ion implant data log analysis for process control and fault detection

7/30/2019 Ion Implant Data Log Analysis for Process Control and Fault Detection

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Ion Implant Data Log Analysis for Process Control

and Fault Detection

Michael J. Rendon, David C. Sing, Marcy Beard, Michael Hartig, John C. Arnold

Motorola Technology and ManufacturingDan Noble Center

Austin, TX 78721 US

Abstract Data mining techniques have been introduced to the

semiconductor industry in recent years. In many applications

real time process data from plasma etch or deposition tools is

collected and powerful statistical methods are used to detect

trends and correlations among variables; the results of which can

be used for process control and fault detection. The introduction

of such methods to ion implant process control has been slow,

partly due to the large number (from hundreds to thousands) of

process recipes which can be run on a given tool, and the relative

lack of real time process signals which are available during atypical ion implant process. However, valuable run summary

data is available on virtually every ion implant tool in the form of

implant data logs (IDLs). The introduction of modern computer

networking technology makes it possible to automatically collect

data from IDLs on multiple implant tools and store them in a

database. Once the database is formed a multitude of analysis

tools can be applied for process control, fault detection, and

ultimately scrap reduction and yield enhancement. Traditional

data mining techniques such as principle component analysis can

be applied to seek out correlations among the many different

signals available. However, the precise nature of the ion implant

process allows some variables to be analyzed in a more

deterministic manner. For example, a precise and predictable

functional relationship is expected to be present between analyzer

magnet current and the implant species charge, mass, andenergy. A model can be generated to predict the magnet current

and the value in the IDL can be compared against it. The

deviations between model predictions and actual implant

parameters can be used as the input to a response system which,

given the nature of the signal and the size and statistical

significance of the deviation, would generate the appropriate

response, if any. The level of the response could vary from an

email notification to an engineer (for example, the detection of

recipe with a bad source setup history which could be improved

to reduce autotune times) to the automatic shutdown of the tool

(if the wrong species was determined to have been implanted).

Keywords-Ion Implantation; Automatic Process Control; Mass

Analysis; Charge Control)

I. INTRODUCTION

In the past decade tool manufacturers have improved uponthe quality and quantity of summary statistics files available onion implant tools. This trend has been facilitated by theincrease in processing and storage capability of the PC andUNIX based workstations that have been coupled to the toolsautomation systems. Several implant tool suppliers offer SPCpackages that provide the capability to review summary

statistics of a user selectable recipe or set of recipes. However,the data export capability of the on-board SPC function is oftenlimited to transferring a single variable at a time to a 3.5-inchfloppy disk, and the system cannot be easily accessed remotely.

In this paper we report on progress in developing a networkbased implant data log (IDL) data analysis system which canbe used to access data from multiple tools and multiple recipes.The system that is currently under development can be used togenerate individual control charts from any of over 100 processvariables for a user selectable process recipe or from allimplants in the database. Multi-variable models are beingdeveloped to compare relationships among process variables.These models calculate predicted values of process variables,and the differences between the model and the actual variablesare used as indicators of process drift, hardware malfunctions,recipe integrity, and in some cases mis-processing.

II. GLOBAL PROCESS MODELS

A common problem an implant process engineer is facedwith is that a given implant tool or toolset utilizes a very largenumber of process recipes. Even within a tool type, forexample high current implanters, doses can range over fourorders of magnitude (10

12

to 1016

ions/cm2

), energies can rangeover nearly two orders of magnitude (3 to 160 keV), and ionmasses can vary over one order of magnitude (11 AMU forBoron to 121 AMU for Antimony). Control charts for a singleprocess variable for a single recipe can be useful for detectingtool faults and process drifts, but faced with hundreds ofprocess recipes and over 100 variables for each recipe toreview, a process engineer can easily be overwhelmed by thecrush of data, especially since each tool often produces over

100 IDL files each day. Multi-variable process models whichcan be applied to all the IDL files regardless of the specificprocess recipe, or at least a large subset of available processrecipe, provide the basis for a more powerful process controlsystem. Multi-variable process models for analyzer magnetcurrent, secondary electron flood gun current, and source arccurrent are presented in the following section.


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III. EXAMPLES OF GLOBAL PROCESS VARIABLE MODELS

A. Analyzer Magnet Universal Curve

The heart of virtually all ion implant tools is the massanalysis magnet. The fundamental equation which governs the

operation of the magnet is the well known relationship betweenthe charge Q, mass M, and energy E of an ion and its radius ofcurvature R as it moves in magnetic field of strength B:

R*B = sqrt ( 2*M*E / Q2

) (1)

This equation can be re-written in terms of the analyzermagnet current I and the extraction voltage V as:

I = K * sqrt ( M*V / Q) (2)

where K is a constant which contains the geometry factors(radius R) and other fixed factors. The quantity sqrt ( M*V / Q)is referred to as the magnetic stiffness of the beam. In reality,the relationship between magnet current and magnetic stiffnessbecomes non-linear at high magnetic fields due to saturation ofthe magnet core. However, a plot of analyzer magnet currentversus magnetic stiffness will form a smooth curve for implantsof all species, charge states, beam currents, and extractionvoltages for a properly calibrated analyzer magnet. A similarmodel can also be constructed for other magnets which areused to steer the beam, for example, the final energy magnet ofmany high energy implanters.

