sensitive to the chemistry of the solution! Small changes in chemistry (ph, additive concentration, etc.) can cause large changes in the hardness ( GPa) Future Work Study effects of exposure time of solution on hardness (film formation due to fast reaction or slow reaction) Surface hardness measurements performed in this study may not be representative of surface hardness that occurs during a CMP process Different measurement technique? Study effects of temperature IMPACT CMP 34

35 Huaming Xu Motivation Mechanics of Nanoscale Lapping and Polishing Increasing demands for extremely high-density recording have led to very tight tolerance requirements for the head-disk interface. This has necessitated ultra-smooth (rms < 0.2 nm) recording head surfaces. Optimization of the lapping/polishing process to achieve extremely high-density recording with direct implications to other technologies relying on surface smoothness and flatness. Development of stochastic mechanics models for material removal rate at the nanoscale. IMPACT CMP 35

36 Huaming Xu 2008 Main Objective Analyze the mechanisms of the plate charging process and develop mechanics models. Analyze the mechanisms of the lapping process and develop analytical models for material removal rate and resulting surface roughness. Bridge the gap of knowledge in nanoscale lapping mechanics, in particular as it pertains to ultra-smooth surfaces of magnetic recording head media. Analyze material removal processes in terms of important lapping parameters and material properties. IMPACT CMP 36

37 Huaming Xu The Problem Low diamond particles density of charged lapping plate. Mechanism of single diamond particle embedment process Probabilistic analysis of embedded diamond particles on the lapping plate Insufficient longevity and efficiency of lapping plates. Parametric study of the longevity of the lapping plate Study on material removal mechanism during lapping process Demand for sub-nanometer surface roughness for magnetic recording heads Probabilistic analysis of lapping process IMPACT CMP 37

38 Huaming Xu Charging Process Models Experiments Hydrodynamic Model Probabilistic Model Experiments of Charging Process Kinematics (Tool, Plate rpm ) Mean gap distance Fluid Properties (Viscosity) Topography properties Load Charge Density Geometry Particle distribution Output Friction Coefficient Material Properties Particle Debonding Debonding Model IMPACT CMP 38

39 Huaming Xu Hydrodynamic Model Purpose: Estimate the mean gap between the two surfaces during the charging process. Basic Equations: (1) Modified Reynolds equation F ( θh) ( θh) rur + Uφ = r φ Charge Ring 3 3 L M h g h g y Tin Plate Assumptions: (1) The charge ring is tilting(α, β). (2) Self-balance for Moments. Numerical Methods: (1) Finite Difference Method (2) Newton-Raphson Method M x β ( θ 1) 1 β ( θ 1) ( r ) + ( ) r 6μ r r φ 6μ φ (2) Local gap distance h= f( h, α, β ) (3) Boundary conditions p( r = r ) = p ; p( r = r ) = p min 0 max 0 (4) Force and moment equilibrium equations r max 2π Fh (, αβ, ) = ( p patm) rdθdr L= 0 rmin 0 rmax 2π 2 Mx( h, αβ, ) = pr sinθθ d dr = 0 rmin 0 rmax 2π 2 M y ( h, αβ, ) pr cosθθ d dr 0 = = rmin 0 IMPACT CMP 39

40 Huaming Xu Hydrodynamic Model(cont d) Geometry (along radial direction): rmin rmax Results: Fluid p 0 Conclusions: The mean gap distance decreases with increasing load (pressure). Parameters: Tin Plate Charge ring velocity 20 rpm Maximum radius 67.5 mm Plate velocity 30 rpm Minimum radius 30 mm Viscosity 3.2 cp Distance between centers 130 mm IMPACT CMP 40

41 Huaming Xu Probabilistic Charging Model Charging Process Slurry Assumption: Diamond particles with sizes larger than the local gap can be embedded into the tin layer. Particle density function n ( particles/unit area): 2 k IMPACT CMP 41 h z' 1 n= f( dk) f '( z') f "( z'') dz'' dz' dd d dk k v slurry f '( z '), f "( z") : PDFs of height distribution of top and bottom surfaces f ( dk ): h : PDF of diamond particle size distribution Mean gap distance between two surfaces z ' h z L z "

42 Input parameters: 1 2 Results: Probabilistic Charging Model(cont d) σ = 15 nm; σ = 15 nm; μ = 100 nm; σ = 15 nm; A = 1 μm d Variable : top surface σ Variable : bottom surface σ 2 1 d 2 Conclusions: (1) Particle density decreases with mean gap distance. (2) For mean gap distance larger than mean particle size, the charge density increases with surface roughness; otherwise, the charge density decreases with surface roughness. IMPACT CMP 42

43 Huaming Xu Debonding Model Reason: When a diamond particle is not sufficiently embedded into the tin layer, it could be removed upon application of a lateral force. Debonding Condition: M F > M int Input: Effective particle radius, friction coefficient, material properties. F L Equations: r θ β L int R γ τ i p Conclusion: δ L int F L M F M int π /2 θ 3 2 int = 4 0 τ sin cos 4 0 i β γ β γ = τ i δ F = = μ mπ θ = μ mπ δ δ 2 2 F > int μ mπ (2 δ δ ) > 4τiδ M R d d R M FR p R sin p R(2 R ) M M p R R R δ τ δ i crit i i 2 τ f, τ < = = μ R μπ pm R μπ pm pm IMPACT CMP 43

44 Probabilistic Model-Debonded Particles Probability after particle debonding αdk h z' + crit < k = < k = Pz ( δ d) Pz ( αd) f'( z') f"( z'') dz'' dz' 2τ i α = ( α 1) μπ p m Particle density function n: 1 n= P( z < αd ) f( d ) dd d = 2 k k k k αdk h z' 1 2 k d k f ( d ) f '( z ') f "( z '') dz '' dz ' dd k Result: Parameters: σ = 15 nm; σ = 15 nm; 1 2 μ = 100 nm; σ = 15 nm; d d τ / p 1/6( fully plastic deformation) i m IMPACT CMP 44

45 Huaming Xu Future Goals Experimental verification of hydrodynamic and probabilistic models of the particle charging process. Analysis of the material removal mechanism and estimation of final surface roughness after lapping. Slip-line plasticity analysis and FEM modeling of single diamond particle plowing through a ceramic surface. Estimation of the gap between the recording head surface and the lapping plate during lapping. Lapping mechanics studied by nanoscratching experiments with diamond tips of different size/shape and contact loads. Friction coefficient measurements and lubricant effect on material removal rate and metal transfer/smearing. Lubricated lapping experiments under various loads and speeds using glycol and other hydrocarbon-based lubricants. IMPACT CMP 45