In Situ and Real-Time Metrology during Cleaning, Rinsing, and Drying of Microand Nano-Structures

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1 In Situ and Real-Time Metrology during Cleaning, Rinsing, and Drying of Microand Nano-Structures Jun Yan *, Davoud Zamani *, Bert Vermeire +, Farhang Shadman * * Chemical Engineering, University of Arizona + Electrical Engineering, Arizona State University 1

2 Outline Background Objective and Approach Electro-Chemical Residue Sensor (ECRS) Applications to the batch tools Applications to the single wafer tools 2

3 Semiconductor Manufacturing Technology Nodes 10 µm R. Chau et. al., Intel Corp., 61st DRC, June µm 90 nm 32 nm Can we use current surface preparation technologies for future nano features? Data from 3

4 Fast Increase of Water Usage 50 % of which is UPW consumption Can we decrease water usage per unit wafer area? 4

5 Typical Wet Process The rinse process consumes the 80-85% of the ultra pure water. Over rinse is common practice. 5

6 Metrology Requirement Convection Desorption Adsorption Diffusion Water Chemical Wafer Desorption Convection Convection Desorption Convection/ Diffusion Fundamentals of rinsing patterned wafers are poorly understood Key to efficient rinse is on-line metrology; technology not available presently 6

7 Resistivity Measurement 7

8 Surface Analysis (TXRF and SIMS) Pros: High sensitivity ( atoms/cm 2 ) Low operation cost Cons : High equipment cost Time consuming Low portability Not sensitive for low atomic number elements (TXRF) Flat surface required(txrf) Sensitivity decrease with probe diameter (SIMS) 8

9 Objective and Approach Investigate the fundamentals of the cleaning, rinsing, and drying of micro- and nano-structures Develop a novel metrology method for in-situ and realtime monitoring of the dynamics of impurity transport inside micro- and nano-structures. Apply the metrology method together with process modeling to discover operational strategies for lowering resource usage in the cleaning, rinsing, and drying of small structures. 9

10 Novel Hardware: Electro-Chemical Residue Sensor (ECRS) Key Features Real Time In-situ Online Hardware (ECRS) High Sensitivity Non-destructive Quick Response 10

11 Clean Chemistry Dependence of Rinse Sensor Output (% full scale) HCl Sulfate concentration (mol/m3) H 2 SO C (mol/m3) 2.95x x x x10-4 C (ppb) Time (min) Sensor shows different rinse dynamics for different chemicals 11

12 Software: Comprehensive Simulation of Poisson equation: Rinse Process Multi-component species transport equations : C i t C S 2 t = (D i C i + where charge density: Ohm s law: z Fµ C i i i Change in tank concentration : V C b t = Q(C C b ) + Surface adsorption and desorption: in 2 φ = A Flux ρ ε φ) = k a2 C 2 (S 02 C S 2 ) k C d 2 S 2 J =σ E ρ = F z i i C i E = 0 Convection Desorption Surface Charge Diffusion Surface reaction Ionic transport Convection/ Diffusion where electrical conductivity: σ = λ C i i i 12

13 Comparison of Experimental Data with Model Prediction: H 2 SO 4 Residual Impurity Measured as Magnitude of Impedance (kω) T (rinse water) = 49 ºC Experiment Simulation Time (sec) Residual Impurity Measured as Magnitude of Impedance (kω) x 10-3 M H 2 SO T (rinse water) = 32 ºC Experiment Simulation 2 x 10-3 M H 2 SO Time (sec) Accurate modeling of the surface cleanup during rinse is possible 13

14 Surface Concentration Profile Impurity Conc. in tank (mole/l) 3.E-05 2.E-05 1.E-05 0.E+00 Conventional technology Resistivity probe T (rinse water) = 32 ºC 1.15 x 10-3 M NH 4 OH Time (sec) Surface Conc. (1e 11 mole/cm 2 ) Novel technology ECRS T (rinse water) = 32 ºC 1.15 x 10-3 M NH 4 OH Time (sec) Conventional technology might provide misleading information ECRS provides in situ and real-time contamination profile 14

15 ECRS Application in Over-Flow and Quick- Dump Rinse Tools Joint work with Freescale and EMC* on development of new low-water rinse processes using ECRS and process modeling. Co-Investigators and Liaisons: Hsi-An Kwong, Marie Burnham, Tom Roche, Amy Belger, Stuart Searing, Georges Robert, and Andrew Hebda * EMC is a Engineering Research Center spin-off company that is formed for tech transfer and commercialization of ECRS technology 15

