WO2014204528A2 - Method for predicting lifetime of optimal conditioned kdp optics - Google Patents

Abstract

A method for predicting damage performance of a boule is disclosed, wherein a linear absorbing defect is understood to be associated with a potential intrinsic defect that cannot be conditioned with a ns-laser pulse, and wherein a nonlinear absorbing defect is understood to be associated with a potentially extrinsic defect that can be conditioned with a ns-laser pulse. The method may involve obtaining defect absorption parameters for a plurality of first and second different growth areas of the boule. The defect absorption parameters may be used to identify a trend indicating when the defects have increased from the first growth region to the second growth region as the boule has grown. The trend may be used to predict damage performance of the boule.

Description

METHOD FOR PREDICTING LIFETIME OF OPTIMAL CONDITIONED KDP

OPTICS

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the priority to U.S. Provisional

Application No. 61 /781 ,859, filed on March 14, 2013. The entire disclosure of the above application is incorporated herein by reference.

STATEMENT OF GOVERNMENT RIGHTS

[0002] The United States Government has rights in this invention pursuant to Contract No. DE-AC52-07NA27344 between the U.S. Department of Energy and Lawrence Livermore National Security, LLC, for the operation of Lawrence Livermore National Laboratory. FIELD

[0003] The present disclosure relates to methods for predicting the lifetime of an optical material used in a high energy laser system, and more particularly to methods for predicting a lifetime of a conditioned DKDP optic. BACKGROUND

[0004] The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

[0005] Potassium dihydrogen phosphate ("KDP") and it's deuterated analog ("DKDP") crystals are key enabler technologies for high-energy large aperture laser systems such as the National Ignition Facility ("NIF") and the Laser Mega-Joule ("LMJ"). Because of all non-linear materials, only KDP can be grown in boules to the sizes (typically around 700lbs) needed for fusion class lasers. In particular, DKDP has been successfully deployed as a final optic for Third Harmonic Generation ("THG") of 351 nm light. This is due in no small part to its ability to increase its damage threshold by laser conditioning. In this regard it will be appreciated with DKPD, its laser damage threshold increases after exposure to sub-damaging laser fluences. [0006] Furthermore, recent development of absorption distribution model ("ADM") methodology on predicting laser-induced damage and conditioning on DKDP have significantly increased the practicability of managing the lifetime of these optics. ADM was developed to be a self-consistent empirical model for explaining a diverse set of damage related data that includes both damage probability and damage density measurements. ADM assumes that the precursor defects are not homogeneous but made up of two distinct populations of defect clusters, one of which absorbs linearly and the other which absorbs non-linearly.

[0007] ADM allows accurate projection of damage density from damage probability measurements as well as projecting optimal conditioning of the component either as part of the manufacturing process or once installed for use (online). However, this has traditionally still required using a sophisticated laser system to destructively damage test new parts in order to model the damage performance using ADM. Furthermore, it is important to note that damage probability and damage density (ρ(φ)) tests have been traditionally been measured with small beam areas (-0.1 - 1 cm²). So the question remains how valid these tests are when applied to a full size optic (-1200 cm² for the National Ignition Facility (NIF)) in terms of material variability. Previous studies on KDP have shown differences in photoexciation signature but no consistent damage threshold between the different parts of a boule. And all of these studies were based on samples from a single boule.

[0008] A significant advance and benefit would involve the ability to non-destructively model defect population parameters, as well as the online conditioning effect, for a given optic by using ADM to analyze a boule's growth- dependent damage behavior. This could allow one to predict the damage and condition effect of a given DKDP optic without destructively damage testing it. SUMMARY

[0009] In one aspect the present disclosure relates to a method for predicting damage performance of a boule. For the purpose of evaluating the damage performance, it may be understood that a linear absorbing defect is understood to be associated with a potential Type 1 (intrinsic) defect that cannot be conditioned with an ns-laser pulse. A nonlinear absorbing defect may be understood as being associated with a potential Type 2 (extrinsic) defect that can be conditioned with an ns-laser pulse. The method may comprise obtaining defect absorption parameters for a plurality of first and second growth areas of the boule. The defect absorption parameters may be used to identify a trend indicating when at least one of the Type 1 or Type 2 defects has increased from the first growth region to the second growth region as the boule has grown. The trend may be used to predict damage performance of the boule.

