CN114584252B - Micro-ring resonance wavelength searching method combined with particle swarm algorithm - Google Patents
Micro-ring resonance wavelength searching method combined with particle swarm algorithm Download PDFInfo
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Abstract
The invention discloses a micro-ring resonance wavelength searching method combined with a particle swarm algorithm, which comprises the following steps: s1, randomly initializing a heating power particle group to obtain straight-through end light power values corresponding to n heating power particles, a straight-through end light power optimal value of the heating power particle group and an optimal position of the heating power particle group; s2, judging the optimal value Y of the ith generation of heating power particle swarm m Whether it is less than a threshold; s3, updating the moving speed and the moving position of each heating power particle in the particle swarm; s4, obtaining a new generation heating power particle group through end light power value Y (n), updating a heating power particle history optimal position p (n) and a new generation heating power particle group global optimal position g, and judging the global optimal value Y obtained after iteration m Whether less than a threshold. The method of the invention applies the particle swarm algorithm to the global optimal heating power search, the consumption time for searching the optimal heating power value each time is greatly reduced compared with the global scanning algorithm, and the searching speed is improved.
Description
Technical Field
The invention belongs to the technical field of silicon-based micro-ring wavelength control, and particularly relates to a micro-ring resonance wavelength searching method combined with a particle swarm algorithm.
Background
Microrings are used in a large number of photonic integrated applications due to their spectral selectivity, compact footprint and low power consumption characteristics. In practical application, the resonant wavelength and the signal wavelength are generally required to be aligned to realize corresponding functions, and a modulator based on micro-ring design is taken as an example for explanation, PN junction is integrated on the micro-ring to carry out high-speed electro-optic modulation, and when the micro-ring is in a 0 state, the resonant wavelength of the micro-ring is consistent with the signal wavelength, and at the moment, the light power of a straight-through end is in a low state; in the state 1, the micro-ring resonance wavelength is inconsistent with the signal wavelength, at the moment, the light power of the straight-through end is in a high state, and the modulated signal can be obtained by adopting a photoelectric detector to convert the straight-through end. In an ideal case, the original signal and the modulation signal are in the same amplitude and phase, but due to the heat sensitivity of the silicon-based micro-ring device, the resonance wavelength of the micro-ring is offset left and right due to the fluctuation of the ambient temperature, so that the resonance wavelength is not aligned with the signal wavelength, and the light power of the direct-current end is always in a high state, so that the quality of the modulation signal is greatly influenced. In addition to temperature fluctuations affecting the resonance wavelength drift, the actual resonance wavelength of the micro-ring may deviate from the designed resonance wavelength due to the existence of manufacturing errors. Currently, in order to solve the problem that environmental temperature fluctuation and manufacturing errors affect the resonant wavelength of the micro-ring, monitoring parameter changes caused after the wavelength of the micro-ring is misaligned, counteracting the influence of the environmental temperature by applying proper heating power to a micro-heater integrated on the micro-ring, and forming a closed-loop control system to maintain the always aligned resonant wavelength and target wavelength is a common wavelength locking method.
The heating power control algorithm of the micro heater on the micro ring is generally divided into a global searching and local locking stage, and the global scanning searching and local locking control algorithm has mature technology, wide application and high stability. However, the global scanning algorithm has a certain limitation, firstly, the algorithm needs to set a fixed step length, if the step length is too large, the drift of the resonant wavelength caused by increasing the heating power for one time skips the target wavelength, the minimum through end optical power value can not appear in the whole scanning process, and the global optimal heating power can not be found; if the step size is too small, the minimum value of the direct-end light power is scanned, but a large number of steps are needed to complete the whole scanning process, and excessive searching time is consumed.
Disclosure of Invention
The invention mainly aims to overcome the defects and shortcomings of the prior art, and provides a micro-ring resonance wavelength searching method combined with a particle swarm algorithm, wherein the particle swarm algorithm is used for a global searching optimal heating power process of micro-ring wavelength locking, so that the problems of more searching times and low speed of the existing global scanning searching algorithm are solved, and a solution is provided for improving the wavelength locking global searching speed of a silicon-based micro-ring.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a micro-ring resonance wavelength searching method combined with a particle swarm algorithm comprises the following steps:
s1, randomly initializing a heating power particle group to obtain straight-through end light power values corresponding to n heating power particles, a straight-through end light power optimal value of the heating power particle group and an optimal position of the heating power particle group;
s2, judging the optimal value Y of the ith generation of heating power particle swarm m Whether it is less than a threshold;
s3, updating the moving speed and the moving position of each heating power particle in the particle swarm;
s4, obtaining a new generation heating power particle group through end light power value Y (n), updating a heating power particle history optimal position p (n) and a new generation heating power particle group global optimal position g, and judging the global optimal value Y obtained after iteration m Whether less than a threshold.
