Multi-layer/multi-input/multi-output (MLMIMO) models and method for using

• US 7,967,995 B2
• Filed: 03/31/2008
• Issued: 06/28/2011
• Est. Priority Date: 03/31/2008
• Status: Expired due to Fees

• Current Assignee: Tokyo Electron Limited
• Original Assignee: Tokyo Electron Limited
• Inventors: Sundararajan, Radha, Lee, Hyung Joo, Yamashita, Asao, Prager, Daniel, Funk, Merritt
• Primary Examiner(s): OLSEN, ALLAN W
• Application Number:
  US12/059,624
• Publication Number:
  US 20090242513A1
• Time in Patent Office: 1,184 Days
• Field of Search: None
• US Class Current: 216/59
• CPC Class Codes: H01L 21/32137 of silicon-containing layers
• CPC Class Codes: H01L 22/12 for structural parameters, e.g. thickness, line width, refractive index, temperature, warp, bond strength, defects, optical inspection, electrical measurement of structural dimensions, metallurgic measurement of diffusions electrical measurement of diffusions H01L22/14
• CPC Class Codes: H01L 22/20 Sequence of activities consisting of a plurality of measurements, corrections, marking or sorting steps
• CPC Class Codes: H01L 2924/00 Indexing scheme for arrangements or methods for connecting or disconnecting semiconductor or solid-state bodies as covered by H01L24/00
• CPC Class Codes: H01L 2924/0002 Not covered by any one of groups H01L24/00, H01L24/00 and H01L2224/00

Abstract

The invention provides a method of processing a substrate using multilayer processing sequences and Multi-Layer/Multi-Input/Multi-Output (MLMIMO) models and libraries that can include one or more masking layer creation procedures, one or more pre-processing measurement procedures, one or more Partial-Etch (P-E) procedures, one or more Final-Etch (F-E) procedures, and one or more post-processing measurement procedures.

Claims

1. A method for establishing a Multi-Layer/Multi-Input/Multi-Output (MLMIMO) model comprising:

determining a first multi-layer processing sequence to create one or more final poly-gate structures in one or more multi-layer gate structures, wherein the first multi-layer processing sequence includes one or more first measurement procedures, one or more Partial-Etch (PE) procedures, one or more Final-Etch (F-E) procedures, and one or more second measurement procedures, wherein a final poly-gate structure comprises at least one sidewall angle (SWA) and at least one gate-width, the SWA being between approximately eighty-five degrees and approximately ninety-two degrees, and the gate-width being between approximately twenty nanometers and approximately fifty nanometers;

selecting a first Multi-Layer/Multi-Input/Multi-Output (MLMIMO) model configured to simulate the first multi-layer processing sequence, the first MLMIMO including a plurality of Controlled Variables (CVs), a plurality of Manipulated Variables (MVs), and a plurality of Disturbance Variables (DVs);

determining a first set of DVs associated with the first MLMIMO, wherein one or more of the first measurement procedures are configured to provide one or more of the DVs, one or more of the DVs being determined by a preceding procedure or layer;

determining a first set of CVs associated with the first MLMIMO and ranges associated with the CVs, wherein the CVs include one or more critical dimensions (CDs) associated with the final poly-gate structure and one or more SWAs associated with the final poly-gate structures, wherein one or more of the second measurement procedures are configured to provide one or more of the first set of CVs;

establishing a first set of MVs associated with the first MLMIMO using one or more candidate recipes, wherein the first set of MVs includes one or more Within-Wafer Manipulated Variables (WiW-MVs) configured to change while a substrate is being processed, and one or more Wafer-to-Wafer-Manipulated Variables (W2W-MVs) configured to change after the substrate has been processed;

analyzing the MLMIMO model, wherein one or more statistical models are selected, one or more ranges are provided for the CVs and the MVs, and one or more statistical analysis procedures are performed to establish Design of Experiments (DOE) data, wherein the statistical models are configured to associate one or more MVs with one or more CVs;

establishing one or more nonlinear models having quadratic and interaction terms by using a least squares technique and statistical software;

creating one or more steady-state linear gain matrices (G) using the DOE data, wherein each gain matrix includes a first number of MVs and a second number of CVs;

calculating one or more Relative Gain Arrays (RGA) using one or more of the steady-state linear gain matrices (G), wherein RGA=G{circle around (x)}(G<sup>−

