X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/dmems12.git/blobdiff_plain/79572a1f43f7dd9f07ba3d9a96f8a8958e267379..b5dcb332822aed6879619b36c36ad800b7672e2f:/dmems12.tex?ds=inline diff --git a/dmems12.tex b/dmems12.tex index 4a6bd69..0c00ee0 100644 --- a/dmems12.tex +++ b/dmems12.tex @@ -1,12 +1,813 @@ -\documentclass{article} +%\usepackage{latex8} +%\usepackage{times} +%\usepackage[cyr]{aeguill} +%\usepackage{pstricks,pst-node,pst-text,pst-3d} +%\usepackage{babel} +%\input{psfig.sty} +%%%%%%%%%%%%%%%%%%%%%%%%%%%% LyX specific LaTeX commands. + + +\documentclass[10pt, peerreview, compsocconf]{IEEEtran} +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\usepackage[utf8]{inputenc} +\usepackage{amsmath} +\usepackage{url} +\usepackage{graphicx} +\usepackage{thumbpdf} +\usepackage{color} +\usepackage{moreverb} +\usepackage{commath} +\usepackage{subfigure} +\usepackage{fullpage} +\usepackage{fancybox} +\usepackage[ruled,lined,linesnumbered]{algorithm2e} + +\setcounter{MaxMatrixCols}{10} +%TCIDATA{OutputFilter=LATEX.DLL} +%TCIDATA{Version=5.50.0.2953} +%TCIDATA{} +%TCIDATA{BibliographyScheme=BibTeX} +%TCIDATA{LastRevised=Wednesday, October 26, 2011 09:49:54} +%TCIDATA{} + +\newcommand{\noun}[1]{\textsc{#1}} +\newcommand{\tab}{\ \ \ } + + \begin{document} -\abstract { -In this paper we describe.... -} +\title{A new approach based on a least square method for real-time estimation of cantilever array deflections with a FPGA} +\author{\IEEEauthorblockN{Raphaël Couturier\IEEEauthorrefmark{1}, Stéphane +Domas\IEEEauthorrefmark{1}, Gwenhaël Goavec-Merou\IEEEauthorrefmark{2} and +Michel Lenczner\IEEEauthorrefmark{2}} +\IEEEauthorblockA{\IEEEauthorrefmark{1}FEMTO-ST, DISC, University of Franche-Comte, Belfort, France \and +\{raphael.couturier,stephane.domas\}@univ-fcomte.fr} +\IEEEauthorblockA{\IEEEauthorrefmark{2}FEMTO-ST, Time-Frequency, University of Franche-Comte, Besançon, France \and +\{michel.lenczner@utbm.fr,gwenhael.goavec@trabucayre.com} } + +\begin{abstract} +Atomic force microscopes (AFM) provide high resolution images of surfaces. +In this paper, we focus our attention on an interferometry method for +deflection estimation of cantilever arrays in quasi-static regime. In its +original form, spline interpolation was used to determine interference +fringe phase, and thus the deflections. Computations were performed on a PC. +Here, we propose a new complete solution with a least square based algorithm +and an optimized FPGA implementation. Simulations and real tests showed very +good results and open perspective for real-time estimation and control of +cantilever arrays in the dynamic regime. +\end{abstract} + +%% \author{\IEEEauthorblockN{Authors Name/s per 1st Affiliation (Author)} +%% \IEEEauthorblockA{line 1 (of Affiliation): dept. name of organization\\ +%% line 2: name of organization, acronyms acceptable\\ +%% line 3: City, Country\\ +%% line 4: Email: name@xyz.com} +%% \and +%% \IEEEauthorblockN{Authors Name/s per 2nd Affiliation (Author)} +%% \IEEEauthorblockA{line 1 (of Affiliation): dept. name of organization\\ +%% line 2: name of organization, acronyms acceptable\\ +%% line 3: City, Country\\ +%% line 4: Email: name@xyz.com} +%% } + +%\maketitle + +\thispagestyle{empty} + +\begin{IEEEkeywords} +FPGA, cantilever arrays, interferometry. +\end{IEEEkeywords} + +\IEEEpeerreviewmaketitle \section{Introduction} -\section{Conclusion} +Cantilevers are used in atomic force microscopes (AFM) which provide high +resolution surface images. Several techniques have been reported in +literature for cantilever displacement measurement. In~\cite{CantiPiezzo01}, +authors have shown how a piezoresistor can be integrated into a cantilever +for deflection measurement. Nevertheless this approach suffers from the +complexity of the microfabrication process needed to implement the sensor. +In~\cite{CantiCapacitive03}, authors have presented a cantilever mechanism +based on capacitive sensing. These techniques require cantilever +instrumentation resulting in\ complex fabrication processes. + +In this paper our attention is focused on a method based on interferometry for +cantilever displacement measurement in quasi-static regime. Cantilevers are +illuminated by an optical source. Interferometry produces fringes enabling +cantilever displacement computation. A high speed camera is used to analyze the +fringes. In view of real time applications, images need to be processed quickly +and then a fast estimation method is required to determine the displacement of +each cantilever. In~\cite{AFMCSEM11}, an algorithm based on spline has been +introduced for cantilever position estimation. The overall process gives +accurate results but computations are performed on a standard computer using +LabView \textsuperscript{\textregistered} \textsuperscript{\copyright}. +Consequently, the main drawback of this implementation is that the