A machine learning-based all-solid-state laser thermal compensation method and device, and a laser

Abstract

The application discloses a kind of machine learning-based all-solid-state laser thermal compensation method, device and laser, wherein the method comprises: according to the parameters of the working substance of the all-solid-state laser to be compensated and the pump light power, the lens model type is selected, the lens model type is: thin lens model or thick lens model;Thermal compensation is carried out on the all-solid-state laser using the thin lens model or the thick lens model and negative lens.The thick lens model constructed simultaneously considers the influence of temperature gradient, thermal stress and end face deformation on multi-section bonded laser crystal, can more accurately simulate the thermal lens effect of multi-section doped crystal, so as to improve the performance of all-solid-state laser.

Description

Technical Field

[0001] The present invention relates to the field of laser thermal compensation, and in particular to a full-solid-state laser thermal compensation method, device and laser based on machine learning. Background Art

[0002] 2μm lasers are widely used in laser ranging, laser processing, and laser medical treatment due to their strong penetrating power, easy absorption by water molecules, and eye safety. However, due to the thermal lens effect, excessive heat accumulation in the laser medium can lead to uneven temperature distribution within the laser crystal, forming a thermal gradient. This not only causes thermal expansion and thermal distortion of the crystal, but also leads to non-uniform changes in the refractive index, which in turn causes a decrease in laser beam quality. Furthermore, excessive heat can cause stress concentration on the crystal surface, increasing the risk of crystal fracture and shortening the laser's service life.

[0003] Currently, researchers are mitigating the thermal effects of 2μm all-solid-state lasers primarily through pumping methods, crystal structures, and cooling techniques. For example, when thermally compensating all-solid-state lasers using crystals with a single or multiple doping concentrations, a thermal lens model is used to achieve thermal compensation within the full pump power range.

[0004] However, the same thermal lens model can produce significantly different thermal compensation amounts for bonded crystals or single-doped crystals in different pump power ranges. Therefore, a flexible and efficient all-solid-state laser thermal compensation method is needed to improve the output performance of 2μm lasers. Summary of the Invention

[0005] The present invention aims to provide a method, device, and laser for thermal compensation of an all-solid-state laser based on machine learning, which can solve at least one of the above-mentioned technical problems. The specific solution is as follows:

[0006] According to a specific embodiment disclosed in the present invention, a first aspect of the present invention provides a method for thermal compensation of an all-solid-state laser based on machine learning, comprising: selecting a lens model type according to parameters of a working material and pump light power of the all-solid-state laser to be compensated, wherein the lens model type is a thin lens model or a thick lens model;

[0007] Performing thermal compensation on the all-solid-state laser using the thin lens model or the thick lens model and a negative lens;

[0008] The expression of the thick lens model is:

[0009]

[0010] The expression of the thin lens model is:

[0011]

[0012] Where, ωp represents the spot radius of the pump light; Pin represents the pump light power;

[0013] k c represents thermal conductivity; l c represents the crystal length;

[0014] n0 represents the refractive index of the crystal; b represents the radius of the crystal;

[0015] ζ represents the thermal load ratio of the crystal;

[0016] α T represents the absorption coefficient; C r,φ represents the photoelastic coefficient;

[0017] dn / dt represents the thermo-optical coefficient.

[0018] Preferably, the lens model type is selected according to the parameters of the working material of the all-solid-state laser to be compensated and the pump light power, including:

[0019] Constructing a neural network screening model, inputting the parameters of the working substance and the pump light power into the neural network screening model to obtain the lens model type;

[0020] The working material includes: a crystal with a single doping concentration or a multi-segment bonded laser crystal;

[0021] The parameters of the working material include: the doping concentration of the laser crystal and the corresponding laser crystal segment length.

[0022] Preferably, the neural network screening model is based on a back propagation neural network to create a multilayer perceptron regression model;

[0023] The parameters of the input layer of the neural network screening model include: a thick lens model simulation result obtained using the thick lens model, a thin lens model simulation result obtained using the thin lens model, a measured thermal focal length value, parameters of the working material, and a pump light power value.

[0024] Preferably, when the all-solid-state laser is thermally compensated using a thin lens model and a negative lens, the position distance of the negative lens in the resonant cavity of the all-solid-state laser satisfies:

[0025]

[0026] Wherein, L1 is the distance from the front end surface of the working material to the total reflection mirror;

[0027] L2 is the distance from the rear end face of the working material to the output mirror;

[0028] R2 is the curvature radius of the output mirror.