Further, the step S1 specifically includes:
at [0, P max ]Randomly selecting n initial heating power particles x (b) in a range, respectively applying the plurality of initial heating power particles to the micro-ring resonator, and obtaining optical power y (n) corresponding to each initial heating power particle at the straight-through end of the micro-ring; wherein P is max Is the maximum heating power;
comparing a plurality of Y (n) to obtain a minimum direct end light power value Y m Namely the optimal value of the optical power at the straight-through end of the 0 th generation heating power particle group, and Y m Corresponding heating power X m And the optimal position of the heating power particle swarm of the 0 th generation is obtained.
Further, the step S2 specifically includes:
assuming that when the light power of the micro-ring through end is at a larger value level, the resonance wavelength of the micro-ring is not aligned with the signal wavelength, and when the light power of the micro-ring through end is at a minimum value level, the resonance wavelength of the micro-ring is completely aligned with the signal wavelength;
setting a proper threshold value;
when the optimal value of the light power of the through end of the ith generation of heating power particle swarm is smaller than the threshold value, the resonance wavelength of the micro-ring is approximately aligned with the signal wavelength, and the micro-ring enters a local locking stage at the moment, and the optimal position X of the ith generation of heating power particle swarm is obtained m Adjusting on the basis to ensure that the output light power of the through end of the micro-ring is at the minimum level, and completely aligning the resonance wavelength of the micro-ring with the signal wavelength at the moment to finish the local wavelength locking stage of the micro-ring;
otherwise, the next iteration is performed to obtain a new heating power particle swarm through end optical power optimal value and a heating power particle swarm optimal position, and step S3 is performed.
Further, step S3 includes:
s31, calculating to obtain a new heating power particle movement speed value and carrying out boundary processing;
s32, updating the positions of the heating power particles and performing boundary processing.
Further, the step S31 specifically includes:
calculating a heating power particle moving speed value, wherein a calculation formula is as follows:
v(n)＝w*v(n)+C 1 *rand1*[p(n)-x(n)]+C 2 *rand2*[g-x(n)]
wherein V (n) is the moving speed value of the nth heating power particle, and the initial moving speed value is [ V ] min ，V max ]Randomly select within the range V max 、V min Respectively a maximum value and a minimum value of the moving speed of the heating power particles; w is a dynamic inertia weight value, and the calculation formula is as follows:
w＝W max -(W max -W min )*i/T
wherein W is max 、W min Respectively setting a maximum value and a minimum value of the inertia weight of the movement speed of the heating power particles, wherein i is the iteration number, and T is the maximum iteration number;
as the iteration number increases, the dynamic inertia weight gradually decreases, and the influence of the moving speed of the heating power particle of the previous generation on the moving speed of the heating power particle of the previous generation is smaller;
C 1 、C 2 for the heating power particle moving speed learning factors are constants, rand1 and rand2 are random numbers in the range of (0, 1), p (n) is the optimal position of the n-th heating power particle history, namely the position corresponding to the minimum value of the light power of the through end of the heating power particle history after multiple iterations, and g is the optimal position of the heating power particle group of the previous generation;
boundary processing is performed when V (n) is not at [ V ] min ，V max ]When a range is reached, it is updated to a random value within the range.
Further, the step S32 specifically includes:
updating the heating power particle position, specifically:
x(n)＝x(n)+v(n)
the n-th heating power particle moves v (n) on the original position x (n) to obtain a new heating power particle position;
boundary processing is performed when x (n) is not at [0, P max ]When a range is reached, it is updated to a random value within the range.
Further, the step S4 specifically includes:
sequentially applying a new heating power particle group x (n) on the micro-ring resonator to obtain a new straight-through end optical power value y (n), comparing the straight-through end optical power value y (n) corresponding to x (n) of each heating power particle in the new generation particle group with y (n) of a previous generation heating power particle, if y (n) of the new generation is smaller than y (n) of the previous generation, updating the heating power x (n) corresponding to y (n) of the new generation into an n-th particle history optimal position p (n), otherwise, keeping p (n) unchanged;
comparing the new generation Y (n) to obtain the minimum value Y of the light power of the direct end m Is the optimal value of the heating power particle group of the new generation, and Y m Corresponding heating power X m The optimal position g of the particle swarm is the new generation;
judging the optimal value Y of the through end of the heating power particle swarm obtained after iteration m If the power is smaller than the threshold value, entering a local locking stage, otherwise repeating the steps S3 to S4 until the through end optimal value and the heating power particle optimal position which are smaller than the threshold value are found and entering the local locking stage.