computer is a +bottleneck. In this paper we pose the problem of real-time cantilever position +estimation and bring a hardware/software solution. It includes a fast method +based on least squares and its FPGA implementation. + +The remainder of the paper is organized as follows. Section~\ref{sec:measure} +describes the measurement process. Our solution based on the least square +method and its implementation on a FPGA is presented in Section~\ref{sec:solus}. Numerical experimentations are described in Section~\ref{sec:results}. Finally a conclusion and some perspectives are drawn. + +\section{Architecture and goals} + +\label{sec:measure} + +In order to build simple, cost effective and user-friendly cantilever +arrays, authors of ~\cite{AFMCSEM11} have developed a system based on +interferometry. + +\subsection{Experimental setup} + +\label{sec:archi} + +In opposition to other optical based system\textbf{s u}sing a laser beam +deflection scheme and sensitive to the angular displacement of the +cantilever, interferometry is sensitive to the optical path difference +induced by the vertical displacement of the cantilever. + +The system is based on a Linnick interferometer~\cite{Sinclair:05}. +It is illustrated in Figure~\ref{fig:AFM} \footnote{by courtesy of + CSEM}. A laser diode is first split (by the splitter) into a +reference beam and a sample beam both reaching the cantilever array. +The complete system including a cantilever array\ and the optical +system can be moved thanks to a translation and rotational hexapod +stage with five degrees of freedom. Thus, the cantilever array is +centered in the optical system which can be adjusted accurately. The +beam illuminates the array by a microscope objective and the light +reflects on the cantilevers. Likewise the reference beam reflects on a +movable mirror. A CMOS camera chip records the reference and sample +beams which are recombined in the beam splitter and the +interferogram. At the beginning of each experiment, the movable mirror +is fitted manually in order to align the interferometric fringes +approximately parallel to the cantilevers. Then, cantilever motion in +the transverse direction produces movements in the fringes. They are +detected with the CMOS camera which images are analyzed by a Labview +program to recover the cantilever deflections. + +\begin{figure}[tbp] +\begin{center} +\includegraphics[width=\columnwidth]{AFM} +\end{center} +\caption{AFM Setup} +\label{fig:AFM} +\end{figure} + +%% image tirée des expériences. + +\subsection{Inteferometric based cantilever deflection estimation} + +\label{sec:deflest} + +\begin{figure}[tbp] +\begin{center} +\includegraphics[width=\columnwidth]{lever-xp} +\end{center} +\caption{Portion of a camera image showing moving interferometric fringes in +cantilevers} +\label{fig:img-xp} +\end{figure} + +As shown in Figure \ref{fig:img-xp} \footnote{by courtesy of CSEM}, each +cantilever is covered by several interferometric fringes. The fringes +distort when cantilevers are deflected. In \cite{AFMCSEM11}, a novel +method for interferometric based cantilever deflection measurement was +reported. For each cantilever, the method uses three segments of pixels, +parallel to its section, to determine phase shifts. The first is +located just above the AFM tip (tip profile), it provides the phase +shift modulo $2\pi $. The second one is close to the base junction +(base profile) and is used to determine the exact multiple of $2\pi $ +through an operation called unwrapping where it is assumed that the +deflection means along the two measurement segments are linearly +dependent. The third is on the base and provides a reference for +noise suppression. Finally, deflections are simply derived from phase +shifts. + +The pixel gray-level intensity $I$ of each profile is modelized by% +\begin{equation} +I(x)=A\text{ }\cos (2\pi fx+\theta )+ax+b \label{equ:profile} +\end{equation}% +where $x$ denotes the position of a pixel in a segment, $A$, $f$ and $\theta +$ are the amplitude, the frequency and the phase of the light signal when +the affine function $ax+b$ corresponds to the cantilever array surface tilt +with respect to the light source. + +The method consists in two main sequences. In the first one +corresponding to precomputation, the frequency $f$ of each profile is +determined using a spline interpolation (see section \ref% +{sec:algo-spline}) and the coefficient used for phase unwrapping is +computed. The second one, that we call the \textit{acquisition loop,} +is done after images have been taken at regular time steps. For each +image, the phase $\theta $ of all profiles is computed to obtain, +after unwrapping, the cantilever deflection. The phase determination +in \cite{AFMCSEM11} is achieved by a spline based algorithm which is +the most consuming part of the computation. In this article, we +propose an alternate version