This paper discusses the architecture of software utilized in spectroscopic measurements. As optical coatings become
more sophisticated, there is mounting need to automate data acquisition (DAQ) from spectrophotometers. Such need is
exacerbated when 100% inspection is required, ancillary devices are utilized, cost reduction is crucial, or security is
vital. While instrument manufacturers normally provide
point-and-click DAQ software, an application programming
interface (API) may be missing. In such cases automation is impossible or expensive.
An API is typically provided in libraries (*.dll, *.ocx) which may be embedded in user-developed applications. Users
can thereby implement DAQ automation in several Windows languages. Another possibility, developed by FTG as an
alternative to instrument manufacturers' software, is the ActiveX application (*.exe). ActiveX, a component of many
Windows applications, provides means for programming and interoperability. This architecture permits a point-and-click
program to act as automation client and server. Excel, for example, can control and be controlled by DAQ applications.
Most importantly, ActiveX permits ancillary devices such as barcode readers and XY-stages to be easily and
economically integrated into scanning procedures. Since an ActiveX application has its own user-interface, it can be
independently tested. The ActiveX application then runs (visibly or invisibly) under DAQ software control.
Automation capabilities are accessed via a built-in spectro-BASIC language with industry-standard (VBA-compatible)
syntax. Supplementing ActiveX, spectro-BASIC also includes auxiliary serial port commands for interfacing programmable logic controllers (PLC). A typical application is automatic filter handling.
Optical coatings are subject to random and systematic errors. Assuming unvarying dispersion, least-squares fitting of
measured spectra provides means to solve for non-gross thickness errors. Unlike coating design, in which many
acceptable and nearly equivalent solutions are possible,
inverse-synthesis requires the unique and correct solution.
We introduce a 'Gedankenspektrum' (thought spectrum) method for determining the range and types of spectra required
for a correct solution. Starting with an ideal design, we simulate production errors and then calculate the spectrum.
Returning to the original design, we solve for the layers corresponding to the modified spectrum. Finally, if each layer is
close to its known value, inverse-synthesis is successful; otherwise it fails. The process is repeated until the statistics
become clear. Reliability depends on the type of design, number of layers, and measurement specifics. Most importantly,
reliability increases markedly when measurements at non-normal incidence are included. This indicates the insufficiency
in the (usual?) practice of measuring optical coatings solely according to pass/fail criteria.
A second 'Gedankenspektrum' method helps decide which spectral measurements and film thicknesses are required for
determining n&k in single films, particularly metals. Starting with given dispersion values, random noise is added to
calculated spectra, thereby simulating measurement conditions. We then solve for n&k and compare to given values.
The optical properties of quasi-inhomogeneous coatings are calculated with standard off-the- shelf optical thin film software running Microsoft Windows. The standard program includes a fast algorithm for repeated groups of layers and allows small index gradations. Index values and layer recipes are generated separately in programs written in Microsoft Excel. These programs approximate inhomogeneous models by the quasi-homogeneous designs required by the standard program. Data is sent to and from the thin film program by dynamic data exchange and the Windows clipboard. The thin film program can be treated as a subroutine. Continuous looping is possible and spectral data is passed back to Excel for analysis. This seamless integration of standard and user-modifiable software was not readily achieved in older operating systems. As an example we discuss an Excel tolerance model for a graded index antireflection design. Thicknesses are subject to random fluctuations simulating manufacturing errors.
Conference Committee Involvement (1)
Optical Modeling and Performance Predictions
6 August 2003 | San Diego, California, United States
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