Figure 1 shows the analyzer magnet current vs stiffness plotfor four high current tools installed at Motorolas MOS13facility. Data is plotted for every implant from June 1 to July31, 2002 for three tools, and from November 2001 to January2002 for a fourth. Over 20,000 implants are represented in thisplot. The November to January dataset includes a case wherean AMU calibration error caused a scrap incident. The bad

implants are clearly separated above the bulk of the data, andare marked with an X. Figure 2 shows the control chart forthe November to January data, where the relative error betweenthe actual magnet current and a fourth order polynomial fit tothe magnet curve data is plotted. The bad implants are againeasily identified as flyers well separated from the bulk of thedata.

B. Secondary Electron Flood (SEF) Primary Current

The high current implant tools employ an SEF chargecontrol system, the operating parameters are set by the recipevalues. The standard recipe setting of the SEF primary current

is ten times the ion beam current. Occasionally, when a beamcurrent in a recipe is changed, or a recipe is copied fromanother recipe, the SEF current will be incorrectly set. A globalcheck of all implants is implemented by calculating the ratio ofthe beam current to plasma flood from the IDL files, This ratioshould be near 10 for all implants. Figure 3 shows the ratio ofPrimary SEF current to Beam current for one tool (#417) fortwo months of implants. This control chart can then be used toflag IDL files for a follow up to check to see if the recipe calledfor the correct SEF primary current or if the SEF system wasoperating normally.

Figure 1. Analyzer Magnet Universal Curve (data from 4 tools

overlayed).X marks fliers due to AMU calibration error.

Figure 2. Magnet Curve Control Chart (data from 1 tool). X

marks fliers due to AMU calibration error.


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C. Species Specific Arc Current Models

Arc current is the single best indication of good source andextraction performance. A poorly built source or poorly alignedextraction assembly often results in excessive arc currentrequired to produce a given beam. Poor source or extractionperformance can be identified by comparing the actual arccurrent against the arc current predicted by a model. A simplemodel was constructed for As+ arc current (I

arc) using a power

law model:

Iarc

= A * (Inorm

)B

(3)

In (3) normalized beam current (Inorm

) was calculated using theaverage beam current from the IDL file divided by the

maximum beam current. The maximum beam current is

calculated by linear interpolation the manufacturers table ofmaximum spec beam currents as a function of energy. Figure 4shows the predicted arc current vs the actual arc current for asingle tool (#417). A good correlation is observed, with acorrelation coefficient of greater than 0.9. A control chart of therelative error between the actual arc current and the predictedarc current is shown in Figure 5. The data shows this toolgenerally sets up within 20% of the predicted value, and theflyers can then be analyzed to identify recipes with excessivearc current that may require retuning.

IV. A PROTOTYPE IDL ANALYSIS SYSTEM

As was mentioned earlier, it is increasingly common forimplanters and many other process tools to have dozens orhundreds of different recipes active at any given time. Evenwhen the number of recipes is smaller, other factors such aswafer pattern density, film thickness, etc. can cause a giventool to have many distinct sets of operating conditions. Thisproliferation of states presents a new challenge for traditionalfault detection methods, which typically demand that the toolowner either explicitly set limits for each parameter or

manually classify runs as good or bad for the purposes oftraining a statistical model. As the number of normaloperating states increases, the burden associated withestablishing and maintaining limits becomes unmanageable.

To mitigate this effect, a different approach was employedin this work. Rather than attempting to automaticallydisposition each run as good or bad, the analytical systemwas designed to flag anomalous runs for further engineeringattention. Although this approach theoretically leaves moreopportunity for wafer scrap, particularly if the cycle time forengineering response is long, the much lower cost in up fronteffort makes it more attractive than the conventional methods.

An analysis program was written using a commerciallyavailable software package to demonstrate the approach. A new

Figure 4. As+ arc current model results (data from 1 tool). Figure 5. As+ arc current control chart (data from 1 tool).

Figure 3. Ratio of primary flood gun current to beam current (data from

1 tool)


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type of chart was created, with limits automatically added assoon as any data is entered. The limits are based on the meanand standard deviation of the data up to that point, and theychange with each new point entered. Initially all points areincluded in the limits calculations. After a user-configurableignore time any point outside the limits will be considered ananomalous run (a flyer) and will not be included insubsequent calculations. In addition, a cap is placed on thenumber of points used in the limits calculation so the datasetrolls as more points are added to the chart..

Models have been created within the program for theanalyzer magnet, plasma flood gun, and arc current curves.After a certain number of points are entered, a model isautomatically generated and residuals are calculated against it.The residual values are plotted and limits are set up asdescribed above in order to flag points that land a significantdistance from the model curve. Figures 1 through 5 areexamples of the models and control charts created using thisalgorithm.

The initial version of the system is interactive and requiresuser participation. Design of an automated system is underway,with a goal of automatically collecting the data, processing itthrough the analysis routines, and reporting on all flyer points[1]. A graphical user interface will be included for viewingcharts and updating user input parameters such as how manypoints are needed before making a model. Default values forthese parameters will allow the system to run without usersetup, but the engineer may wish to modify the parameters for aparticular variable to allow for known trends or different levelsof variation. System tweaks will be required to limit reportingof false flyers while still capturing real problems, but it isexpected that the time spent doing so will be much less thansetting up user-defined limits in the first place.

REFERENCES

[1] M. Rendon, D. C. Sing, Local Area Network LAN AddressManufacturing and Development Implant Tool Issues, International IonImplant Technology Conference 2002, unpublished.

ion implant data log analysis for process control and fault detection

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