based on the least square method which is +faster and better suited for FPGA implementation. + +\subsection{Computation design goals} + +\label{sec:goals} + +To evaluate the solution performances, we choose a goal which consists +in designing a computing unit able to estimate the deflections of +a $10\times 10$% +-cantilever array, faster than the camera image stream. In addition, +the result accuracy must be close to 0.3nm, the maximum precision +reached in \cite{AFMCSEM11}. Finally, the latency between the entrance +of the first pixel of an image and the end of deflection computation +must be as small as possible. All these requirement are +stated in the perspective of implementing real-time active control for +each cantilever, see~\cite{LencznerChap10,Hui11}. + +If we put aside other hardware issues like the speed of the link +between the camera and the computation unit, the time to deserialize +pixels and to store them in memory, the phase computation is the +bottleneck of the whole process. For example, the camera in the setup +of \cite{AFMCSEM11} provides $% +1024\times 1204$ pixels with an exposition time of 2.5ms. Thus, if we +the pixel extraction time is neglected, each phase calculation of a +100-cantilever array should take no more than 12.5$\mu$s. + +In fact, this timing is a very hard constraint. To illustrate this point, we +consider a very small program that initializes twenty million of doubles in +memory and then does 1,000,000 cumulated sums on 20 contiguous values +(experimental profiles have about this size). On an intel Core 2 Duo E6650 +at 2.33GHz, this program reaches an average of 155Mflops. +Obviously, some cache effects and optimizations on huge amount of +computations can drastically increase these performances: peak efficiency is +about 2.5Gflops for the considered CPU. But this is not the case for phase +computation that is using only a few tenth of values. + +In order to evaluate the original algorithm, we translated it in C language. +As stated before, for 20 pixels, it does about 1,550 operations, thus an +estimated execution time of $1,550/155=$10$\mu $s. For a more realistic +evaluation, we constructed a file of 1Mo containing 200 profiles of 20 +pixels, equally scattered. This file is equivalent to an image stored in a +device file representing the camera. We obtained an average of 10.5$\mu$s +by profile (including I/O accesses). It is under our requirements but close +to the limit. In case of an occasional load of the system, it could be +largely overtaken. Solutions would be to use a real-time operating system or +to search for a more efficient algorithm. + +However, the main drawback is the latency of such a solution because each +profile must be treated one after another and the deflection of 100 +cantilevers takes about $200\times 10.5=2.1$ms. This would be inadequate +for real-time requirements as for individual cantilever active control. An +obvious solution is to parallelize the computations, for example on a GPU. +Nevertheless, the cost of transferring profile in GPU memory and of taking +back results would be prohibitive compared to computation time. + +We remark that when possible, it is more efficient to pipeline the +computation. For example, supposing that 200 profiles of 20 pixels +could be pushed sequentially in a pipelined unit cadenced at a 100MHz +(i.e. a pixel enters in the unit each 10ns), all profiles would be +treated in $200\times 20\times 10.10^{-9}=$ 40$\mu$s plus the latency +of the pipeline. Such a solution would be meeting our requirements and +would be 50 times faster than our C code, and even more compared to +the LabView version use in \cite{AFMCSEM11}. FPGAs are appropriate for +such implementation, so they turn out to be the computation units of +choice to reach our performance requirements. Nevertheless, passing +from a C code to a pipelined version in VHDL is not obvious at all. It +can even be impossible because of FPGA hardware constraints. All these +points are discussed in the following sections. + +\section{An hardware/software solution} + +\label{sec:solus} + +In this section we present parts of the computing solution to the above +requirements. The hardware part consists in a high-speed camera, linked on an +embedded board hosting two FPGAs. In this way, the camera output stream can be +pushed directly into the FPGA. The software part is mostly the VHDL code that +deserializes the camera stream, extracts profiles and computes the deflection. + +We first give some general information about FPGAs, then we +describe the FPGA board we use for implementation and finally the two +algorithms for phase computation are detailed. Presentation of VHDL +implementations is postponned until Section \ref{Experimental tests}. + + + +\subsection{Elements of FPGA architecture and programming} + +A field-programmable gate array (FPGA) is an integrated