[0010] In still another aspect the present disclosure relates to a method for predicting damage performance of a boule, wherein the boule is recognized to have an early growth region and a late growth (LG) region as the boule is formed, and wherein linear defect absorption test data is may be associated with an intrinsic defect and cannot be laser conditioned using an ns-laser pulse and non-linear defect absorption test data may be associated with an extrinsic defect that can be laser conditioned using an ns-laser pulse. The method may comprise obtaining a first group of defect absorption data for the early growth region of the boule. A second group of defect absorption data may be obtained for the late growth (LG) region of the boule. The first and second groups of defect absorption data may be used to identify trend data concerning at least one of: when both linear and non-linear defects are lower in the LG region than the early growth region; and when the linear defects have increased from the early growth region to the LG region as the boule has grown; and when nonlinear defects have decreased from the early growth region to the LG region as the boule has grown. The trend data may be used to predict the damage performance of the boule.

[0011] In still another aspect the present disclosure relates to a method for predicting damage performance of a boule, wherein the boule is recognized to have a first growth (FG) region and a late growth (LG) region as the boule is formed, and wherein linear defect absorption test data is understood to be potentially associated with a Type 1 defect that cannot be laser conditioned with an ns-laser pulse and non-linear defect absorption test data is understood to be potentially associated with a Type 2 defect that can be laser conditioned with an ns-laser pulse. The method may comprise obtaining the defect absorption data by extracting the defect absorption distribution using the damage probability test data (i.e. S/1 and R/1 tests) whereas the S/1 test represent the presence of both linear and nonlinear defects and R/1 test represent the presence of only linear defects (with nonlinear defects being completely conditioned by the laser ramping process). These defect absorption distributions may be represented by the mean and the standard deviation of the absorption distribution. The extracted mean absorption value of the FG and LG boules may be used to identify trends showing increases and decreases in both linear defects and nonlinear defects in the boule between the FG region and the LG region. The trends may be used to predict the damage performance of the boule.

[0012] Further areas of applicability will become apparent from the description provided herein. It should be understood that the description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way.

[0014] Figure 1 a is a graph showing a set of R/1 (ramped fluence single shot) test results and S/1 (single fluence) test results and their corresponding ADM models;

[0015] Figure 1 b is a graph of damage density measurements resulting from different fluences;

[0016] Figure 2a is a simplified diagram indicating the relationship between the two types of defects and how it relates to damage and conditioning fluences; [0017] Figure 2b is a plot showing the total Type 1 and Type 2 defect absorption parameter measurements from a plurality of boules and further indicating both the mean (μ) and the standard deviation (σ) to show the variability of defect populations;

[0018] Figure 3a shows a simplified illustration of different growth regions of a boule;

[0019] Figure 3b shows a plot of the Type 1 linear (i.e., intrinsic) defect absorption parameters versus Type 2 non-linear (i.e., extrinsic) defect absorption parameters for both FG and LG samples;

[0020] Figure 4 is a plot of mean Type 1 defects versus Type 2 defects, for both of the FG and LG regions of each one of a plurality of 14 boules that were analyzed, with the plots falling into three distinct regions that show different trends for the Type 1 and Type 2 defects;

[0021] Figure 5 is a graph showing mean Type 1 defect absorption (u1 ) versus precursor density "N" for Group A boules in relation to precursor density extracted from damage density measurements; and

[0022] Figure 6 is a graph illustrating an optimal conditioning sequence for an optic made from a boule using the information gleaned from the graph of Figure 5.

DETAILED DESCRIPTION

[0023] The present disclosure relates to methods for predicting damage performance of a boule in which laser-induced damage is related to the absorption of precursor defects, and where the precursor defects are not homogenous but made up of (at least) two distinct populations of defect clusters. One distinct population is made absorbs linearly, which may be termed "Type 1 " defects, and the other absorbs nonlinearly and may be termed "Type 2" defects. These precursors can be nano-scale absorbing defect clusters that are potentially transformed into macroscopic micron-scale damage through thermal runaway in bulk material and propagating absorbing fronts. Furthermore, a linearly absorbing defect is hypothesized to be associated with an intrinsic defect and a non-linear absorbing defect is hypothesized to be associated with an extrinsic defect because of its ability to be laser conditioned (i.e. laser modified to become less damaging) with a ns-laser pulse. In one implementation the method may comprise obtaining defect absorption data for a plurality of early and late growth areas of the boule. The defect absorption data may be used to identify trend data indicating when the precursor defects have increased from the first (or early) growth region to the late growth region as the boule has grown. The trend data may be used to predict damage and conditioning performance of the boule.