1</sup>)^T and {circle around (x)} denotes element-by-element multiplication;

optimizing one or more sets of MVs using one or more pairing rules;

determining one or more stability conditions for the MLMIMO; and

optimizing the MLMIMO using performance parameters associated with a first set of processing tools configured to perform the first multi-layer processing sequence.

2. The method of claim 1, wherein the first measurement procedures comprises:

3. The method of claim 1, wherein the one or more stability conditions are determined using a Niederlinski Stability Theorem wherein:

4. The method of claim 1, wherein a condition of a gain matrix (G) can be determined using:

G=USV^T

wherein G, U, S, and V are matrices determined using singular value decomposition (SVD).

5. The method of claim 1, wherein the first measurement procedures comprises:

obtaining first metrology data for a first evaluation substrate using a first metrology subsystem, the first metrology data including Integrated Metrology (IM) data for a first photoresist feature in a photoresist layer associated with the final poly-gate structures.

6. The method of claim 1, wherein the first metrology data includes thickness data, top CD data, bottom CD data, sidewall angle data, or optical data for one or more first photoresist features in a center region and for one or more second photoresist features in an edge region.

7. The method as claimed in claim 1, further comprising:

transferring a first substrate to a first etching chamber in a first etching subsystem using a transfer subsystem coupled to the first etching subsystem, wherein the transfer subsystem is configured to prevent an oxide layer from forming on the first substrate during the transferring;

establishing a first substrate holder temperature during a first P-E procedure, the first substrate holder temperature being between approximately 40 degrees Celsius and 70 degrees Celsius, and wherein the first etching subsystem comprises a temperature control system configured to control the first substrate holder temperature during the first P-E procedure; and

establishing a second substrate holder temperature during a first F-E procedure, the second substrate holder temperature being between approximately 40 degrees Celsius and 70 degrees Celsius, and wherein the temperature control system is configured to control the second substrate holder temperature during the first F-E procedure.

8. The method as claimed in claim 7, further comprising:

operating a first radio frequency (RF) source in a first frequency range from approximately 0.1 MHz, to approximately 200 MHz and in a first power range from approximately 100 watts to approximately 700 watts during the first P-E procedure and during the first F-E procedure, wherein the first RF source is coupled to an upper electrode in the first etching chamber; and

operating a second RF source in a second frequency range from approximately 0.1 MHz, to approximately 100 MHz and in a second power range from approximately 10 watts to approximately 170 watts during the first P-E procedure and during the first F-E procedure, wherein the second RF source is coupled to a lower electrode in the first etching chamber.

9. The method as claimed in claim 7, further comprising:

providing a first process gas into the first etching chamber during the first P-E procedure, wherein the first process gas includes CF₄ and O₂, a CF₄ flow rate varying between approximately 10 sccm and approximately 50 sccm and a first O₂ flow rate varying between approximately 3 sccm and approximately 20 sccm wherein the first etching subsystem comprises a gas supply system configured to provide the first process gas to the first etching chamber during the first P-E procedure; and

providing a second process gas into the first etching chamber during the first F-E procedure, wherein the second process gas includes HBr and O₂, a HBr flow rate varying between approximately 20 sccm and approximately 600 sccm and a second O₂ flow rate varying between approximately 2 sccm and approximately 10 sccm, wherein the gas supply system is configured to provide the second process gas to the first etching chamber during the first F-E procedure.