circuit designed to +be configured by the customer. FGPAs are composed of programmable logic +components, called configurable logic blocks (CLB). These blocks mainly +contain look-up tables (LUT), flip/flops (F/F) and latches, organized in one +or more slices connected together. Each CLB can be configured to perform +simple (AND, XOR, ...) or complex combinational functions. They are +interconnected by reconfigurable links. Modern FPGAs contain memory elements +and multipliers which enable to simplify the design and to increase the +performance. Nevertheless, all other complex operations like division and +other functions like trigonometric functions are not available and must be +built by configuring a set of CLBs. Since this is not an obvious task at +all, tools like ISE~\cite{ISE} have been built to do this operation. Such a +software can synthetize a design written in a hardware description language +(HDL), maps it onto CLBs, place/route them for a specific FPGA, and finally +produces a bitstream that is used to configure the FPGA. Thus, from the +developer's point of view, the main difficulty is to translate an algorithm +into HDL code, taking into account FPGA resources and constraints like clock +signals and I/O values that drive the FPGA. + +Indeed, HDL programming is very different from classic languages like +C. A program can be seen as a state-machine, manipulating signals that +evolve from state to state. Moreover, HDL instructions can be executed +concurrently. Signals may be combined with basic logic operations to +produce new states that are assigned to another signal. States are mainly expressed as +arrays of bits. Fortunately, libraries propose some higher levels +representations like signed integers, and arithmetic operations. + +Furthermore, even if FPGAs are cadenced more slowly than classic processors, +they can perform pipelines as well as parallel operations. A pipeline +consists in cutting a process in a sequence of small tasks, taking the same +execution time. It accepts a new data at each clock top, thus, after a known +latency, it also provides a result at each clock top. We observe that the +components of a task are not reusable by another one. Nevertheless, this is +the most efficient technique on FPGAs. Because of their architecture, it is +also very easy to process several data concurrently. Finally, the best +performance can be reached when several pipelines are operating on multiple +data streams in parallel. + +\subsection{The FPGA board} + +The architecture we use is designed by the Armadeus Systems +company. It consists in a development board called APF27 \textsuperscript{\textregistered}, hosting a +i.MX27 ARM processor (from Freescale) and a Spartan3A (from +XIlinx). This board includes all classical connectors as USB and +Ethernet for instance. A Flash memory contains a Linux kernel that can +be launched after booting the board via u-Boot. The processor is +directly connected to the Spartan3A via its special interface called +WEIM. The Spartan3A is itself connected to an extension board called +SP Vision \textsuperscript{\textregistered}, that hosts a Spartan6 FPGA. Thus, it is +possible to develop programs that communicate between i.MX and +Spartan6, using Spartan3 as a tunnel. A clock signal at 100MHz (by +default) is delivered to dedicated FPGA pins. The Spartan6 of our +board is an LX100 version. It has 15,822 slices, each slice containing +4 LUTs and 8 flip/flops. It is equivalent to 101,261 logic +cells. There are 268 internal block RAM of 18Kbits, and 180 dedicated +multiply-adders (named DSP48), which is largely enough for our +project. Some I/O pins of Spartan6 are connected to two $2\times 17$ +headers that can be used for any purpose as to be connected to the +interface of a camera. + +\subsection{Two algorithms for phase computation} + +In \cite{AFMCSEM11}, $f$ the frequency and $\theta $\ the phase of the +light wave are computed thanks to spline interpolation. As said in +section \ref{sec:deflest}, $f$ is computed only once time but the +phase needs to be computed for each image. This is why, in this paper, +we focus on its computation. + +\subsubsection{Spline algorithm (SPL)} + +\label{sec:algo-spline} + +We denote by $M$ the number of pixels in a segment used for phase +computation. For the sake of simplicity of the notations, we consider +the light intensity $I$ to be a mapping of the physical segment in the +interval $[0,M[$. The pixels are assumed to be regularly spaced and +centered at the positions $x^{p}\in\{0,1,\ldots,M-1\}.