[0024] Turning now to the development of the methodology of the present disclosure, ADM was used to analyze over 30 different damage probability tests and a dozen different damage density tests from a dozen different DKDP boules in order to assess the defect population variability from boule to boule, as well as from different cuts of the boule. One specific objective was to correlate the statistical variation from different boules or growth regions to changes in growth condition that would illuminate the nature of damage inducing defects and refine growth condition to produce the optimal crystal in terms of damage resistance. Another important objective was to use the relationships or correlations in the statistical variation to help determine if optimal conditioning protocols with limited or no damage performance data could be developed.

[0025] As noted above, ADM assumes that the precursor defects are not homogenous but made up of (at least) two distinct populations of defect clusters, one of which absorbs linearly and the other nonlinearly. The precursor defect varies in absorption (f(a)) since it can be composed of various densities of the individual defect types. In essence, ADM uses the damage probability measurements S/1 (single-fluence multiple-shot test) and R/1 (ramped-fluence single-shot tests) to extract the two population defect types as follows:

where ax is the threshold absorption for a given precursor size, "a", to achieve damage temperature (Θχ) at fluence φ. Each defect type absorption distribution can be assumed to be Gaussian and can be characterized by its mean (μ) and standard deviation (σ) as follows: f_{(a) =} * _e ^~^t (2) /2πσ 2a² '

[0026] With the total absorption a = a1 + a2 (c0+c1 l+...+cnln) where a1 is the Type 1 linear absorption, a2 is the Type 2 nonlinear absorption, I is the laser intensity, and en's are the coefficients of different orders of intensity- dependent nonlinear absorption. As a result, ADM can extract from each set of R/1 measurements the Type 1 defect absorption parameters (μ1 , σ1 ) and from each set of S/1 measurements the Type 2 defect absorption parameters (μ2, σ2). In other words, a total of 4 parameters for each set of damage probability data may be detected, as noted in Figure 1 a. Using ADM, one can also calculate the damage density measurement (ρ(φ)) by using the following equation: f^dmdX p(cp) =n(ct)P((p)dct (3) with amin and amax being the minimum and maximum precursor sizes, respectively. The precursor size distribution n(a) may be given by the formula:

N(b-l)

(4)

Where "N" is the total density of precursor and "b" is the size-dependent power law. Since the coefficients amin and amax are usually set as 50 and 500 nm respectively which are consistent with observed sizes of damage sites and the smallest size suitable for absorbing energy in sufficient density, this implies that ADM extracts at most two parameters (b, N) for each set of ρ(φ) measurement, as can be seen in Figure 1 b.

[0027] It is convenient to plot the extracted defect population parameters for each sample as a scattering plot with the X axis associated with the Type 1 defect absorption parameter and the Y axis associated with the Type 2 defect absorption parameter. This is shown in Figure 2a. In this type of plot, the Type 1 and Type 2 defect absorption parameters can be associated with the various conditioning and damage fluences. For example, a decrease of the Type 2 defect absorption is associated with an increasing conditioning fluence while a decrease of the Type 1 defect absorption is associated with an increased conditioned damage fluence (i.e. damage fluence for a fully-conditioned optic). Since damage fluence is a function of both Type 1 and Type 2 absorption, the trend is contoured downwardly but with a greater dependence on the Type 2 defect absorption because of the non-linear intensity relationship, as indicated by line 2.1 . Plotting all of the over 30 test results from different boules on the same graph, as shown in Figure 2b, shows the variability of the defect populations. Although the mean absorption values for the Type 1 and the Type 2 defect populations are similar (i.e., 19.7 and 19.1 respectively), the Type 2 defect population absorption has roughly a 1 .5 times larger standard deviation. A similar trend also holds for the standard deviation of the Type 1 defect versus the Type 2 defect. There are two potential interpretations for this. One is that there is, in general, a larger variation of defect population in Type 2 defects. This is plausible since Type 2 defects can be conditioned and as a result, can be construed as a potential "extrinsic defect" and as a contamination. A Type 1 defect, however, can be thought of as an "intrinsic defect", such as stoichiometry, and thus there will be fewer variations within the same location.

[0028] The second interpretation is that since a Type 2 defect is extracted from S/1 measurements, which usually only have about 7 data points per data set, and Type 1 is extracted from R/1 measurements which have typically about 30-70 data points, the variation is related to the resolution of the data. However, the spare S/1 measurement has a resolution of only about 14% whereas the R/1 measurement has a probability resolution of at least 3%. This is a substantially larger disparity than the extracted absorption parameters.