16 Sample Results: Optimization of Rinse Recipe in OFR/QDR Combination Initial overflow Multiple dumps ; overflows in-between Overflow Stage 1 Stage 2 Stage 3 Process Parameters Flow rates at different stages Time for every session of overflow rinse Number of quick dumps Water temperature at every session Collaboration with Freescale to optimize existing rinse recipes is ongoing. 16

17 Effect of the Number of Dumps on the 100 Post-APM Rinsing Initial overflow for 5 sec; overflow in between for 5 sec; flow rates are high flow at all stages; 32 ºC 80 Impedance (kω) Impedance (kohm) dumps 3D_5_0H 2 dumps 2D_5_0H 1D_5_0H 1 dump Time(sec) (sec) 17

18 18

19 ECRS Application in Single-Wafer Spin Rinse Joint work with Samsung and EMC* on analysis of the dynamic of spin rinse and development of new low-water rinse processes. Samsung Co-Investigators and Liaisons: Jeongnam Han, Seung-Ki Chae, Pil-Kwon Jun * EMC is a Engineering Research Center spin-off company that is formed for tech transfer and commercialization of ECRS technology 19

Application of ECRS to Single Wafer Rinsing and Drying RPM UPW UPW 300 mm wafer Experimental Setup Process Model Schematic A single wafer tool

20 Application of ECRS to Single Wafer Rinsing and Drying RPM UPW UPW 300 mm wafer Experimental Setup Process Model Schematic A single wafer tool equipped with ECRS is designed and set up. Combination of experiments and process model is used to study the effect of various process parameters. 20

21 Monitoring Capabilities of ECRS Stage-1: Chemical Exposure Stage-2: Rinsing -Purging of the Trench Stage 3: Rinsing: Surface Reaction Stage-4: Bulk Drying Stage-5: Surface Drying 21

Convection Desorption Convection Boundary #3 2: Boundary layer Boundary #1 Boundary #10

22 Process Model for Spin Rinsing Diffusion Fluid Dynamics Surface Reaction Electrostatic Field Electrokinetic Flow z Convection Wafer r Desorption Flow Boudary#2 Diffusion/ Convection Desorption Convection Boundary #3 2: Boundary layer Boundary #1 Boundary #10 Boundary #4 1: Trench Boundary #9 Boundary #5 H Boundary #8 Boundary #7 Boundary #6 Electrode SiO 2 22

23 Bulk s s Fluid Dynamics From the conditions that = << 1 z r h R Q: volumetric flow rate, m 3 /s υ: kinematic viscosity, m 2 /s r: radius of the wafer, m ω: angular velocity, rad/s u r Q. ν h = r ω ρω 2 rh 2 (1 (1 = 2µ 0.33 z h ) 2 ) I. Lesgev, G. Peev, Film flow on horizontal rotating disk, Chemical Engineering and Processing, vol. 42, 2003, pp

24 Surface Reaction Surface adsorption and desorption dc dt Cs: surface concentration K a : adsorption constant K d : desorption constant S = k ac 2(S SO 0 4 S 0 : maximum number of sites available for adsorption C S ) k d C S 24

25 Electrostatic Filed Dissociation of silanol group KSiOH SiOH SiO + H + Poisson s Equation 2 φ = = F i z ρ ε ρ where charge density: i C i F: Faraday constant, C/mol C i : ions concentration z i : ions valence 25

26 Electrokinetic Flow and Ohm s s Law Multi-component species transport equations C t i = (D i C i + z i F µ i C i φ) u C i Flow Boudary#2 Ohm s law: Boundary #1 2: Boundary layer Boundary #3 H J =σ E E = 0 Boundary #10 Boundary #9 1: Trench Boundary #4 Boundary #5 where electrical conductivity: Boundary #8 Boundary #7 Boundary #6 Electrode σ = i λ C i i SiO 2 26

27 Validation of Process Simulation Magnitude of Impedance (kω) Time (sec) 800 RPM, 20 GPH, N 2 Purging 40:1 Dilute Sulphuric Experimental Data Model Prediction Simulation results are in good agreement with the experimental data 27

28 Effect of Process Parameters Effect of Speed of Rotation Effect of Flow Rate Magnitude of Impedance (kω) GPH, N 2 Purging 40:1 Dilute Sulphuric 150 RPM 800 RPM 400 RPM 1200 RPM 1600 RPM Time (sec) 350 Magnitude of Impedance (kω) RPM, N 2 Purging 40:1 Dilute Sulphuric 6 GPH 16 GPH 10 GPH 20 GPH Time (sec) Gain in rinse efficiency diminishes as we reach an optimum speed of rotation and rinse-water flow rate. Speed of rotation and water flow rate can be optimized to reduce rinse time and increase throughput. 28