$ We use the simplest +definition of a pixel, namely the value of $I$ at its center. The +pixel intensities are considered as pre-normalized so that their +minimum and maximum have been resized to $-1$ and $1$. + +The first step consists in computing the cubic spline interpolation of +the intensities. This allows for interpolating $I$ at a larger number +$L=k\times M$ of points (typically $k=4$ is sufficient) $% +x^{s}$ in the interval $[0,M[$. During the precomputation sequence, +the second step is to determin the afine part $a.x+b$ of $I$. It is +found with an ordinary least square method, taking account the $L$ +points. Values of $I$ in $x^s$ are used to compute its intersections +with $a.x+b$. The period of $I$ (and thus its frequency) is deduced +from the number of intersections and the distance between the first +and last. + +During the acquisition loop, the second step is the phase computation, with +\begin{equation} +\theta =atan\left[ \frac{\sum_{i=0}^{N-1}\text{sin}(2\pi fx_{i}^{s})\times +I(x_{i}^{s})}{\sum_{i=0}^{N-1}\text{cos}(2\pi fx_{i}^{s})\times I(x_{i}^{s})}% +\right] . +\end{equation} + +\textit{Remarks: } + +\begin{itemize} +\item The frequency could also be obtained using the derivates of spline +equations, which only implies to solve quadratic equations but certainly +yields higher errors. + +\item Profile frequency are computed during the precomputation step, + thus the values sin$(2\pi fx_{i}^{s})$ and cos$(2\pi fx_{i}^{s})$ + can be determined once for all. +\end{itemize} + +\subsubsection{Least square algorithm (LSQ)} + +Assuming that we compute the phase during the acquisition loop, equation \ref% +{equ:profile} has only 4 parameters: $a,b,A$, and $\theta $, $f$ and $x$ +being already known. Since $I$ is non-linear, a least square method based on +a Gauss-newton algorithm can be used to determine these four parameters. +This kind of iterative process ends with a convergence criterion, so it is +not suited to our design goals. Fortunately, it is quite simple to reduce +the number of parameters to only $\theta $. Firstly, the afine part $ax+b$ +is estimated from the $M$ values $I(x^{p})$ to determine the rectified +intensities,% +\begin{equation*} +I^{corr}(x^{p})\approx I(x^{p})-a.x^{p}-b. +\end{equation*}% +To find $a$ and $b$ we apply an ordinary least square method (as in SPL but on $M$ points)% +\begin{equation*} +a=\frac{covar(x^{p},I(x^{p}))}{\text{var}(x^{p})}\text{ and }b=\overline{% +I(x^{p})}-a.\overline{{x^{p}}} +\end{equation*}% +where overlined symbols represent average. Then the amplitude $A$ is +approximated by% +\begin{equation*} +A\approx \frac{\text{max}(I^{corr})-\text{min}(I^{corr})}{2}. +\end{equation*}% +Finally, the problem of approximating $\theta $ is reduced to minimizing% +\begin{equation*} +\min_{\theta \in \lbrack -\pi ,\pi ]}\sum_{i=0}^{M-1}\left[ \text{cos}(2\pi +f.i+\theta )-\frac{I^{corr}(i)}{A}\right] ^{2}. +\end{equation*}% +An optimal value $\theta ^{\ast }$ of the minimization problem is a zero of +the first derivative of the above argument,%\begin{eqnarray*}{l} +\begin{equation*} +2\left[ \text{cos}\theta ^{\ast }\sum_{i=0}^{M-1}I^{corr}(i).\text{sin}(2\pi +f.i)\right. +\end{equation*}% +\begin{equation*} +\left. +\text{sin}\theta ^{\ast }\sum_{i=0}^{M-1}I^{corr}(i).\text{cos}(2\pi +f.i)\right] - +\end{equation*}% +\begin{equation*} +A\left[ \text{cos}2\theta ^{\ast }\sum_{i=0}^{M-1}\sin (4\pi f.i)+\text{sin}% +2\theta ^{\ast }\sum_{i=0}^{M-1}\cos (4\pi f.i)\right] =0 +\end{equation*}% +% +%\end{eqnarray*} + +Several points can be noticed: + +\begin{itemize} +\item The terms $\sum_{i=0}^{M-1}$sin$(4\pi f.i)$ and$\sum_{i=0}^{M-1}$cos$% +(4\pi f.i)$ are independent of $\theta $, they can be precomputed. + +\item Lookup tables (namely lut$_{sfi}$ and lut$_{cfi}$ in the following algorithms) can be + set with the $2.M$ values $\sin (2\pi f.i)$ and $\cos (2\pi f.i)$. + +\item A simple method to find a zero $\theta ^{\ast }$ of the optimality +condition is to discretize the range $[-\pi ,\pi ]$ with a large number $% +nb_{s}$ of nodes and to find which one is a minimizer in the absolute value +sense. Hence, three other lookup tables (lut$_{s}$, lut$_{c}$ and lut$_{A}$) can be set with the $% +3\times nb_{s}$ values $\sin \theta ,$ $\cos \theta ,$ +\begin{equation*} +\left[ cos2\theta \sum_{i=0}^{M-1}sin(4\pi f.i)+sin2\theta +\sum_{i=0}^{M-1}cos(4\pi f.i)\right] . +\end{equation*} + +\item The search algorithm can be very fast using a dichotomous process in $% +log_{2}(nb_{s}).$ +\end{itemize} + +The overall method is synthetized in an algorithm (called LSQ in the +following) divided into the precomputing part and the acquisition loop: + +\begin{algorithm}[htbp] +\caption{LSQ algorithm - before acquisition loop.} +\label{alg:lsq-before} + + $M \leftarrow $ number of pixels of the profile\\ + I[] $\leftarrow $ intensity of pixels\\ + $f \leftarrow $ frequency of the profile\\ + $s4i \leftarrow \sum_{i=0}^{M-1} sin(4\pi f.i)$\\ + $c4i \leftarrow \sum_{i=0}^{M-1} cos(4\pi f.i)$\\ + $nb_s \leftarrow $ number of discretization steps of $[-\pi,\pi]$\\ + + \For{$i=0$ to $nb_s $}{ + $\theta \leftarrow -\pi + 2\pi\times \frac{i}{nb_s}$\\ + lut$_s$[$i$] $\leftarrow sin \theta$\\ + lut$_c$[$i$] $\leftarrow cos \theta$\\ + lut$_A$[$i$] $\leftarrow cos 2 \theta \times s4i + sin 2 \theta \times c4i$\\ + lut$_{sfi}$[$i$] $\leftarrow sin (2\pi f.i)$\\ + lut$_{cfi}$[$i$] $\leftarrow cos (2\pi f.i)$\\ + } +\end{algorithm} + +\begin{algorithm}[htbp] +\caption{LSQ algorithm - during acquisition loop.