[0029] Preferably, when the all-solid-state laser is thermally compensated using a thick lens model and a negative lens, the position distance of the negative lens in the resonant cavity of the all-solid-state laser satisfies:

[0030]

[0031]

[0032] Wherein, L1 is the distance from the front end surface of the working material to the total reflection mirror;

[0033] L2 is the distance from the rear end face of the working material to the output mirror;

[0034] R2 is the curvature radius of the output mirror.

[0035] Preferably, the focal length of the negative lens ranges from -5 mm to -150 mm.

[0036] Preferably, the working material is: multi-segment bonded Tm:YAG laser crystal; the first segment is undoped Tm 3+ , length is 3mm; the second segment Tm 3+ The doping concentration is 2at.%, and the length is 3mm; the third section Tm 3+ The doping concentration is 3.5at.%, and the length is 5mm.

[0037] Preferably, the distance between the front end surface of the working material and the total reflection mirror ranges from 0 to 55 mm;

[0038] The distance between the rear end surface of the working material and the negative lens ranges from 0 to 150 mm;

[0039] The distance between the negative lens and the output mirror ranges from 0 to 210 mm.

[0040] According to the specific embodiments disclosed in the present invention, the second aspect of the present invention discloses a machine learning-based all-solid-state laser thermal compensation device for implementing the above method, comprising:

[0041] A lens model selection unit, configured to select a lens model type according to parameters of a working material of the all-solid-state laser to be compensated and pump light power, wherein the lens model type is a thin lens model or a thick lens model;

[0042] A thermal compensation calculation unit is configured to perform thermal compensation on the all-solid-state laser by utilizing the thin lens model or the thick lens model and a negative lens.

[0043] According to the specific embodiments disclosed in the present invention, the third aspect of the present invention discloses an all-solid-state laser based on machine learning, including the above-mentioned all-solid-state laser thermal compensation device based on machine learning.

[0044] Compared with the prior art, the above solution disclosed in the present invention has at least the following beneficial effects:

[0045] The present invention simulates the thermal lensing effect of laser crystals under different doping conditions by constructing thin and thick lens models. A neural network screening model is then constructed to generate a lens model type that matches the doped crystal to be compensated based on the input pump power value. This model selects either a thin or thick lens model for thermal compensation, and further utilizes a negative lens to thermally compensate the laser. The thick lens model constructed by the present invention simultaneously accounts for the effects of temperature gradients, thermal stress, and end-face deformation on multi-segment bonded laser crystals, enabling more accurate simulation of the thermal lensing effect of multi-segment doped crystals, thereby improving the performance of all-solid-state lasers. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The accompanying drawings are incorporated into and constitute a part of the specification, illustrating embodiments consistent with the present disclosure and, together with the specification, explaining the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort. In the drawings:

[0047] Figure 1 This is a flow chart of a method for thermal compensation of an all-solid-state laser based on machine learning according to the present invention;

[0048] Figure 2a This is a temperature distribution diagram of a Tm:YAG crystal with a single doping concentration according to the present invention;

[0049] Figure 2b This is a thermal stress distribution diagram of a Tm:YAG crystal with a single doping concentration according to the present invention;

[0050] Figure 3a This is the temperature distribution diagram of the multi-segment bonded Tm:YAG crystal of the present invention;

[0051] Figure 3b This is a one-dimensional temperature curve distribution diagram of the multi-segment bonded Tm:YAG crystal of the present invention;

[0052] Figure 3c This is a thermal stress distribution diagram of the multi-stage bonded Tm:YAG crystal of the present invention;

[0053] Figure 4 is a schematic diagram of the neural network screening model of the present invention;

[0054] Figure 5 This is a schematic diagram of the principle of the present invention for collecting a neural network screening model data set;

[0055] Figure 6a This is a simulation diagram of the stable region of the Tm:YAG laser resonator using a thin lens model;

[0056] Figure 6b This is a simulation diagram of the stable region of the Tm:YAG laser resonator using the thick lens model of the present invention;

[0057] Figure 7a 、 Figure 7b 、 Figure 7c Schematic diagram of thermal compensation effect under different lens models of the present invention;

[0058] Figure 8 Schematic diagram of the thermal compensation effect of different focal lengths and different positions of the negative lens inserted into the cavity of the present invention;

[0059] Figure 9 This is a structural schematic diagram of an all-solid-state laser thermal compensation device based on machine learning of the present invention. DETAILED DESCRIPTION

[0060] To make the objectives, technical solutions, and advantages disclosed in the present invention more clearly apparent, the present invention will be further described in detail below with reference to the accompanying drawings. It is apparent that the embodiments described are only some of the embodiments disclosed in the present invention, rather than all of them. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments disclosed in the present invention without inventive effort shall fall within the scope of protection disclosed in the present invention.