[0029] It has been well known that for large boules that have been used to harvest NIF optics, the growth condition and stoichiometry of the solution can change dramatically over the growth period. This can result in several identifiable growth regions that are measurable (i.e., measureable for absorption and scattering). For the DKDP boules used in the NIF, test samples were identified with the four different growth regions: an "early" (i.e., First Growth region ("FG")), Late Growth ("LG"), Cap Growth ("CG"), and Mush Growth ("MG"). These regions are visible in the illustration of Figure 3a. For the present discussion, the focus will be primarily on the test results from both FG (also known as "Prismatic") and LG (also known as "Pyramidal"), as little data was available from the other growth regions. Most of the samples have 1 or 2 damage probability measurements from each boule's growth region; the exception was one particular boule (the "LL16 boule") which had 10 damage probability measurements from its FG growth region and 4 damage probability measurements from its LG growth region.

[0030] Figure 3b shows a plot of the Type 1 vs. Type 2 defect absorption parameters for both the FG and the LG samples from the LL16 boule. Both the FG and the LG absorption parameters are close to the sample mean (of all the boules), indicating that this boule is not an outlier. It is also apparent that the Type 2 parameter (both mean and standard deviation) have a significantly greater variance than the Type 1 parameters. This is in trend with the entire boule sample population. The LG data has less variation than the FG data. It is of interest to note that although the Type 2 defect decreases as the boule transitions from FG to LG growth phases (the LG region is considered to be more pure in most cases), the Type 1 defect actually increases. In order to illustrate the unique relationship of FG versus LG samples for all the boules, Figure 4 shows a plot of the Type 1 versus the Type 2 mean defect absorption parameter for all the boules. At first the plots may seem to be random but careful analysis shows that at least two distinct groupings are present. These groupings are labeled as "A" and "B" in the Figure 4. The arrows indicate the directional trend for each of the Type 1 and Type 2 mean absorption values obtained for each boule. Group A consists of 8 boules that have LG Type 2 mean absorption μ2 (LG) < 19 cm^-1.

[0031] All of these boules have a higher Type 2 absorption mean for FG vs. LG. These boules behave exactly like LL16, which was presented in Fig. 3(b), where an increasing "purity" can be seen as the boule is grown. These boules in general have a better damage performance because of the lower Type 2 absorption mean. Group B consists of 6 boules that have LG Type 2 mean absorption μ2 (LG) > 19 cm^-1. The primary difference of Group B boules in contrast to Group A boules is that all the Group B boules have a lower Type 2 mean absorption value for FG vs. LG. As a result, the Group B boules in general exhibit a decreasing "purity" as the boule is grown. Since the Group B boules in general have a higher Type 2 mean absorption value, these boules also exhibit a poorer damage performance.

[0032] In terms of conditioning or damage performance, the worst boules would be those from Group B, which have the lowest damage threshold even if they are easier to condition because of high Type 1 defect absorptions. The best boules would be those from Group A. Although these have a similar Type 1 defect absorption as the boules of Group B, they boast a lower Type 2 defect absorption that translates to a higher unconditioned damage threshold, as well as the ability to condition at higher fluence (i.e. requiring less fluence ramping).

[0033] Damage density ρ(φ) measurements have been performed on a number of the same boule samples over the years at Lawrence Livermore National Laboratory ("LLNL") for pulse-width dependence and conditioning studies. But while boule identification has always been recorded, the growth region of each sample was not. ADM analysis using equation 3 herein was used to extract the precursor size distribution parameters (b, N) from each ρ(φ) measurement. As there can be substantial difference of Type 1 and Type 2 defect absorption parameters from different growth regions (e.g., FG vs. LG), of all eleven damage density ρ(φ) measurements from seven different boules, only one growth region absorption parameter was able to return a valid parameter. This is an important revelation that shows the self-consistency of the ADM methodology and its ability to discriminate erroneous data. Furthermore, it was found that size-dependent power law was invariant for all of the data, b ~ 3 and only the precursor density N varies from sample to sample. At first glance, there doesn't seem to be any relationship between the precursor density N and the defect absorption parameters but when the data was grouped according to the results from Figure 4 a trend emerges. Figure 5 plots the Type 1 mean absorption vs. the precursor density N for all eleven damage density ρ(φ) measurements. At least two exponential dependences emerged in Figure 5 between the Type 1 mean absorption μ1 and the precursor density N. The first dependence (line 5.1 , N (μ) = 7x10⁴-e-0.19 μ1 ) centers around data from three Group A boules, and another boule (denoted by triangle labeled 5.2) that was not able to be classified because it only had damage probability data from the FG growth region. This unclassified boule had FG mean absorption values of 22.7 and 20.6 (μ1 , μ2), so its placement in either Group A or Group B is not determinable without associated LG data. However, it would appear that this unclassified boule would need to belong to Group A if the trend is consistent.

