Chris Payne - PowerPoint PPT Presentation

jeri
characterizing the nanoscale layers of tomorrow s electronics an application of fourier analysis n.
Skip this Video
Loading SlideShow in 5 Seconds..
Chris Payne PowerPoint Presentation
Download Presentation
Chris Payne

play fullscreen
1 / 14
Download Presentation
Chris Payne
125 Views
Download Presentation

Chris Payne

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Characterizing the Nanoscale Layers of Tomorrow’s Electronics :An Application of Fourier Analysis Chris Payne In Collaboration With: Apurva Mehta & Matt Bibee

  2. A Relevant Challenge The electronics industry is increasingly focusing on thin film applications … Moore’s Law demands smaller devices Economically smart Compatible with current fabrication facilities …but they need a way to characterize the layer structure of these devices on the nanometer scale ~200nm Bearing in mind a single page is about 100,000 nanometers thick

  3. Defining the Challenge • Number of Layers • Layer Order • Individual Layer Thickness • Individual Layer Density • Chemistry • Individual Layer Roughness X-Ray Reflectivity Can Help Provide A Comprehensive Answer to All these Questions 1 2 Zinc Oxide(1) Silicon(2) 3 Zinc Oxide(3) Glass Substrate Cross sectional SEM look at a solar cell

  4. Our Tool : XRR Reflectivity Detector λ X-Ray Source z Thin Top Layer X-Ray Source Reflectivity Detector Substrate Θ The path length difference causes interference patterns to arise at the detector according to:

  5. How this Interference appears in the Data λ The oscillations carry the information we want! The varying interference appears as oscillations that span over 8 Orders of Magnitude! The path length distance, a function of Θ and Z, is embedding information in the interference pattern seen by the detector, But what does this interference look like? z Θ

  6. Extracting Oscillations Mathematically 1. We first convert Θto S which importantly gives the X – axis units of m-1 Z Substrate Layer 2 Layer 1 2. The intensity can now be approximated (assuming no roughness) as Z Θ I Derivate of Density Depth Along Z

  7. Extracting Oscillations Mathematically 3. Lastly, lets cut out the Falloff term and free the thickness information from the FT, by taking the inverse FT 2N Algorithm (Because the falloff isn’t as simple ass4) Inverse Fourier Transform

  8. Applying the Math to Simulated Data Layer 2 Layer 1 20 nm Substrate 2 nm Using a simulation program, I generate raw intensity data for two layers on a substrate

  9. Applying the Math to Simulated Data Then I convert to s:

  10. Applying the Math to Simulated Data S [Gm-1] Calculate local average point by point using 2N Method

  11. Applying the Math to Simulated Data S [Gm-1] Remove the falloff:

  12. Applying the Math to Simulated Data Layer 2 Layer 1 Substrate Nanometers Important Note: At this time, this technique does not indicate the order of the layers! Take the FT inverse, to ‘unlock’ FT:

  13. Applying this Process to Real Data Original Sample Cleaned Sample ??? Silicon Oxide SiC Substrate ??? Silicon Oxide SiC Substrate

  14. Thank You For Your Time Especially Apurva Mehta & Matt Bibee