[0061] The terms used in the embodiments disclosed herein are for the purpose of describing specific embodiments only and are not intended to limit the present disclosure. The singular forms "a," "an," "the," and "the" used in the embodiments disclosed herein and the appended claims are also intended to include the plural forms, and "a plurality" generally includes at least two, unless the context clearly indicates otherwise.

[0062] It should be understood that although the terms first, second, third, etc. may be used to describe the embodiments of the present invention, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, the first can also be referred to as the second, and similarly, the second can also be referred to as the first without departing from the scope of the embodiments of the present invention.

[0063] It should also be noted that the terms "include," "comprises," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a product or device comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such product or device. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the product or device comprising the element.

[0064] The optional embodiments disclosed in the present invention are described in detail below with reference to the accompanying drawings.

[0065] In end-pumped solid-state laser systems, the released thermal energy accumulates within the laser medium, causing temperature gradients, thermal stress, and end-face deformation. These factors collectively affect the thermal focal length characteristics of the gain medium. Typically, to simplify complex calculations, the laser crystal is simplified into a thin lens model, focusing on the thermal effects of temperature gradients. However, the influence of factors such as the gain medium's substrate material, doping concentration, structural characteristics, and pumping method on thermal effects under different conditions cannot be ignored. Especially for commonly used bonded crystals, using a thin lens model for analysis can lead to large simulation errors.

[0066] Therefore, the first embodiment of the present invention provides a method for thermal compensation of an all-solid-state laser based on machine learning, comprising the following steps:

[0067] Step S1: selecting a lens model type according to the parameters of the working material of the all-solid-state laser to be compensated and the pump light power, wherein the lens model type is a thin lens model or a thick lens model.

[0068] Step S2: thermally compensate the all-solid-state laser using the thin lens model or the thick lens model and a negative lens.

[0069] This embodiment constructs a thick lens model to simultaneously take into account the effects of temperature gradient, thermal stress, and end face deformation on multi-segment bonded laser crystals, thereby more accurately simulating the thermal lens effect of multi-segment doped crystals.

[0070] Specifically, in step S1, the thin lens model and thick lens model constructed by the present invention are further explained by taking the working materials of the all-solid-state laser as single-doped Tm:YAG crystal and multi-segment bonded Tm:YAG crystal as examples.

[0071] like Figure 2a and Figure 2bFigure 2 shows the temperature and thermal stress distribution of a Tm:YAG crystal with a single doping concentration. It can be seen that the temperature and deformation within the crystal are primarily concentrated in a small area at the very front of the laser crystal, resulting in a large temperature difference across the entire laser crystal, making the thin lens model suitable.

[0072] The expression for constructing the thin lens model is:

[0073]

[0074] Where, ωp represents the spot radius of the pump light; Pin represents the pump light power;

[0075] k c represents thermal conductivity; l c represents the crystal length;

[0076] n0 represents the refractive index of the crystal; b represents the radius of the crystal;

[0077] ζ represents the thermal load ratio of the crystal;

[0078] α T Indicates: absorption coefficient; C r,φ represents the photoelastic coefficient; dn / dt represents the thermo-optical coefficient.

[0079] Figure 3a 、 Figure 3b and Figure 3c The temperature distribution, one-dimensional temperature curve distribution, and thermal stress distribution of the multi-section bonded Tm:YAG crystal 3 (0at.%) + 3 (2at.%) + 5 (3.5at.%) are shown. It can be seen that the temperature at the beginning of the laser crystal bonding surface has reached 310.11K. After a section that is more severely heated, the temperature at the second bonding surface can still reach 313.17K. Figure 3c It can also be seen that the portion of the laser crystal affected by thermal stress accounts for a large portion of the total crystal length. However, compared to the single doping concentration Tm:YAG crystal, the bonded crystal has a larger overall temperature drop due to the effect of homogenized absorption, which to some extent solves some of the thermal effects. Therefore, in order to better reflect the thermal lens effect, based on the thin lens model, the present invention constructs a more complex thick lens model, which comprehensively considers the temperature gradient f T 、thermal stress S and end face deformation f D The impact on laser crystals enables more accurate simulation and analysis of the behavior of thermal effects.

