Challenges for Real-Time Control in Reactive Semiconductor Manufacturing Process Environments

Transcription

1 Challenges for Real-Time Control in Reactive Semiconductor Manufacturing Process Environments G. W. Rubloff Institute for Systems Research and Department of Materials and Nuclear Engineering 11/20/00 1

2 Acknowledgements CURRENT COWORKERS John Kidder Yiheng Xu Nayanee Gupta Rock Shi Brian Conaghan Ramaswamy Sreenivasan Evanghelos Zafiriou Theodosia Gougousi Laurent Henn-Lecordier Charles Tilford Anne Rose SUPPORT National Science Foundation Semiconductor Research Corporation Texas Instruments NIST Institute for Systems Research, UMCP Leybold Inficon Ulvac Corporation Visual Solutions, Inc. postdoc/faculty, MNE, UMCP grad student, MNE, UMCP grad student, MNE, UMCP grad student, EE, UMCP grad student, MNE, UMCP grad student, ChE, UMCP faculty, ChE, UMCP postdoc, UMCP staff, UMCP NIST staff, HCIL, UMCP PRIOR COWORKERS Laura Tedder, G. Brian Lu postdocs, NCSU Monalisa Bora, Josh Wolf,Nabil Rabbani graduate students, NCSU Brian Conaghan, David Eckard undergraduates, NCSU Greg Parsons, Yates Sorrell faculty, NCSU MENTORING Stephanie W. Butler, Texas Instruments G. Brian Lu, Novellus Systems Ed Rietman, Lucent Technologies 11/20/00 2

3 Manufacturing Process Control Decision capability = value delivered Real-time regulatory control Real-time sensor signals Real-Time Experiment Real-time comparison Expected sensor signals Everything fine Course correction Process adjustment required, beneficial Fault management Shutdown or repair indicated COURSE CORRECTION SHUTDOWN & REPAIR NO ACTION - preventive maintenance - emergency stop Process Control Response 11/20/00 3

4 APC Analysis Goal Process Control Targets Integrated Sensors Required Control Methods Physical Insights and Modeling Needed Course Correction Enhanced yield through drift compensation Single wafer state sensor Robust run-to-run and real-time process control Minimal Fault Management Enhanced tool OEE Multiple process state & equipment state sensors Wafer state sensors useful but optional Selected fault detection, classification, and response Obvious -- OR -- Physically-based simulators and/or extensive diagnosed fault database 11/20/00 4

5 Downstream Chemical Sensing for Wafer State Metrology Real-time sensing of product generation and reactant depletion Chemical sensors: mass spectrometry, acoustic, optical, Deposition and etching Thickness, etch depth, rates Support by dynamic system simulation Sensor-IN-tool models Basis for run-to-run/real-time control and fault management Importance of system identification Sensor information in complex, dynamic process cycles 11/20/00 5

6 Real-Time Mass Spectrometry for Thickness Metrology in RTCVD polysi from SiH 4 Real-time mass spectrometry through process cycle SiH 4(g) Si (s) 2 H 2(g) 300 sccm 10% SiH 4 /Ar 10-3 Gas On Lamps On Gas Off Lamps Off Integrated H 2 product signal used for real-time, in-line thickness metrology 10-4 Partial Pressure (arb. units) o C SiH 4 reactant depletion 750 o C SiH 2 H 2 H 2 reaction product Integrated H 2 Mass Spec Signal (Reaction Product) 8.0x x x10-9 Reaction Product Measured In Situ Correlates With Film Thickness D s(h 2 )dt 2.0x PolySi FilmThickness (A) (Nanometrics) 11/20/00 6

7 Wafer State (Thickness) Metrology for SiO 2 RTCVD from SiH 4 / N 2 O H 2 reaction product observed from SiH 4 decomposition ==> dominant process chemistry SiH 4 2 N 2 O ==> SiO 2 (s) 2 N 2 (g) 2 H 2 (g) Mass spec signal linear in SiO 2 thickness ==> useful product sensing Across-wafer nonuniformity (>polysi) influences measurement corrected by nonuniformity model Integrated H 2 Signal (amp-sec) 5.0x x x x x10-10 Bicomponent CVD reaction sensing ==> specific thickness metrology (from Si component of SiO 2 unit) ==> fault detection, classification, control SiO 2 Film Thickness (A) 11/20/00 7

