Mass Spectrometry for Equipment, Process and Wafer State Sensing and Control

Transcription

1 Mass Spectrometry for Equipment, Process and Wafer State Sensing and Control Laura L. Tedder, G. Brian Lu and Gary W. Rubloff NSF Engineering Research Center for Advanced Electronic Materials Processing North Carolina State University Raleigh, NC fault detection & classification metrology dynamic simulation control Real-time mass spectrometry results equipment & process state fault detection & classification wafer state o thickness metrology o dynamic reaction rate Flexible manufacturing example Integration of simulation & MS sensing for process extrapolation

2 Why mass spectrometry as a real-time sensor? Already in manufacturing RGA for contamination control Generic applications virtually any process except lithography In-situ, real-time, non-invasive / non-destructive Sensitive to equipment, process and wafer state chemical & physical changes

3 Relative Abundance 2x x x x % SiH 4 /Ar, 1 slm RTP Pressure: 5 torr QMS Pressure: 1E-6 torr H 2 SiH 4 Cracking Ar 2 Fragments SiH 2 Ar Active Sampling Mass Spectrometry for PolySi RTCVD % SiH 4 /Ar Atomic Mass Unit 1 mm sampling aperture Lamps 5x10-6 torr 50 mtorr 5 torr QMS Leybold Inficon Transpector 50 l/s 50 l/s Mech. Pump Mass Spec Sensor System RTCVD tool Downstream sensing Two-stage differential pumping Rapid sensor time response (3 sec) Tedder, et al., JVST-B, 13 (4), (1995) 1924

4 Analysis of Mass Spectrometry Sensor Data 2x % SiH 4 /Ar, 1 slm Problem: Ionization of chemical species produces multiple peaks fragmentation multi-ionization Relative Abundance 2x x x10-11 RTP Pressure: 5 torr QMS Pressure: 1E-6 torr H 2 Ar 2 SiH 4 Cracking Fragments SiH 2 Ar Surface reaction products can be same species as fragments Atomic Mass Unit Solution: Identify peaks unique to chemical species e.g., AMU 30 => SiH 2 & NO AMU 31 => SiH 3 only

5 Mass Spec Monitoring of RTCVD Processes SiH 4(g) Si (s) 2 H 2(g) Gas On Lamps On Gas Off Lamps Off SiH o C H 2 Partial Pressure (arb. units) o C o C Equipment & process state sensing equipment function & control process gases Wafer state sensing H 2 production & reactant depletion metrology?

6 Equipment Fault Pressure Control System 4.0x x10-10 normal Ar trend 3.0x10-10 Relative Abundance 2.5x x x x x x x10-10 abnormal Ar trend 3.0x10-10 Relative Abundance 2.5x x x x x Capacitance manometer-based control Mass spec sensitivity to pressure oscillations

7 Equipment Fault Temperature Contol System H 2 Partial Pressure (Arb. Units) 1.2x x x x x10-8 temperature control system re-calibration: before after 5 torr 10% SiH 4 /Ar 300 sccm 650 o C, 35s temperature overshoot ~50 o C 2.0x Pyrometry-based process control Mass spec sensitivity to reaction rate

8 Time-Integrated Sensor Data for Metrology Reaction product signal (H 2 ) indicates reaction rate at wafer SiH 4 ==> Si 2 H 2 Every two H 2 product molecules sensed represents a Si atom deposited Integrate H 2 product signal through cycle to determine deposited Si thickness Signal should be fault-tolerant, i.e., insensitive to details of process recipe and control system response Use as a real-time metrology tool

9 Fault-tolerant thickness metrology Temp time torr 10% SiH 4 /Ar (300 sccm) polysi RTCVD on o 1000A thermal oxide N2 N3 N1 N4 N Temperature (oc) Nanometrics th ickness measurements => ex-situ

10 Wafer State (Thickness) Metrology SiH 4(g) Si (s) 2 H 2(g) polysi RTCVD Integrated H 2 Mass Spec Signal (Reaction Product) 8.0x x x x Reaction Product Measured In Situ Correlates With Film Thickness d = c ( y H 2 ) PolySi Film Thickness (A) (Nanometrics) o 300 sccm 10% SiH 4 /Ar Linear over crucial thickness range (< 2000A) to 10% Candidate for RTCVD metrology Non-linearities instrinsic and need to be understood Tedder, et al., JVST-B, submitted

11 Mass Spec Sensing of SiO 2 RTCVD 2% SiH 4 / N 2 O 1.0x10-10 Gas Lamps Gas Off On On Lamps Off 8.0x10-11 Relative Abundance 6.0x x x10-11 N 2 O Ar H 2 1.6x x Electron Multiplier sccm (10%SiH 4 /Ar) 300 sccm N 2 O 800 o C, C, sec Relative Abundance 1.2x x x x10-12 N 2 Ar 2 H 2 4.0x x H 2 observed as reaction product from SiH 4 reaction H 2 /H 2 O > 10 3 (=> SiH 4 rxn followed by N 2 O oxidation)

12 Flexible Manufacturing Example Goal: "Customer" needed to extrapolate 1200A RTCVD polysi process to A process not a simple matter of reducing time or temperature Simulation: Used simulator to determine process parameters for 250A polysi RTCVD, varying time and pressure Experimental validation: Immediate detection of process control limitation pressure control system calibrated for > 5 torr Sensor data raises concerns for reproducibility and statistical distribution of end product

13 Process Extrapolation 4.0x torr 10% SiH 4 /Ar 650 o C 30s A o 3.5x x10-10 spurious event pressure control system oscillation H 2 Ar Ion Gauge On Relative Abundance 2.5x x x x x torr 10% SiH 4 /Ar 640 o C 15s 500 A 4.0x10-10 H o 3.5x Ar 3.0x10-10 Mass Spec Signal 2.5x x x x x x

14 Metrology Validation & Process Extrapolation normal process: 650 o C, 5 torr, 35 sec, 1200A polysi experimental constraints: ~650 o C, ~500A equipment constraints: 300 sccm, 5 torr & >15 sec simulation result: 640 o C, 15 sec, 470A average mass spec estimate: 444A average polysi thickness (SIMS): 482A 1000 Estimated Thickness (A) In-Situ Mass Spec Estimated polysi Thickness (A, Mass Spec) polysi RTCVD runs target thickness: ~500A 640 o C, 15 sec 300 sccm 10% SiH 4 /Ar polysi Thickness (A, SIMS) Actual Thickness (A) Off-Line SIMS

15 Mass Spec Sensing Viable & valuable for equipment faults gas flow, pumps,... Useful for some process faults SiH 4 polysi => SUCCESS other process chemistries => to be determined Potential application to real-time wafer state metrology Contributes to process mechanism knowledge base many chemistries not well understood knowledge capture in physically-based simulation

16 General Conclusions Real-time mass spectrometry o o Fault detection & classification equipment functionality complex process dynamics metrology Understanding of chemistry & physics Dynamic simulation for process analysis Tool to optimize manufacturing and environment o Platform for sensor interpretation and control o Physics- and chemistry-based Integration of in-situ diagnostics & dynamic simulation Process & equipment development o o Flexible manufacturing Environmental optimization

17 Acknowledgments National Science Foundation Semiconductor Research Corporation Leybold Inficon technical assistance & β-site interaction Visual Solutions, Inc. VisSim consultations John B. Flanigan, III technical assistance John R. Hauser, Prof. Brian F. Conaghan data management Patrick Bednarz, grad student Nanometrics measurements Gregory N. Parsons, Assoc. Prof. discussion