[0034] The second dependence (dashed line 5.3, N (μ) = 8x10³-e-0.19 μ1 ) centers around data from two boules (2 data per boule) from Group B. Although this trend line fits well with the data, its validity is less concrete as all four data points have somewhat similar values.

[0035] An optimal conditioning protocol can be calculated based on the results of this study. For example, if a THG optic without any damage data from boule samples was required to be conditioned to operate at 7 J/cm² at 3ω using a 5ns Fiat-In-Time (FIT) pulse, then one may sequentially calculate the maximal allowable fluence before damage using the previous shot fluence as a conditioning fluence. For simplicity, we will assume that the laser fluences are uniform and that no initiations are allowed in any of the conditioning sequences with an optic area of 1000cm². Figure 6 shows the optimal conditioning sequence for two different assumptions for the boules. One boule is assumed to be of average quality from Group A (square labeled 6.1 ) with (μ1 =19.7, μ2=19.1 ). The other boule is assumed to be the worst boule (circle labeled 6.2) possible (μ1 =23.8, μ2=44.3) from Group B, having the highest Type 2 defect in all of the boules studied (Figure 4). The precursor density N that is used to calculate the damage density is derived using the trend lines from Figure 5. Figure 6 clearly shows that an average boule will require only two shots to be able at operate safely at 7 J/cm², while the worst boule would need at least three shots to just barely operate safely. This is a simple illustration to demonstrate the potential of calculating optimal conditioning protocols. Employing this on an actual laser system will require a more complicated model that can take into account beam spatial and temporal contrast [MaxN]. However, it is also worth noting that optimizations can also be made if damage inspections are fed back into the model to adjust defect parameters. [0036] Accordingly, the above methodology shows how ADM may be used to investigate variations of defect population from boule-to-boule, as well as boules from different growth regions. Variation of the defect population of both types was found from boule-to-boule but there are at least two distinct groupings of the boules that are predicated on the defect absorption parameter of the two primary growth regions (i.e. regions FG and LG). This grouping is also important in determining the exponential relationship between the Type 1 absorption and the total defect precursor density. Understanding this grouping will help to potentially refine growth conditions to produce more damage-resistant boules as well as to help formulate optimal conditioning protocols that can significantly reduce shot time while avoiding damage to an optic.

[0037] While various embodiments have been described, those skilled in the art will recognize modifications or variations which might be made without departing from the present disclosure. The examples illustrate the various embodiments and are not intended to limit the present disclosure. Therefore, the description and claims should be interpreted liberally with only such limitation as is necessary in view of the pertinent prior art.

Claims

CLAIMS What is claimed is:

1 . A method for predicting damage performance of a boule, wherein a linear absorbing defect is understood to be associated with a potential Type 1

(intrinsic) defect that cannot be conditioned with a ns-laser pulse, and wherein a nonlinear absorbing defect is understood to be associated with a potential Type 2 (extrinsic) defect that can be conditioned with a ns-laser pulse, the method comprising:

obtaining defect absorption parameters for a plurality of first and second different growth areas of the boule;

using the defect absorption parameters to identify a trend indicating when the at least one of the Type 1 or Type 2 defects has increased from the first growth region to the second growth region as the boule has grown; and

using the trend to predict damage performance of the boule.

2. The method of claim 1 , wherein the second growth region comprises a Late Growth (LG) region of the boule.

3. The method of claim 2, wherein obtaining the defect absorption parameters comprises:

obtaining a first group of defect absorption parameters for the first growth region (FG) of the boule, and wherein the first growth region forms an early growth region; and

obtaining a second group of defect absorption parameters for the late growth (LG) region of the boule.

4. The method of claim 1 , further comprising using the defect absorption parameters to identify at least one of:

when both Type 1 and Type 2 defects are lower in the second growth region than the first growth region; and

when both defects are greater in the first growth region than the second growth region.

5. The method of claim 3, wherein obtaining the first group of defect absorption parameters comprises obtaining both linear and non-linear defect absorption parameters.