8 3.5x x10-11 Mass Spec Sensing of SiO 2 Etching in ECR CF 4 Plasma 18 nm SiO 2 CF CF 4 4 plasma etch etch of of SiO SiO 2 2 /Si /Si 2.5x10-11 Partial Pressure 2.0x x10-11 CO SiF 3 1.0x x Time (sec) End point corresponds to etch rate determined for 18 nm SiO 2 film 11/20/00 8

9 Real-Time Sensors: Status and Limitations Sensors rich with chemical process and wafer state information exist Mass spectrometry, optical absorption and emission, acoustics, rf, Real-time chemical state information is primarily zero-dimensional Scalar vs. time at best Complex positional and temporal averaging Wafer end-pointing useful OES, interferometry, mass spectrometry, 2-D and patterned wafer state sensing is limited Full-wafer interferometry - mainly applied to plasma etching Process sensing by optical tomographies (OES, LIF,...) Access in reactor an issue Uniformity and vertical profile sensing and control difficult Sensor and reactor system chemistry complicates chemical sensor usage Sensor drift and reliability Wall reactions in reactor and downstream 11/20/00 9

10 Dynamic System Simulation Real-time sensor observations ==> system dynamics Equipment and process state Reaction model Sensor and control system dynamics Understanding and exploitation of dynamic sensor signals Sensor-IN-tool models Support for process control algorithms Model fidelity influences control efficacy Sensor signals and dynamic simulation improve model fidelity 11/20/00 10

11 Dynamic RTCVD PolySi Simulator Dynamic simulation can realistically represent complex systems, including equipment process sensors control Results validated against experiment timing/dynamics subtle systematics Numerous applications systems analysis optimization sensor-in-tool models control system design training ==> learning Platforms commercially available (Windows) Exploit rapidly growing software base Process Recipe Equipment Simulator Process Simulator Manufacturing FoM Simulator Sensors and Sensors and Control System Control System Gas Flow Vacuum chambers Mass flow controllers Pumps, valves Conductances, volumes Partial and total presssures Pressure control system Viscous/fluid flow partial pressures Sensors Total and partial pressures Temperatures Valve and MFC status Controls PID controlers for temperature and pressure Lamp power output control Throttle valve positions Manufacturing Process Efficiency Cycle time Consumables volume Energy consumption Valves, MFC s vs. time, status Lamp power vs. time Overall process timing, conditions Heat Flow Wafer absorptivity, emissivity Wafer thermal mass Wafer radiation, conduction Wafer temperature Temperature control system Process-dependent absorptivity, emissivity Convective heat loss in fluid flow temperatures CVD Reaction Gas phase transport Boundary layer transport Surface-condition-dependent reaction rates - surface kinetics rates Wafer State Deposition rate Film thickness Thickness control system Product properties - uniformity, conformality, material quality, topography, reliability Environmental Assessment Gaseous emissions Reactant utilization Power consumption Solid waste 11/20/00 11

12 Dynamic RTCVD PolySi Simulator Visual Solutions, Inc. Film Film Thickness (A) (A) QMS Partial Pressures Ar, Ar, SiH SiH 4, 4, H 2 2 wafer T (oc) (oc) growth rate rate (A/sx10) Multi-level Structure Second Level Compound Block 11/20/00 12

13 Wafer State Thickness Metrology Experiment and Dynamic Simulation SiH 4(g) Si (s) 2 H 2(g) Integrated H 2 product signal used for real-time, in-line thickness metrology 300 sccm 10% SiH 4 /Ar Dynamic simulation vs. Experiment Integrated H 2 Mass Spec Signal (Reaction Product) 8.0x x x x10-9 Reaction Product Measured In Situ Correlates With Film Thickness D s(h 2 )dt PolySi FilmThickness (A) (Nanometrics) PolySi Thickness (A) - Simulated PolySi Thickness (A) - Actual 11/20/00 13

14 Flow Rate Dependence of Mass Spec Sensor Signal Mass spec sensitive to reactor flow rate at constant pressure Dynamic simulator captures flow rate dependence Sensor is influenced by process dynamics Consequences: minor if fixed process recipe tractable for varying recipes with simulator available System dynamics introduces complexity in sensor response H2 QMS Signal (amp.) 2.5x x x10 1.0x x10-11 Mass spec H 2 signal during polysi RTCVD at 750 o C, 5.0 torr SiH 4 /Ar for 40 sec o o o 500 sccm 1000 sccm Experimental Simulation 200 sccm Sensor-in in-tool model not just a sensor model TIME (sec.) 11/20/00 14

