Multivariate Optical Computing: Application-Specific Optical Sensors And how to create them Michael L. Myrick University of South Carolina Columbia, SC 29208 PG MM MCEC “All we want is a cheap tricorder. Is that too much to ask?” CPL Why is all this complexity necessary? Absorbance Absorbance 20 Species 1 Species 2 15 10 5 400 450 500 550 Wavelength 600 650 20 15 10 5 0 400 450 500 550 Wavelength 600 650 A simple 2-component mixture is trivial to analyze Add an arbitrary amount of another species, and complexity increases 30 Absorbance 25 20 15 10 5 0 400 450 500 550 Wavelength 600 650 The mathematics of prediction in direct spectroscopy are very simple. . . but often very expensive to implement. yest = b • x = b1x1 + b2x2 + b3x3 + b4x4 +. . . where b = “scaled” regression vector and x = spectrum, or spectral vector multiplication Addition detector b1•x(λ1) x(λ1) yest … T (λ1) =b1 b1•x(λ1) b2•x(λ2) b3•x(λ3) b4•x(λ4) b5•x(λ5) Our concept was to use this form of computation to produce small, inexpensive sensors based on multivariate spectroscopy. 1998 Multivariate Optical Computing: The use of optical discriminators to directly sense the magnitude of pectral patterns in light. These optical discriminators encode, n one of several ways, spectral patterns so that multivariate measurements are simplified. We are focussing on interference ilters to serve as optical discriminators. March 15, 2001 Detector for Transmitted Light MOE b(λ) Light from Sample D DTT DR DR Detector for Reflected Light R(λ)=0.5-b(λ) T(λ)=0.5+b(λ) Multivariate optical elements (MOEs) are interference filters air BK7 Nb2O5 SiO2 Bandpass Filter Instrument: intensity vs. BB concentration in the presence of CV interference. Intensity (arb units) SEP ˜ 4 µM 2 4 6 8 10 12 14 16 18 Calibrated Concentration of BB (µM) Anal. Chem. 73 (2001), 1069. MOC result: calibrated (T-R) channel difference vs. BB concentration with CV interference 20 18 SEP = 0.68 µM Predicted conc. (µM) 16 14 12 10 8 6 4 2 2 7 12 17 Actual conc. (µM) Anal. Chem. 73 (2001), 1069. Breakdown of key technologies in MOE application: 1. System Design 2. Radiometry 3. Ellipsometry 4. Thin-film synthesis 5. Thin-film deposition 6. Process control System Design Begins with a Knowledge of Sample Spectroscopy Dimethyl methyl phosphonate/ethyl acetate mixture transmission spectra Conventional multivariate statistics can be used to determine whether a pectrum-based measurement is possible. System Design Determines Precision “poor” J σ 2MC 2 = ξ ∆ ∑ j j 2 σ MOE j=1 ∆ j =Difference in MOE T and R at wavelen gth j ξ j =fractional intensity at λ j in calibratio n set From “Precision in Multivariate Optical Computing”, F.G. Haibach and M.L. Myrick, Appl. Optics 43 (2004), 2130-2140. “so-so” “ideal” A measurement system is selected that provides sensitivity in the appropriate band. light source lens Band selection can be made by a combination of prefiltering (if any sample cell A. light sources B. reproducible filters C. detector response mirrors detector MOE Spectral Radiance of Source x 10 -3 Spectral Radiance (W/nm) 1 0.8 0.6 0.4 0.2 0 400 450 500 550 Wavelength (nm) 600 650 Filter Bandpass Transmittance 0.8 0.6 0.4 0.2 0 400 450 500 550 Wavelength (nm) 600 650 Detector Sensitivity Spectral Sensitivity (V/mW) 4 3.5 3 2.5 2 1.5 1 400 450 500 550 Wavelength (nm) 600 650 Radiometry The simple picture of all wavelengths being equally sensed is incorrect. Optical systems have efficiencies that vary with wavelength. For a given measurement, a “stable” system design must be selected and calibrated. For absorbance or reflectance measurements, a light source, optical train and detectors must all be calibrated. Total Photon flux Detector efficiency Average Optical Bandpass spectral efficiency efficiency radiant flux Ellipsometry