} +\label{alg:lsq-during} + + $\bar{x} \leftarrow \frac{M-1}{2}$\\ + $\bar{y} \leftarrow 0$, $x_{var} \leftarrow 0$, $xy_{covar} \leftarrow 0$\\ + \For{$i=0$ to $M-1$}{ + $\bar{y} \leftarrow \bar{y} + $ I[$i$]\\ + $x_{var} \leftarrow x_{var} + (i-\bar{x})^2$\\ + } + $\bar{y} \leftarrow \frac{\bar{y}}{M}$\\ + \For{$i=0$ to $M-1$}{ + $xy_{covar} \leftarrow xy_{covar} + (i-\bar{x}) \times (I[i]-\bar{y})$\\ + } + $slope \leftarrow \frac{xy_{covar}}{x_{var}}$\\ + $start \leftarrow y_{moy} - slope\times \bar{x}$\\ + \For{$i=0$ to $M-1$}{ + $I[i] \leftarrow I[i] - start - slope\times i$\\ + } + + $I_{max} \leftarrow max_i(I[i])$, $I_{min} \leftarrow min_i(I[i])$\\ + $amp \leftarrow \frac{I_{max}-I_{min}}{2}$\\ + + $Is \leftarrow 0$, $Ic \leftarrow 0$\\ + \For{$i=0$ to $M-1$}{ + $Is \leftarrow Is + I[i]\times $ lut$_{sfi}$[$i$]\\ + $Ic \leftarrow Ic + I[i]\times $ lut$_{cfi}$[$i$]\\ + } + + $\delta \leftarrow \frac{nb_s}{2}$, $b_l \leftarrow 0$, $b_r \leftarrow \delta$\\ + $v_l \leftarrow -2.I_s - amp.$lut$_A$[$b_l$]\\ + + \While{$\delta >= 1$}{ + + $v_r \leftarrow 2.[ Is.$lut$_c$[$b_r$]$ + Ic.$lut$_s$[$b_r$]$ ] - amp.$lut$_A$[$b_r$]\\ + + \If{$!(v_l < 0$ and $v_r >= 0)$}{ + $v_l \leftarrow v_r$ \\ + $b_l \leftarrow b_r$ \\ + } + $\delta \leftarrow \frac{\delta}{2}$\\ + $b_r \leftarrow b_l + \delta$\\ + } + \uIf{$!(v_l < 0$ and $v_r >= 0)$}{ + $v_l \leftarrow v_r$ \\ + $b_l \leftarrow b_r$ \\ + $b_r \leftarrow b_l + 1$\\ + $v_r \leftarrow 2.[ Is.$lut$_c$[$b_r$]$ + Ic.$lut$_s$[$b_r$]$ ] - amp.$lut$_A$[$b_r$]\\ + } + \Else { + $b_r \leftarrow b_l + 1$\\ + } + + \uIf{$ abs(v_l) < v_r$}{ + $b_{\theta} \leftarrow b_l$ \\ + } + \Else { + $b_{\theta} \leftarrow b_r$ \\ + } + $\theta \leftarrow \pi\times \left[\frac{2.b_{ref}}{nb_s}-1\right]$\\ + +\end{algorithm} + +\subsubsection{Algorithm comparison} + +We compared the two algorithms on the base of three criteria: + +\begin{itemize} +\item precision of results on a cosines profile, distorted by noise, + +\item number of operations, + +\item complexity of FPGA implementation +\end{itemize} + +For the first item, we produced a matlab version of each algorithm, +running in double precision. The profile was generated for about +34,000 different quadruplets of periods ($\in \lbrack 3.1,6.1]$, step += 0.1), phases ($\in \lbrack -3.1,3.1]$, steps = 0.062) and slope +($\in \lbrack -2,2]$, step = 0.4). Obviously, the discretization of +$[-\pi ,\pi ]$ introduces an error in the phase estimation. It is at +most equal to $\frac{\pi}{nb_s}$. From some experiments on a $17\times +4$ array, authors of \cite{AFMCSEM11} noticed a average ratio of 50 +between phase variation in radians and lever end position in +nanometers. Assuming such a ratio and $nb_s = 1024$, the maximum lever +deflection error would be 0.15nm which is smaller than 0.3nm, the best +precision achieved with the setup used in \cite{AFMCSEM11}. + +Moreover, pixels have been paired and the paired intensities have been +perturbed by addition of a random number uniformly picked in +$[-N,N]$. Notice that we have observed that perturbing each pixel +independently yields too weak profile distortion. We report +percentages of errors between the reference and the computed phases +out of $2\pi ,$% +\begin{equation*} +err=100\times \frac{|\theta _{ref}-\theta _{comp}|}{2\pi }. +\end{equation*}% +Table \ref{tab:algo_prec} gives the maximum and the average errors for both +algorithms and for increasing values of $N$ the noise parameter. + +\begin{table}[ht] +\begin{center} +\begin{tabular}{|c|c|c|c|c|} +\hline +& \multicolumn{2}{c|}{SPL} & \multicolumn{2}{c|}{LSQ} \\ \cline{2-5} +noise (N)& max. err. & aver. err. & max. err. & aver. err. \\ \hline +0 & 2.46 & 0.58 & 0.49 & 0.1 \\ \hline +2.5 & 2.75 & 0.62 & 1.16 & 0.22 \\ \hline +5 & 3.77 & 0.72 & 2.47 & 0.41 \\ \hline +7.5 & 4.72 & 0.86 & 3.33 & 0.62 \\ \hline +10 & 5.62 & 1.03 & 4.29 & 0.81 \\ \hline +15 & 7.96 & 1.38 & 6.35 & 1.21 \\ \hline +30 & 17.06 & 2.6 & 13.94 & 2.45 \\ \hline +\end{tabular}% +\end{center} +\caption{Error (in \%) for cosines profiles, with noise.