15 Course Correction Real-time sensors to drive process control Run-to-run and real-time control Feedback, feedforward Regulatory control systems already in broad use Control algorithms require models Empirical to sophisticated Controller I/O s Control to single metric or utility function o Rate, thickness, uniformity, vertical profile, material properties, Control by single (or multiple) control variables o Pressure, flow, power, temperature, System drifts (and noise) can be substantial in chemical processes Sensor drifts and wall reactions Premium on robust control algorithms o Less sensitive to sensor noise and model error 11/20/00 15

16 Run-to-Run Process Drift Process drift moves metrology targets gradually off-center Centering drift has strong adverse consequences for yield Often, drift results from systematic drift mechanism in equipment/process ==> compensation possible 2.0x10-10 Relative Abundance 1.5x x x % SiH 4 /Ar, 5 torr, 300 sccm 650 o C, 30 sec process cycles H 2 Ar/ Ar 2 Utilize run-to-run feedback control to retune process so that subsequent wafers show correct metrology targets Substantial benefit in run-to-run control Corresponding benefits in realtime feedback control More difficult implementation Integrated H 2 mass spec (product) signal x x x x x x Time (sec) 0 systematic drift ("aging") statistical variation PolySi RTCVD Wafer Number Actual Thickness 11/20/00 16

17 Models for Use in Control Algorithms Response surface models (RSM s) Empirical or theoretical/analytical Structured design-of-experiments using experiment or simulation Neural network models Dynamic models Appropriate or required where process dynamics important (cf. steady-state description) Numerous variations Linear, nonlinear, exponentiallyweighted moving average (EWMA) Models are imperfect, especially in an environment of process adjustments and tuning as necessitated by Process integration and yield optimization Technology evolution Equipment evolution Data noise Robust control algorithms which add value even in the presence of Model error Data noise 11/20/00 17

18 Fault Management Fault detection, classification, prognosis, and response major benefits in overall equipment effectiveness Early benefit in faults with obvious origin E.g., oxygen (or air) impurity in process ambient Strong industry trend Multiple sensor signals required (in most cases) Confirm genuine fault and determine origin In all but the trivial cases, classification and prognosis are a significant challenge 11/20/00 18

19 Equipment Fault Temperature Control System 1.2x10-7 Pyrometry-based process control Mass spec sensitivity to reaction rate H 2 Partial Pressure (Arb. Units) 1.0x 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 Time (sec) 11/20/00 19

20 The Big Questions in Fault Management Does something appear wrong? Detect a possible fault from a sensor signal (equipment, process, or wafer state) Is something wrong? Use multi-sensor integration to verify a system fault I.e., confirm that the sensor isn t broken Identify the system parameter which the sensors indicate is wrong Detection Classification What is wrong? Determine what the system problem is by inference from sensor indicators I.e., the cause of the problem What is going to go wrong, and when? Predict further failure events, timing, and consequences Prognosis What should I do about it, and when? Generate action plan optimized for factory operations and product performance Response 11/20/00 20

21 Multi-Sensor Integration and Control Integration of multiple sensor signals LabView/PC integration Crucial for fault classification Decision support W CVD Reactor Central wafer handler W CVD Reactor Load lock Ulvac ERA-1000 W CVD cluster tool Process and wafer state: mass spec chemical sensing Brooks controller (future) Ulvac controller mass spec Pump system x j (t) PC LabView Equipment state: valve status, pressures, flows, temperatures,... 11/20/00 21

22 Approaches to Fault Classification Statistical correlation Mathematical engines (e.g., Triant Modelware n-th nearest neighbor) Identify patterns in sensor data Associate them with normal behavior or fault events Attribute fault patterns to physical/causal origins Continuously enhance fault pattern database Physical identification Identify physical origin of important faults (Pareto analysis) Determine sensor signatures useful for fault detection and classification Implement fault-specific detection/trapping algorithms Mixtures of statistical and physical approaches 11/20/00 22

23 Intentionally Introduced Faults MFC shutdown sticky MFC Ar Experimental mass spec signals provide distinctive patterns for different fault scenarios H 2 SiH x N Time (sec) pressure controller failure -10 Ar H 2 SiH x N Time (sec) wrong MFC set point Ar -10 Ar -10 N 2 SiH x H 2-11 SiH x H 2 N Time (sec) Time (sec) 11/20/00 23