6. The method of claim 3, wherein obtaining the second group of defect absorption parameters comprises obtaining both linear and non-linear defect absorption parameters.

7. The method of claim 3, wherein obtaining the first and second groups of defect absorption parameters comprises obtaining both linear and nonlinear defect absorption parameters for each of the first growth (FG) and the late growth (LG) regions of the boule.

8. The method of claim 3, wherein:

obtaining the first group of defect absorption parameters comprises obtaining a mean value of a plurality of both linear and non-linear defect absorption distributions; and

obtaining the second group of defect absorption parameter comprises obtaining the mean value of a plurality of both linear and non-linear defect absorption distributions.

9. The method of claim 8, further comprising:

obtaining a mean value for all linear defect absorption parameters;

obtaining a plurality of damage density measurements for the boule;

from the damage density measurements, extracting a plurality of precursor density values; and

using the relationship between the precursor density values and the linear defect absorption parameters to determine an optimal conditioning protocol to be used in conditioning an optic made from the boule.

10. A method for predicting damage performance of a boule, wherein the boule is recognized to have an early first growth region and a late growth (LG) region as the boule is formed, and wherein linear defect absorption test parameters are understood to be potentially associated with a Type 1 (intrinsic) defect that is not able to be conditioned with an ns laser pulse and non-linear defect absorption test parameters are understood to be potentially associated with a Type 2 (extrinsic) defect which is able to be conditioned with the ns laser pulse, the method comprising:

obtaining a first group of defect absorption parameters for the early growth region of the boule;

obtaining a second group of defect absorption parameters for the late growth (LG) region of the boule; and

using the first and second groups of defect absorption parameters to identify a trend concerning at least one of:

when both linear and non-linear defects are lower in the LG region than the early growth region; and

when the linear defects have increased from the early growth region to the LG region as the boule has grown, and the non-linear defects have decreased from the early growth region to the LG region as the boule has grown; and

using the trend to predict the damage performance of the boule.

1 1 . The method of claim 10, further comprising determining when both the linear and the non-linear defects are greater in the early growth region than the LG region.

12. The method of claim 1 1 , wherein the first group of defect absorption parameters includes a mean value of a plurality of collected linear defect absorption parameters and non-linear defect absorption parameters.

13. The method of claim 10, wherein the second group of defect absorption parameters includes a mean value of a plurality of collected linear and non-linear collected defect absorption parameters.

14. The method of claim 10, further comprising obtaining a mean value for all linear defect absorption parameters;

obtaining a plurality of damage density measurements for the boule;

from the damage density measurements, extracting a plurality of precursor density values; and

using the relationship between the precursor density values and the linear defect absorption parameter data to determine a relationship there between that is used for indicating optimal conditioning protocol to be used in conditioning an optic made from the boule.

15. A method for predicting damage performance of a boule, wherein the boule is recognized to have a first growth (FG) region and a late growth (LG) region as the boule is formed, and wherein linear defect absorption test data is understood to be potentially associated with a Type 1 (intrinsic) defect that cannot be conditioned with an ns laser pulse, and wherein the non-linear defect absorption test data is understood to be potentially associated with a Type 2 (extrinsic) defect, the method comprising:

obtaining the defect absorption data by extracting a defect absorption distribution using S/1 and R/1 damage probability test data, wherein:

the S/1 damage probability test data represents the presence of both linear and nonlinear defects; and

the R/1 damage probability test data represents the presence of only linear defects, and wherein nonlinear defects are completely conditioned by the laser ramping process;

representing the defect absorption distribution by extracting a mean absorption value and a standard deviation of the defect absorption distribution; using the extracted mean absorption value of the FG and LG regions of the boule to identify trends showing increases and decreases in both linear defects and nonlinear defects in the boule between the FG region and the LG region; and

using the trends to predict the damage performance of the boule.

16. The method of claim 15, wherein using the trends further comprises:

identifying when both the Type 1 defects and the Type 2 defects are lower in the LG region than the FG region; and

identifying when the Type 1 defects have increased from the FG region to the LG region as the boule has grown, and Type 2 defects have decreased from the FG region to the LG region as the boule has grown.

17. The method of claim 15, wherein obtaining a mean absorption comprises:

obtaining a plurality of damage density measurements for the boule;

from the damage density measurements, extracting a plurality of precursor density values; and

using the relationship between the precursor density values and the linear defect absorption absorption data to determine an a relationship there between that is used for indicating optimal conditioning protocol to be used in conditioning an optic made from the boule.