} +\label{tab:algo_prec} +\end{table} + +The results show that the two algorithms yield close results, with a slight +advantage for LSQ. Furthermore, both behave very well against noise. +Assuming an average ratio of 50 (see above), an error of 1 percent on +the phase corresponds to an error of 0.5nm on the lever deflection, which is +very close to the best precision. + +It is very hard to predict which level of noise will be present in +real experiments and how it will distort the profiles. Authors of +\cite{AFMCSEM11} gave us the authorization to exploit some of their +results on a $17\times 4$ array. It allowed us to compare experimental +profiles to simulated ones. We can see on figure \ref{fig:noise20} the +profile with $N=10$ that leads to the biggest error. It is a bit +distorted, with pikes and straight/rounded portions. In fact, it is +very close to some of the worst experimental profiles. Figure +\ref{fig:noise60} shows a sample of worst profile for $N=30$. It is +completely distorted, largely beyond any experimental ones. Obviously, +these comparisons are a bit subjectives and experimental profiles +could also be completly distorted on other experiments. Nevertheless, +they give an idea about the possible error. + +\begin{figure}[ht] +\begin{center} +\includegraphics[width=\columnwidth]{intens-noise20} +\end{center} +\caption{Sample of worst profile for N=10} +\label{fig:noise20} +\end{figure} + +\begin{figure}[ht] +\begin{center} +\includegraphics[width=\columnwidth]{intens-noise60} +\end{center} +\caption{Sample of worst profile for N=30} +\label{fig:noise60} +\end{figure} + +The second criterion is relatively easy to estimate for LSQ and harder for +SPL because of the use of the arctangent function. In both cases, the number +of operation is proportional to $M$ the numbers of pixels. For LSQ, it also +depends on $nb_{s}$ and for SPL on $L=k\times M$ the number of interpolated +points. We assume that $M=20$, $nb_{s}=1024$ and $k=4$, that all possible +parts are already in lookup tables and that a limited set of operations (+, +-, *, /, $<$, $>$) is taken into account. Translating both algorithms in C +code, we obtain about 430 operations for LSQ and 1,550 (plus a few tenth for +$atan$) for SPL. This result is largely in favor of LSQ. Nevertheless, +considering the total number of operations is not fully relevant for FPGA +implementation which time and space consumption depends not only on the type +of operations but also of their ordering. The final evaluation is thus very +much driven by the third criterion. + +The Spartan 6 used in our architecture has a hard constraint since it +has no built-in floating point units. Obviously, it is possible to use +some existing "black-boxes" for double precision operations. But they +require a lot of clock cycles to complete. It is much simpler to +exclusively use integers, with a quantization of all double precision +values. It should be chosen in a manner that does not alterate result +precision. Furthermore, it should not lead to a design with a huge +latency because of operations that could not complete during a single +or few clock cycles. Divisions fall into that category and, moreover, +they need a varying number of clock cycles to complete. Even +multiplications can be a problem since a DSP48 takes inputs of 18 bits +maximum. So, for larger multiplications, several DSP must be combined +which increases the overall latency. + +In the present algorithms, the hardest constraint does not come from the +FPGA characteristics but from the algorithms. Their VHDL implementation can +be efficient only if they can be fully (or near) pipelined. We observe that +only a small part of SPL can be pipelined, indeed, the computation of spline +coefficients implies to solve a linear tridiagonal system which matrix and +right-hand side are computed from incoming pixels intensity but after, the +back-solve starts with the latest values, which breaks the pipeline. +Moreover, SPL relies on interpolating far more points than profile size. +Thus, the end of SPL works on a larger amount of data than at the beginning, +which also breaks the pipeline. + +LSQ has not this problem since all parts, except the dichotomic search, work +on the same amount of data, i.e. the profile size. Furthermore, LSQ requires +less operations than SPL, implying a smaller output latency. In total, LSQ +turns out to be the best candidate for phase computation on any architecture +including FPGA. + +\section{VHDL implementation and experimental tests} + +\label{Experimental tests} + +\subsection{VHDL implementation} + +From the LSQ algorithm, we have written a C program that uses only +integer values. We used a very simple quantization which consists in +multiplying each double precision value by a factor power of two and +by keeping the integer part. For an accurate evaluation of the +division in the computation of $a$ the slope coefficient, we also +scaled the pixel intensities by another power of two. The main problem +was to determin these factors. Most of the time, they are chosen to +minimize the error induced by the quantization. But in our case, we +also have some hardware constraints, for example the size and depth of +RAMs or the input size of DSPs. Thus, having a maximum of values that +fit in these sizes is a very important criterion to choose the scaling +factors. + +Consequently, we have determined the maximum value