[0080] Specifically, the laser crystal absorbs energy during the pumping process, resulting in uneven temperature distribution within the crystal and forming a radial temperature gradient f T , causing the beam to converge or diverge.

[0081] The non-uniformity of the crystal's thermal expansion will generate thermal stress inside the crystal. S Thermal stress causes stress birefringence effect inside the crystal, which changes the propagation path of light and produces thermal focus.

[0082] Thermal load causes mechanical deformation of the laser crystal end face, namely, the end face deformation f D The deformed end surface acts as a geometric lens for the laser beam, changing the convergence or divergence characteristics of the beam.

[0083] Therefore, the expression for constructing the thick lens model is:

[0084]

[0085] Where, ωp represents the spot radius of the pump light; Pin represents the pump light power;

[0086] k c represents thermal conductivity; l c represents the crystal length;

[0087] n0 represents the refractive index of the crystal; b represents the radius of the crystal;

[0088] ζ represents the thermal load ratio of the crystal;

[0089] α T Indicates: absorption coefficient; C r,φ represents the photoelastic coefficient;

[0090] dn / dt represents the thermo-optical coefficient.

[0091] Furthermore, when simulating the thermal focal length of a laser crystal using the constructed thin and thick lens models, there are differences in the accuracy of the simulated thermal focal length within the same pump light power range. Because the characteristics of a laser crystal change when irradiated with pump light, a matching thin or thick lens model should be used for different pump light power ranges.

[0092] In the initial stage, when the pump light irradiates the laser crystal, heat conduction is not significant, and part of the heat energy is absorbed by the matrix without doped ions, resulting in little difference in the overall temperature of the laser crystal. The end face deformation is relatively small compared to the overall shape change of the laser crystal, making it suitable to use a thick lens model.

[0093] As the pump power gradually increases, the rate at which the doped ions absorb photons accelerates, causing the temperature inside the laser crystal to rise rapidly. The heated lens effect on the crystal bonding surface is significant, and there is a significant difference in temperature from the back end of the laser crystal. The deformation also begins to increase, and the thin lens model is more suitable at this time.

[0094] As the pump power increases, the temperature of the laser crystal's bonding surface reaches a certain level, and heat begins to transfer backward. Simultaneously, the deformation caused by thermal stress gradually intensifies, making the thick lens model suitable for simulation.

[0095] Therefore, in a preferred embodiment of the present invention, a neural network screening model is established, the parameters of the laser crystal and the pump light power are input into the neural network screening model, and the matching lens model type is obtained using the neural network screening model.

[0096] Specifically, the neural network screening model is based on the back propagation neural network to create a multi-layer perceptron regression model. Figure 4 As shown, the parameters of the input layer of the neural network screening model include: the simulation results of the thick lens model obtained using the thick lens model, the simulation results of the thin lens model obtained using the thin lens model, the measured thermal focal length value, the parameters of the working material and the pump light power value.

[0097] The number of neurons in the hidden layer of the neural network screening model is set to (20, 20, 20), the maximum number of iterations is set to 1000, and the learning rate is 0.01.

[0098] The output layer of the neural network screening model includes: thin lens model, thick lens model and actual thermal focal length.

[0099] During model training, the model calculates predicted values ​​through forward propagation. Backpropagation is then used to adjust model weights based on the difference between the predicted and actual values, gradually optimizing the model to approximate the true value. In the output phase, predictions can be made by inputting any pump power Pin and compared with measured data and the predictions from thin and thick lens models. Ultimately, for a specific laser crystal, at a specific pump power point, the predicted actual thermal focal length is output, along with the simulation results for either the thin or thick lens model.

[0100] In other embodiments of the present invention, the thermal focal length of the laser crystal is simulated by using formulas (1) and (2), and the measured data is analyzed and compared with the results of using a thin lens model and a thick lens model to form a data set for training a neural network screening model.

[0101] Specifically, build Figure 5As shown in the schematic diagram, a 785nm LD is first used as pump source 1 to illuminate the crystal, focusing its beam spot on the first bonding surface of Tm:YAG crystal 2. A 45° folding mirror 4 is placed between Tm:YAG crystal 2 and coupling lens 3. Heating of Tm:YAG crystal 2 produces a thermal lens effect, causing the red light emitted by He-Ne laser 6 to pass through the already convex lens effect of Tm:YAG crystal 2, gradually losing its parallel transmission properties and beginning to focus.