24 Induced Fault: sticky MFC Consequences: Thickness variation Equipment malfunction detected Add tl sensors required for fault classification Experiment Experiment Relative Abundance 2.5x x x x x Avg Thickness 951A Ar close MFC slowly SiH x open MFC suddenly H 2 process ended N 2 Simulation Simulation Time (sec) close MFC slowly Relative Abundance 1.0x x x x x10-7 Ar open MFC suddenly SiH x Thickness ~1000A H 2 (x5) Time (sec) 11/20/00 24

25 Education and Training for APC graphical user interface (GUI) for simulator time-base strip chart recorder simulation parameters pop-up control bar guidance window display options pop-up 11/20/00 25

26 Integrated H2 Signal 3.00E E E E E E-010 0E000 Fault detection (early H2 signal) , 30 Ar APC Elements in RTCVD PolySi Thickness Control TEMP ( o C), TIME (sec) 625, , , , , , , 5 675, , 5 625, , , , , , , , , , , PolySi Thickness (A) - Actual 1.5x x x10-11 Run-to-run course correction (signals look fine) 2.0x , 25 Ar SiH x (x5) H 2 (x4) Time (sec) Sensor-in-tool model (recipe change to to low flow rate) , 15 Ar H 2 (x4) -10 SiH x (x5) H 2 (x4) SiH x (x5) Time (sec) Time (sec) 11/20/00 26

27 Manual (advisory) control Architecture for Advanced Process Control Automatic (committed) control Equipment Controller TOOL Equipment State Sensors Course Correction Wafer State Sensors Process State Sensors Sensor Signals End Point Determination Run-to-run Control Real-time Control Fault Management Emergency Shutdown Early Preventive Maintenance Sensor Signal Preprocessors Signal Integration / Database Signal Processing Fault Validation Experiments and Simulations Physically-Based Models and Simulators Experimental Sensor Signals: Metrology Indicators NormalProcess Signatures Diagnosed Fault History Fault Analysis Fault Classification Fault Prognosis Known-Fault Signatures and Metrology Database Reliability Database System Knowledge Base 11/20/00 27

28 Conclusions Mass spectrometry and other chemical sensors provide significant capability toward process metrology and fault detection prime issues: sensor stability, uniformity measurement Advanced process control means both course correction to maintain process target centering and yield fault management to optimize equipment utilization and manufacturing cost Achieving the promise of advanced process control requires other important elements as well, including multi-sensor integration (equipment, process, and wafer state) dynamic simulation, including sensor-in-tool models robust course correction algorithms fault classification and prognosis strategies 11/20/00 28

29 11/20/00 29

30 Building a Reference Database for Fault Detection and Classification Actual equipment fault experiences provide valuable but limited database Intentionally induced faults could significantly enhance the value of a fault database Multiple fault scenarios are known to be important Experiments can be constructed to introduce or mimic those faults Physically-based simulations of faults could provide insight and additional database elements 11/20/00 30

31 Steps: 1). Thickness was measured at 0, 1, 2, 3, 4cm from the center. 2). Thickness at the edge (5 cm) was extrapolated form the first five points. 3). The average thickness in each ring is the average between the thickness at the ID and that at the OD. e 4). The volume of the film in each ring is the product of the average thickness and d the surface area of the ring. 5). The average thickness across the whole wafer equals the sum of the film volumes in each ring divided by the total surface area. Contribution of each ring to the total surface area: Ring a: 4% Ring b: 12% Ring c: 20% Ring d: 28% Ring e: 36% Calculation of Average Film Thickness Average film thickness across the wafer: d = 4d a 0.12d b 0.20d c 0.28d d 0.36d e Validate mass spec thickness metrology under nonuniform deposition process conditions c b a 4 Wafer cm 11/20/00 31

32 The potential of mass spectrometry and other chemical sensing approaches to determine dynamic chemical constituency through a reactive semiconductor process cycle holds substantial promise for process control and attendant manufacturing benefits. Applications to mainline VLSI processes have already demonstrated value in supplying metrology information (rates, deposition thickness, etch endpointing) and in revealing process mechanisms and equipment subtleties, information which significantly complements the existing raison-d'etre for RGA's in manufacturing, namely, equipment qualification and debugging. However, the benefits of advanced process control will require other elements. Use of sensor signals for metrology depends on having good sensor models, which in turn often means good models for the sensor IN the tool. Real-time or run-to-run control necessitates either good models or robust algorithms which are less sensitive to model or data error. Fault management - i.e., fault classification and prognosis as well as detection - requires multi-sensor integration including equipment state signals for decision support to minimize down-time, along with dynamic models for interpreting system behavior. This talk will provide examples for mass spec based metrology and process insight, along with an outline of how sensor information might lead to beneficial control and fault management responses. 11/20/00 32