Ellipsometry is used to provide “best estimates” of the optical constants of the real materials that will be used to make a MOE. Every deposition chamber is slightly different and changes gradually over time; monthly recalibration is not a bad idea at all. Models in the UV-Vis region are easy. Models in the NIR are slightly more difficult. Models in the MIR are an art form. Deposition and nucleation conditions can change the exact values for a given material and can defeat the best models. Lattice vibrations Electronic transitions Refr. Index Thin Film Design 3.0 1.0 Thickness (100s of nm – 10s of µms) Paul Gemperline and Olusola Soyemi developed an iterative solving procedure to generate structures for MOEs using the SEP as a figure of merit that has become a basis for our current algorithms. A design represents a road map for deposition of a MOE, not a full description of what the finished product will look like. Thin Film Deposition In our laboratory we use reactive magnetron sputtering to make thin films. For 350-1400 nm MOEs we use Nb2O5 and SiO2 films. For longer wavelengths, higher-index materials are available. Oxides deposit at about 100-200 nm/hour. Silicon deposits at about 10X this rate. Most commercial film production is done with e-beam evaporation, which is usually more than 10X faster than sputtering. Process Control Because of nucleation and growth properties of films, their optical properties vary with thickness. Small changes in temperature, oxygen content, deposition rate, etc. can result in deviations of the growing film from the trajectory expected of the design. Nb Si Nb Si Reflectance is a Complex Quantity The Bootstrap Method In the past several months, we have developed a new method of controlling the deposition of thin films in which we abandon complete modeling of the film stack by deriving the complex reflectance at each wavelength using real experimental measurements. The consequence of “bootstrapping” is that the imperfections in already-deposited layers are eliminated so that redesign can be performed using “true” reflectance values rather than idealized values. Bootstrapping has not been automated (yet). Regression Vector of IMOE @ 45º To date, all our development work has been done with visible-absorbing samples because most of our experience has been with thin films for visible optics. 0.8 Regression Vector (unscaled) @ 45º 0.6 Calculated Measured 0.4 0.2 0 Ordinarily, the extension to NIR/MIR would be very difficult because the materials are less dependable. -0.2 -0.4 -0.6 -0.8 -1 400 450 500 550 600 650 However, Bootstrapping does not require “dependable” films! Wavelength, nm We believe that NIR and MIR sensors using multivariate optical computing are within reach once we’ve finalized our automation code. So What Can MOEs be Used For? UV-Vis absorbance Point Sensing Fluorescence/Phosphoresce nce Imaging Classification Raman Scattering Spectral Data Compression Reflectance Adulteration Testing NIR absorbance Miniature optical sensors MIR absorbance In-vivo or In-process sensors Thermal Emission T -Is o 40 20 T-R 0 D una -2 0 -4 0 -6 0 -8 0 - 10 0 x1 0 3 P h ae 0 100 200 300 T+R 400 5 0 0 x1 0 3 Multivariate Optical Computing is not magic: it is another way of performing an end measurement. MOE Advantages MOE Disadvantages Low cost Small Low power Rapid Low-light Mass producible “just give me the result” Not a spectroscopic research tool Fixed spectral interpretation We haven’t made a tricorder yet, but we’re making progress. . . Acknowledgements Paul Gemperline, ECU Norman Schmidt, GSU Olusola Soyemi Karl Booksh Fred Haibach Maria Schiza Raju Karunamuni Matt Nelson Hong Li Ashley Greer David Perkins Ryan Priore Ruth Wang Joshua Farr Burt V. Bronk Funding: ONR, NSF, AFRL*, Detect-X, Foster Miller Inc.