[0102] By manually moving the ceramic white plate 5 to observe the evolution of the light spot, it was found that the light spot gradually focused from a diverging ring to a focal point. The brightest point reflects the thermal focal length formed by the Tm:YAG crystal 2. In order to deal with the equivalence of the thermal focal length of the image and object space and calculate the true thermal focal length value, multiple measurements were performed to improve accuracy, considering that there may be errors in manually adjusting the ceramic white plate and observing the light spot size with the human eye. By using formulas (1) and (2) to simulate the thermal focal length of the crystal, the measured data were analyzed and compared with the results using thin lens and thick lens models to form a data set.

[0103] To verify the model's accuracy, Figure 6 shows the comparative prediction results at different power points using the neural network screening model of the present invention. It can be seen that the thick lens model has higher simulation accuracy at pump powers of 5-9.5W and 18-30W, while the thin lens model is more accurate in the 9.5-18W range. The model's maximum error is only 1.8mm, and the minimum error accuracy reaches 1.9%.

[0104] In step S2, the thermal focal length is predicted using the thin lens model or thick lens model selected at different power points in step S1, and negative lenses with different focal lengths are selected for thermal compensation. When using the thin lens model or thick lens model, respectively, the optical transfer matrix (ABCD matrix) is used, and according to the laser resonator stability condition -1 < (A + D) / 2 < 1, the following can be obtained:

[0105] When using a thin lens model and a negative lens to perform thermal compensation on an all-solid-state laser, the position distance of the negative lens in the resonant cavity of the all-solid-state laser satisfies:

[0106]

[0107] Wherein, L1 is the distance from the front end surface of the working material to the total reflection mirror;

[0108] L2 is the distance from the rear end face of the working material to the output mirror;

[0109] R2 is the curvature radius of the output mirror.

[0110] When using the thick lens model and the negative lens to perform thermal compensation on the all-solid-state laser, the position distance of the negative lens in the resonant cavity of the all-solid-state laser satisfies:

[0111]

[0112] Wherein, L1 is the distance from the front end surface of the working material to the total reflection mirror;

[0113] L2 is the distance from the rear end face of the working material to the output mirror;

[0114] R2 is the curvature radius of the output mirror.

[0115] In a preferred embodiment of the present invention, the focal length of the thin lens model is greater than that of the thick lens model, and the laser can be effectively thermally compensated when the following positional relationships are met:

[0116] When the focal length range of the negative lens is -5mm to -150mm;

[0117] The distance between the front face of the Tm:YAG crystal in the resonant cavity and the total reflection mirror ranges from 0 to 55 mm;

[0118] The distance between the rear end face of the Tm:YAG crystal and the negative lens ranges from 0 to 150 mm;

[0119] The distance between the negative lens and the output mirror ranges from 0 to 210 mm.

[0120] Taking the working material as a multi-segment bonded crystal Tm:YAG 3(0at.%)+3(2at.%)+5(3.5at.%) as an example, negative lenses with focal lengths of -25mm, -50mm, -75mm, -100mm and -125mm were introduced at 50mm on the rear end face of the multi-segment bonded crystal, and a simulation comparison diagram of the stable zone of the Tm:YAG laser resonator was obtained.

[0121] like Figure 6a As shown in FIG, when thermal compensation is performed for pump powers in the range of 8-18 W, the compensation effects vary with different negative focal lengths.

[0122] Specifically, when the focal length is F = -25mm, maximum cavity length compensation is achieved at almost all power points. At 17W, the compensation range reaches its maximum with a focal length of F = -125mm, with the length between the negative lens and the output mirror reaching 194.7mm. Meanwhile, when the focal length is F = -25mm, the minimum length L3 between the negative lens and the output mirror is only 16.7mm. This indicates that within this pump power range, lenses with longer focal lengths achieve better compensation, and a thin lens model is recommended.

[0123] like Figure 6bAs shown in the figure, thermal compensation for pump powers between 18 and 30 W is effective across all power points only when the focal length is F = -25 mm and L3 = 200 mm. The compensation effect of negative lenses at other focal lengths is nearly halved. When using the thick lens model, the maximum compensation range achieved with a negative lens of F = -25 mm at a pump power of 30 W reaches 173.4 mm. However, at an 18 W pump power, significantly better compensation is observed with a focal length of F = -125 mm than with a focal length of F = -25 mm.