29 Effect of Process Parameters Magnitude of Impedance (kω) Effect of Rinse Water Temperature 800 RPM, N 2 purging 40:1 Dilute Sulfuric 32 0 C 43 0 C 52 0 C Time (sec) 350 Temperature has significant effect on enhancing both the early stage and the final stage of the rinse process. 29

30 Surface Concentration (ions/cm 2 ) 1.E13 1.E12 1.E11 1.E10 Benefits of Staged Rinsing Target Cleanliness RPM = 250 Hydrophilic Surface Flow Rate = 2.5 lit/min Time (sec) Post SC-1 1 Rinsing 50 0 C 32 0 C Staging temperature of UPW decreases rinse time without sacrificing cleanliness To reach 1.E12 ions/cm 2, staged rinsing leads to water savings of 40% for staged rinse 1 and 50% for staged rinse Flow Rate (lit/min) Flow Rate (lit/min) Flow Rate (lit/min) Warm Cold Cold Cold 120 Time (sec) Time (sec) Warm Time (sec)

31 Effect of Feature Size Rinse Time (sec) Rinse time to reach ions/cm 2 on the surface of the trench. 800 RPM, 20 GPH Mild Adsorption Strong Adsorption Very Strong Adsorption Trench Width (nm) 31

32 Effect of Wafer Size Rinse time to reach ions/cm 2 on the surface of the trench. 80 Positive ion, strong adsorption Rinse Time (sec) Trench width=100 nm Trench width=50 nm Wafer Size (mm) 32

33 Sensor Responses During Drying Z Z Drying Drying Drying 33

34 Indication of Drying Responses 34

35 Effect of Rinse Water Temperature on Magnitude of Impedance (kω) Drying 800 RPM, 20 GPH, N 2 Purging 40:1 Dilute Sulphuric Time (sec) 32 0 C 43 0 C 52 0 C Higher rinse water temperature leads to higher wafer temperature and therefore, faster drying. 35

36 Development of Low-Impact Cleaning Determining the Bottlenecks Using ECRS Etching creates residues on the sidewalls of the trenches and vias. Side wall cleaning becomes more complex, more difficult, and more resource intensive as we move further into manufacturing of highaspect-ratio nano-features No in-situ sensor is available for on-line monitoring of residues; ECRS would result in significant reduction in use of cleaning chemicals. Solvent Dissolved contaminants diffuse out Polymer layers Polymer dissolves in cleaning liquid Electrodes 36

37 Post-etch Cleaning of Micro- and Nano-Structures Magnitude of Impedance (kω) IPA followed by acetone Acetone followed by IPA Time (sec) High sensitivity and a detection of the end point 37

38 Cleaning of Micro- and Nano-Structures Magnitude of Impedance (kω) Hot DI water Rinse In Samsung Cleaning Solution Hot DI water Rinse Time (sec) 1200 ECRS can monitor cleaning of the polymer residue inside trench 38

39 Summary ECRS hardware was developed and fabricated. Developed comprehensive process simulator and verified it for both batch overflow rinsing and single-wafer spin rinsing; the simulator is applicable to rinsing and cleaning of fine structures on patterned wafers. Developed a method for integrating ECRS with single-wafer tools. Applied ECRS to determine the effect of key process parameters such as flow rate, rinse water temperature, and speed of rotation. Rinse and dry recipes can be optimized by applying the real-time and in-situ metrology using ECRS and the associated process simulator. The impact of feature size and wafer size was studied by ECRS. 39

40 Industrial Interactions and Future Plans Continue work on application and tech transfer: Joint work, co-sponsored by Freescale (Hsi-An Kwong, Marie Burnham, Tom Roche, Andrew Hebda); rinse process Joint work, co-sponsored by Samsung (Seung-Ki Chae, Jeong-Nam Han, Pil-Kwon Jun); cleaning and rinse processes EMC (Doug Goodman); commercialization Fundamental work on a novel wireless metrology version of ECRS Acknowledge to all the partners and co-workers 40

Kurita Water Industries Ltd. Takeo Fukui, Koji Nakata

Study of particle attachment on silicon wafers during rinsing Kurita Water Industries Ltd. Takeo Fukui, Koji Nakata Outline 1. Introduction 2. Experimental Method 1 3. Result & Discussion 1 4. Experimental