of each variable as +a function of the scale factors and the profile size involved in the +algorithm. It gave us the the maximum number of bits necessary to code +them. We have chosen the scale factors so that any variable (except +the covariance) fits in 18 bits, which is the maximum input size of +DSPs. In this way, all multiplications, except one, could be done with +a single DSP, in a single clock cycle. Moreover, assuming that $nb_s = +1024$, all LUTs could fit in the 18Kbits RAMs. Finally, we compared +the double and integer versions of LSQ and found a nearly perfect +agreement between their results. + +As mentionned above, some operations like divisions must be +avoided. But when the denominator is fixed, a division can be replaced +by its multiplication/shift counterpart. This is always the case in +LSQ. For example, assuming that $M$ is fixed, $x_{var}$ is known and +fixed. Thus, $\frac{xy_{covar}}{x_{var}}$ can be replaced by + +\[ (xy_{covar}\times \left \lfloor\frac{2^n}{x_{var}} \right \rfloor) \gg n\] + +where $n$ depends on the desired precision (in our case $n=24$). + +Obviously, multiplications and divisions by a power of two can be +replaced by left or right bit shifts. Finally, the code only contains +shifts, additions, subtractions and multiplications of signed integers, which +are perfectly adapted to FGPAs. + + +We built two versions of VHDL codes, namely one directly by hand +coding and the other with Matlab using the Simulink HDL coder feature~\cite% +{HDLCoder}. Although the approaches are completely different we obtained +quite comparable VHDL codes. Each approach has advantages and drawbacks. +Roughly speaking, hand coding provides beautiful and much better structured +code while Simulind HDL coder produces allows for fast code production. In +terms of throughput and latency, simulations show that the two approaches +yield close results with a slight advantage for hand coding. + +\subsection{Simulation} + +Before experimental tests on the FPGA board, we simulated our two VHDL +codes with GHDL and GTKWave (two free tools with linux). We built a +testbench based on experimental profiles and compared the results to +values given by the SPL algorithm. Both versions lead to correct +results. Our first codes were highly optimized, indeed the pipeline +could compute a new phase each 33 cycles and its latency was equal to +95 cycles. Since the Spartan6 is clocked at 100MHz, estimating the +deflection of 100 cantilevers would take about $% +(95+200\times 33).10=66.95\mu $s, i.e. nearly 15,000 estimations by +second. + +\subsection{Bitstream creation} + +In order to test our code on the SP Vision board, the design was +extended with a component that keeps profiles in RAM, flushes them in +the phase computation component and stores its output in another +RAM. We also added a wishbone, a component that can "drive" signals to +communicate between i.MX and other components. It is mainly used to +start to flush profiles and to retrieve the computed phases in +RAM. Unfortunately, the first designs could not be placed and routed +with ISE on the Spartan6 with a 100MHz clock. The main problems were +encountered with series of arthmetic operations and more especially +with RAM outputs used in DSPs. So, we needed to decompose some parts +of the pipeline, which added few clock cycles. Finally, we obtained a +bitstream that has been successfully tested on the board. + +Its latency is of 112 cycles and computes a new phase every 40 +cycles. For 100 cantilevers, it takes $(112+200\times 40).10=81.12\mu +$s to compute their deflection. It corresponds to about 12300 images +per second, which is largely beyond the camera capacities and the +possibility to extract a new profile from an image every 40 +cycles. Nevertheless, it also largely fits our design goals. + +\label{sec:results} + +\section{Conclusion and perspectives} + +In this paper we have presented a full hardware/software solution for +real-time cantilever deflection computation from interferometry images. +Phases are computed thanks to a new algorithm based on the least square +method. It has been quantized and pipelined to be mapped into a FPGA, the +architecture of our solution. Performances have been analyzed through +simulations and real experiments on a Spartan6 FPGA. The results meet our +initial requirements. In future work, the algorithm quantization will be +better analyzed and an high speed camera will be introduced in the +processing chain so that to process real images. Finally, we will address +real-time filtering and control problems for AFM arrays in dynamic regime. + +\section{Acknowledgments} +We would like to thank A. Meister and M. Favre, from CSEM, for sharing all the +material we used to write this article and for the time they spent to +explain us their approach. + +\bibliographystyle{plain} +\bibliography{biblio} \end{document}