[0124] It can be further seen that the combination of the negative lens and the lens model of the present invention can effectively perform thermal compensation. Moreover, regardless of the type of model used, after the negative lens is inserted, the length L3 between the negative lens and the output mirror shows a significant lengthening trend compared to the case without the negative lens. This not only expands the total cavity length of the resonant cavity, but also provides a potential opportunity to introduce more optical elements into the resonant cavity.

[0125] 1. Comparison of experimental compensation effects of thin lens and thick lens models

[0126] In order to ensure the space for inserting the negative lens, the maximum total cavity length of the resonant cavity is set to 250mm, and a negative lens with F=-100mm is selected for compensation. The distance between the front end face of the crystal and the total reflector is set to 6mm, the distance between the rear end face of the crystal and the negative lens is set to 4mm, and the distance between the negative lens and the output mirror is 190mm and 195mm respectively. The following comparative experiment is carried out.

[0127] Figure 7a The trend diagram of the continuous laser output power without a negative lens under the thick lens model shows that when the pump power is 30.31W, the maximum output powers obtained are 7.01W and 8.35W respectively;

[0128] Figure 7b This graph shows the laser output power trend after using a negative lens with a focal length of -100mm in the thick lens model. It shows that the addition of the negative lens provides thermal compensation for pump powers ranging from 14.41W to 30.31W. At pump powers of 18.23W and 23.68W, the output power increases by 0.581W and 0.51W, respectively.

[0129] Figure 7c The laser output power trend chart using a -100mm negative lens in the thin lens model shows that when the pump power is 18.23W and 23.68W, the output power only increases by 0.131W and 0.38W respectively;

[0130] At this time, when compensating the laser with a cavity length of 210 mm, the laser output power drops by 1.178 W.

[0131] Therefore, it is necessary to select the thin-thick lens model or the thick lens model according to the actual pump power value.

[0132] 2. Comparison of thermal compensation effects of negative focal lengths with different focal lengths and different positions in the insertion cavity based on the thick lens model, such as Figure 8 shown.

[0133] A negative lens with F=-100mm is selected for compensation, and the distance L1 between the front face of the crystal and the total reflective mirror is 6mm. Figure 8 a and Figure 8 The total cavity length of both lasers (b) is 120 mm, with the distance L2 between the rear facet of the laser crystal and the negative lens being 13 mm and 18 mm, respectively. It can be seen that, given the same cavity length, the closer the distance between the negative lens and the crystal facet, the greater the output power adjustment range (the greater the pump power it can withstand).

[0134] In addition, when the cavity length is 90 mm, the negative lens with F=-75 mm is selected and the position of the negative lens is adjusted to compensate. Figure 8 d and 8e show that when the distance between the rear end face of the laser crystal and the negative lens is L2 = 3 mm, the adjustment range of the output power is significantly greater than that of L2 = 8 mm.

[0135] Based on the above phenomenon, we used Rezonator to simulate the spot size in the cavity, as shown in the following figure: Figure 8 c and 8f. The results show that, at the distance between the rear end face of the crystal and the negative lens, the spot radius of the combination of L2 = 18mm / L2 = 8mm is significantly larger than that of the combination of L2 = 13mm / L2 = 3mm. As the intracavity laser passes through the compensation of the negative lens, the spot radius of the combination of L2 = 13mm / L2 = 3mmmm gradually increases and eventually exceeds the combination of L2 = 18mm / L2 = 8mm. At the output mirror, the difference in spot radius between the two reaches 14mm / 14.6mm. This is because the increase in the spot size when L2 = 13mm / L2 = 3mm leads to a corresponding expansion of the intracavity mode volume, which in turn increases the photon density allowed for oscillation, thereby helping to increase the output power of the laser. In addition, the increase in the intracavity mode volume will weaken the interaction between different modes, enhance the stability of the mode, and thus improve the overall stability of the laser.

[0136] In summary, the changes in the focal length and position of the negative lens will have different effects on the laser compensation. When the focal length range of the negative lens is -5mm to -150mm,

[0137] The distance between the front face of the Tm:YAG crystal in the resonant cavity and the total reflection mirror ranges from 0 to 55 mm;

[0138] The distance between the rear end face of the Tm:YAG crystal and the negative lens ranges from 0 to 150 mm;

[0139] When the distance between the negative lens and the output mirror is in the range of 0 to 210 mm, the laser can be effectively thermally compensated.

