2018-04-14 13:38

2 files changed, 63 insertions, 7 deletions

diff --git a/opa/TOPAS-C.png b/opa/TOPAS-C.png
new file mode 100644
index 0000000..da31d22
--- /dev/null
+++ b/opa/TOPAS-C.png
diff --git a/opa/chapter.tex b/opa/chapter.tex
index 89d4d5d..3d163e2 100644
--- a/opa/chapter.tex
+++ b/opa/chapter.tex
@@ -26,10 +26,10 @@ OPA tuning range into the visible, near-infrared, and mid-infrared.  %
 OPAs are very sensitive to changes in upstream lasers and lab conditions, so OPA tuning is

 regularly required.  %

 Manual OPA tuning can easily take a full day. %

-Furthermore, manual tuning typically results in inferior tuning curves, since it is difficult to

-consider all available information simultaneously.  %

-Automated OPA tuning makes OPA upkeep easier, faster and more reproducible, facilitating frequency

-domain experiments. %

+Furthermore, manual tuning typically results in inferior tuning curves, since it is difficult for

+humans to consider all available information simultaneously.  %

+Automated OPA tuning makes OPA upkeep easier, faster and more reproducible, facilitating higher

+throughput, higher quality frequency domain experiments. %

 The major challenges in automated OPA tuning are:

 \begin{enumerate}

   \item Expensive to take high resoltion data.

@@ -42,10 +42,57 @@ curves.  %
 While I have strategies for all four kinds of OPAs used in the Wright Group, I focus on the

 femtosecond TOPAS-C models because they are by far the most challenging model to calibrate.  %

 

-\clearpage

+\section{Curves} \label{opa:sec:curves}  % ========================================================

+

+OPA tuning curves are the functional correspondence between desired output color and motor

+positions.  %

+In theory, these could be recorded as analytical functions derived in an \textit{ab initio} way

+from known phase matching and dispersion relations.  %

+In practice, ideal tuning curves are determined empirically by simply monitoring OPA output at a

+series of given motor combinations.  %

+This practice of seeing how OPA output depends on motor positions is called ``tuning'' the OPA.  %

+

+I have defined a Python class \python{Curve} which acts as a interface to OPA tuning curves.  %

+Within the class, a series of discrete OPA output colors (``setpoints'') are defined, and the motor

+positions are defined for each setpoint.  %

+Since it is important that OPAs be settable to \emph{any} position within their output range, a

+one-dimensional interpolator is used to determine the correct motor positions for \emph{any} valid

+color.  %

+There are three kinds of interpolators, linear, spline and polynomial.  %

+The particular interpolator used depends on the model of OPA and the complexity of its tuning

+curve.  %

+The method \python{Curve.get_motor_positions} abstracts away this complexity, simply returning a

+list of motor positions for the desired color(s).  %

+

+OPAs often use multiple ``stages'' of interaction to create the desired output.  %

+For example, an OPA might generate signal and idler in a first stage, then send that signal on to

+be doubled in a second ``second harmonic signal'' (SHS) stage.  %

+Depending on the experiment being performed, different stages of the OPA will be used.  %

+One could approach this complexity by simply creating an entirely separate curve for each

+combination of stages, but this would result in the same information being duplicated in many

+different curves.  %

+Instead, I have chosen to use a nested approach that directly reflects the approach that the

+hardware uses.  %

+Curve objects can have ``subcurves'' which define the behavior of the proceeding stage.  %

+In the example above, the parent curve would control the second harmonic signal stage.  %

+For each SHS position, the parent would define a desired signal color for the first stage to

+create.  %

+This is passed to the subcurve, which defines the motor positions needed in the first stage to

+achieve optimal conversion at the desired \emph{signal} color.  %

+In this way, each stage can be tuned separately and the tuning of an upstream stage is immediately

+propagated to all downstream stages.  %

+

+% BJT: consider putting an example curve figure

+

 \section{TOPAS-C}  % ==============================================================================

 

-[INTRODUCTION TO THE TOPAS-C]

+The TOPAS-C is a popular commercially available motorized OPA.  %

+It consists of a large initial stage where signal and idler are generated, and a series of optional

+mixing stages where further up- or down-conversion can occur to widen the total range of output

+frequencies.  %

+\autoref{opa:fig:ranges} shows all of the possible output ranges of the TOPAS.  %

+It ranges from the mid infrared (accessible through difference frequency generation) to the

+ultraviolet (accessible through multiple second harmonic upconversion).  %

 

 % TODO: introduction to the internal design of the OPA

 

@@ -54,7 +101,16 @@ femtosecond TOPAS-C models because they are by far the most challenging model to
   \caption{

     CAPTION TODO

   }

-  \label{opa:fig:preamp}

+  \label{opa:fig:ranges}

+\end{figure}

+

+\begin{figure}

+  \includegraphics[width=\textwidth]{opa/TOPAS-C}

+  \caption[TOPAS-C internal optics and beam path.]{

+    TOPAS-C internal optics and beam path.  %

+    Image taken from manual, originally generated by Light Conversion [CITE].  %

+  }

+  \label{opa:fig:TOPAS-C}

 \end{figure}

 

 \begin{figure}