33 Advanced Process Control (APC) Characterisics Sensor-driven Model-based Integrated Key elements Exception detection Fault validation Course correction end point run-to-run real-time Fault management early preventive maintenance emergency stop Fault learning through sensors fault diagnosis process model development Control models & algorithms Real-time Run-to-run Course Correction COURSE CORRECTION Real-time sensor signals Real-time regulatory control Real-Time Experiment Real-time comparison Exception Distinguish system vs. sensor fault Fault Detection Fault database Fault classification Fault prognosis Fault Management NO ACTION Process Control Response Expected sensor signals Experimental history Physics-based dynamic simulators Sources for Analysis Intelligent databases Model-based decision tools Systems Tools SHUTDOWN & REPAIR - preventive maintenance -emergency stop 11/20/00 33

34 1.0x10-11 Mass Spec Sensing of SiO 2 RTCVD 2% SiH 4 / N 2 O Time Temperature 1.0x10 SiH 2N O SiO (s) 2N (g) 2H (g) SiH 2N O SiO (s) 2N (g) 2H (g) x % SiH /N O o C 5 Torr 8.0x Torr SiH /N O mixture sec process time QMS H 2 Signal (amp.) 6.0x x sec 30 sec 45 sec 60 sec 75 sec QMS H 2 Signal (amp.) 6.0x x % 800 o C 2% 775 o C 2.0x x % 750 o C 1% 800 o C TIME (sec) TIME (sec) % SiH 4 1.0x10-11 SiH 4 2N 2 O SiO 2 (s) 2N 2 (g) 2H 2 (g) 8.0x Torr SiH /N O mixture sec process time QMS H 2 Signal (amp.) 6.0x x x % 800 o C 1% 800 o C 0.5% 800 o C TIME (sec) 11/20/00 34

35 Mass Spec Sensing of Pulsed-Gas Selective Si Deposition by PECVD Modulated Silane Flow in H 2 Can Result in Selective Si PECVD 10-6 SiH 4 ON SiH 4 OFF Observed Mass Spec Signal Pressure, torr SiH 2 (amu 30) Etching SiH 4 pulsed in H 2 SiH 4 on/off = 20s/50s Dep. Plasma On G. N. Parsons et. al Plasma Off SiH 4 pulsed in H 2 SiH 4 on/off = 20s/50s Time, min 11/20/00 35

36 Mass Spec Sensing of SiO 2 RTCVD 2% SiH 4 / N 2 O H 2 reaction product observed from SiH 4 decomposition ==> dominant process chemistry is 3.0x x % SiH 4 /N 2 O 800 o C 5 Torr 60 sec N 2 (mass 28) SiH 4 2 N 2 O ==> SiO 2 (s) 2 N 2 (g) 2 H 2 (g) [e.g., not SiH 4 4 N 2 O ==> SiO 2 (s) 4 N 2 (g) 2 H 2 O (g)] Experimental improvements in s/n ==> useful reaction product sensing Reaction simplicity ==> metrology from Si component of SiO 2 unit QMS Signal (amp.) 2.0x x10-11 Ar 2 (mass 20) 1.0x x10-12 H 2 (mass 2) SiH (mass 31) TIME (sec) 11/20/00 36

37 Equipment Fault Pressure Control System 4.0x x x10-10 normal Ar trend 5torrprocess Capacitance manometer based pressure feedback Mass spec sensitivity to pressure oscillations Relative Abundance Relative Abundance 2.5x x x x x x x x x x x x Time (sec) abnormal Ar trend 0.5 torr process pressure feed back system fault 5.0x Time (sec) 11/20/00 37

38 Induced Fault: wrong MFC set point Consequences: Experiment No significant variation in thickness Decreased throughput Equipment failure anticipated Relative Abundance 2.0x x x x10-11 Ar SiH x H Time (sec) Simulation Relative Abundance 1.0x x x x x sccm Ar SiH x 250 sccm H 2 (x5) Time (sec) 11/20/00 38

39 Induced Fault: pressure controller failure Experiment Experiment throttle valve position fixed process stop Avg. Thickness 1284A Consequences: Significant thickness increase Throughput substantially decreased Equipment failure detected Relative Abundance 6.0x x x10-10 Ar SiH x H 2 N 2 Simulation Simulation Time (sec) 2.0x10-6 Relative Abundance 1.5x x x10-7 Ar SiH x H Time (sec) 11/20/00 39