[0140] The second embodiment of the present invention discloses an apparatus embodiment that is consistent with the above embodiment, which is used to implement the method steps described in the above embodiment. The interpretation based on the same name meaning is the same as that of the above embodiment, and has the same technical effect as the above embodiment, which will not be repeated here.

[0141] As shown in FIG2 , the present invention discloses a full-solid-state laser thermal compensation device based on machine learning, comprising:

[0142] A lens model selection unit 201 is configured to select a lens model type according to parameters of a working material of the all-solid-state laser to be compensated and pump light power, wherein the lens model type is a thin lens model or a thick lens model;

[0143] The thermal compensation calculation unit 202 performs thermal compensation on the all-solid-state laser by using the thin lens model or the thick lens model and a negative lens.

[0144] The third embodiment of the present invention discloses an all-solid-state laser based on machine learning, including the all-solid-state laser thermal compensation device based on machine learning in the above embodiment.

[0145] The flow charts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products disclosed in various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0146] The units involved in the embodiments described in the present invention may be implemented in software or hardware, wherein the name of a unit does not necessarily limit the unit itself.

Claims

1. A method for thermal compensation of all-solid-state lasers based on machine learning, characterized in that: include: Selecting a lens model type according to parameters of a working material of the all-solid-state laser to be compensated and pump light power, wherein the lens model type is a thin lens model or a thick lens model; Thermally compensating the all-solid-state laser using one of the thin lens model or the thick lens model in conjunction with a negative lens; The expression of the thick lens model is: ； The expression of the thin lens model is: ； in, represents the spot radius of the pump light; represents the pump light power; represents thermal conductivity; represents the crystal length; represents the refractive index of the crystal; b represents the crystal radius; represents the thermal load ratio of the crystal; represents the absorption coefficient; represents the photoelastic coefficient; dn / dt represents the thermo-optical coefficient; When the thin lens model is used in conjunction with a negative lens to perform thermal compensation on the all-solid-state laser, the position distance of the negative lens in the resonant cavity of the all-solid-state laser satisfies: ； Wherein, L1 is the distance from the front end surface of the working material to the total reflection mirror; L2 is the distance from the rear end face of the working material to the output mirror; R2 is the curvature radius of the output mirror.

2. The method according to claim 1, characterized in that The method of selecting a lens model type according to the parameters of the working material of the all-solid-state laser to be compensated and the pump light power includes: Constructing a neural network screening model, inputting the parameters of the working substance and the pump light power into the neural network screening model to obtain the lens model type; The working material includes: a crystal with a single doping concentration or a multi-segment bonded laser crystal; The parameters of the working material include: the doping concentration of the laser crystal and the corresponding laser crystal segment length.

3. The method according to claim 2, characterized in that The neural network screening model is based on a back propagation neural network to create a multi-layer perceptron regression model; The parameters of the input layer of the neural network screening model include: a thick lens model simulation result obtained using the thick lens model, a thin lens model simulation result obtained using the thin lens model, a measured thermal focal length value, parameters of the working material, and a pump light power value.

4. The method according to claim 1, wherein The focal length range of the negative lens is -5mm to -150mm.

5. The method according to claim 1, wherein The working material is: multi-segment bonded Tm:YAG laser crystal; the first segment is undoped Tm 3+ , length is 3mm; the second segment Tm 3+ The doping concentration is 2 at.%, and the length is 3 mm; the third section Tm 3+ The doping concentration is 3.5 at.%, and the length is 5 mm.

6. The method according to claim 5, characterized in that The distance between the front end surface of the working material and the total reflection mirror ranges from 0 to 55 mm; The distance between the rear end surface of the working material and the negative lens ranges from 0 to 150 mm; The distance between the negative lens and the output mirror ranges from 0 to 210 mm.

7. A full-solid-state laser thermal compensation device based on machine learning, characterized in that: The method for implementing any one of claims 1 to 6 comprises: A lens model selection unit, configured to select a lens model type according to parameters of a working material of the all-solid-state laser to be compensated and pump light power, wherein the lens model type is a thin lens model or a thick lens model; A thermal compensation calculation unit is configured to perform thermal compensation on the all-solid-state laser by utilizing one of the thin lens model or the thick lens model in combination with a negative lens.

8. A machine learning-based all-solid-state laser, characterized in that: include: The device as claimed in claim 7.

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