10. The method as claimed in claim 7, further comprising:

controlling a first pressure in the first etching chamber between approximately 5 mTorr and approximately 40 mTorr during the first P-E procedure; and

controlling a second pressure in the first etching chamber between approximately 5 mTorr and approximately 40 mTorr during the first F-E procedure, wherein the first etching subsystem comprises a vacuum pumping system configured to control the first pressure and the second pressure in the first etching chamber.

11. The method as claimed in claim 1, wherein the DOE data includes performance data from sensors intrinsic to a plasma processing chamber and sensors extrinsic to the plasma processing chamber, wherein the performance data includes Helium backside gas pressure data, Helium backside flow data, electrostatic clamping (ESC) voltage data, ESC current data, substrate holder temperature data, coolant temperature data, upper electrode (UEL) temperature data, forward RF power data, reflected RF power data, RF self-induced DC bias data, RF peak-to-peak voltage data, chamber wall temperature data, process gas flow rate data, process gas partial pressures data, chamber pressure data, matching network data, focus ring thickness data, RF hours data, or focus ring RF hours data, or any combination thereof.

12. The method as claimed in claim 1, wherein the DOE data includes performance optical data, End Point Detector (EPD) data, current and/or voltage probe data, power meter data, spectrum analyzer data, RF Impedance analyzer data, time domain data, or frequency domain data, or any combination thereof.

13. The method as claimed in claim 1, wherein the multi-layer gate structures, include pFET structures, nFET structures, Tri-gate structures, and FinFET structures.

14. A method for using a Multi-Layer/Multi-Input/Multi-Output (MLMIMO) model to create gate structures on a plurality of substrates, the method comprising:

a) receiving a first set of substrates and associated substrate data, the substrate data including real-time and historical data;

b) establishing a first number (l) of disturbance variables DV(l) using real-time integrated metrology (IM) data associated with a patterned photoresist layer on one or more incoming substrates, wherein the real-time IM data includes critical dimension (CD) data, sidewall angle (SWA) data, thickness data, photoresist data, BARC data, wafer substrate data, and diffraction signal data from multiple sites in the patterned photoresist layer on each incoming substrate, wherein I is a first integer greater than two;

c) establishing a second number (m) of manipulated variables MV(m), wherein m is a second integer greater than two;

d) establishing a third number (n) of control variables, wherein n is a third integer greater than two and CV(n) is defined as

CV(n)=f_n{MV(1), . . . MV(m−

1),MV(m),DV(1), . . . DV(l−

1),DV(l)}+offsets_n

e) calculating optimized process settings using a quadratic objective function, and target deviations t(n) defined as;

t(n)={DV(n}−

target CV(n)};

f) calculating a plurality of manipulated variables MV(l);

g) defining an adjusted process recipe using one or more of the calculated manipulated variables MV(l) established during nonlinear programming;

h) processing one or more of the first set of substrates using the adjusted process recipe;

I) obtaining additional measurement data for one or more of the processed substrates, wherein new controlled variable (CV) data is obtained and filtered;

j) calculating one or more process errors using differences between measured control variable data and predicted control variable data;

k) calculating feedback data items, wherein errors are used to update the offsets_n using an exponentially weighted moving average (EWMA) filter;

l) updating the model offsets_n in an optimizer unit; and

m) repeating steps a)-l) using each substrate in the first set of substrates.

15. The method as claimed in claim 14, wherein the plurality of manipulated variables MV(l) are calculated by performing nonlinear programming using the quadratic objective function defined as,

16. The method as claimed in claim 14, wherein the weightings w_j are dynamically updated based on feedback error of each CV term.

17. The method as claimed in claim 14, wherein one or more control variable targets are prioritized when one or more manipulated variables are outside an allowable process window.

18. The method as claimed in claim 14, wherein the adjusted process recipe is defined using process state data.

19. The method as claimed in claim 14, wherein the adjusted process recipe is defined using chamber state data.