Further, the threshold is specifically set to be a 1/2 intermediate value between the larger value and the minimum value of the output optical power of the micro-ring through end.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the particle swarm algorithm is used for globally searching the optimal heating power process of the micro-ring wavelength locking; the initial heating power particle swarm is randomly selected, heating power is increased or decreased according to the historical optimal position of each particle and the historical optimal position of the particle swarm, the global optimal heating power is searched for repeatedly, the searching times are theoretically 18 times less than that of the traditional method for searching the optimal heating power through scanning, and the global searching speed is greatly improved.
The particle swarm algorithm is applied to global optimal heating power search, the consumption time for searching the optimal heating power value each time is greatly reduced compared with that of the global scanning algorithm, and the searching speed is improved.
3. The algorithm provided by the invention is not only suitable for the thermo-optical modulation search occasion based on the micro-ring, but also suitable for the thermo-optical modulation occasion of some other photon devices such as MZI.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a line graph of the through end of a conventional silicon-based microring;
FIG. 3a is a statistical chart of the number of heating times in the multiple simulation experiment of the embodiment;
FIG. 3b is a statistical plot of the number of occurrences of optimal heating power obtained from multiple simulation experiments of the examples.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but embodiments of the present invention are not limited thereto.
Examples
As shown in fig. 1, the micro-ring resonance wavelength searching method combined with the particle swarm algorithm comprises the following steps:
s1, randomly initializing a heating power particle swarm to obtain straight-through end light power values corresponding to n heating power particles, a straight-through end light power optimal value of the heating power particle swarm and an optimal position of the heating power particle swarm; the method comprises the following steps:
at [0, P max ]Randomly selecting n initial heating power particles x (n), P in a range max For maximum heating power, initial heating power particles are respectively applied to the micro-ring resonator, and the optical power y (n) corresponding to each initial heating power particle can be obtained at the straight-through end of the micro-ring.
Comparing Y (n) to obtain the minimum value Y of the light power of the direct-current end m Namely the optimal value of the optical power at the straight-through end of the 0 th generation heating power particle group, and Y m Corresponding heating power X m Is 0 thAnd substituting the optimal position of the heating power particle swarm.
S2, judging the optimal value Y of the ith generation of heating power particle swarm m Whether it is less than a threshold; the method comprises the following steps:
when the light power of the micro-ring through end is at a larger value level, the resonance wavelength of the micro-ring is not aligned with the signal wavelength, and when the light power of the micro-ring through end is at a smaller value level, the resonance wavelength of the micro-ring is completely aligned with the signal wavelength. As shown in FIG. 2, the spectral lines at the straight-through end of a conventional silicon-based microring are shown in FIG. 1. The wavelength corresponding to the lowest power point of the through end is the resonance wavelength of the micro-ring.
Setting the threshold value as a 1/2 intermediate value between the larger value and the minimum value of the output optical power of the micro-ring straight-through end, when the optimal value of the optical power of the straight-through end of the ith generation of heating power particle swarm is smaller than the threshold value, considering that the resonance wavelength of the micro-ring is approximately aligned with the signal wavelength, enabling the micro-ring to enter a local locking stage, and enabling the micro-ring to enter an optimal position X of the ith generation of heating power particle swarm m Fine tuning is carried out on the basis, so that the resonance wavelength of the micro-ring is completely aligned with the signal wavelength; otherwise, the next iteration is carried out to obtain a new heating power particle swarm through end optimal value and a heating power particle swarm optimal position.
S3, updating the moving speed and the moving position of each heating power particle in the particle swarm; the method comprises the following steps:
s31, calculating to obtain a new heating power particle movement speed value and carrying out boundary processing:
v(n)＝w*v(n)+C 1 *rand1*[p(n)-x(n)]+C 2 *rand2*[g-x(n)]
wherein V (n) is the moving speed value of the nth heating power particle, and the initial moving speed value is [ V ] min ，V max ]Randomly select within the range V max 、V min Respectively a maximum value and a minimum value of the moving speed of the heating power particles; w is a dynamic inertial weight value:
w＝W max -(W max -W min )*i/T
W max 、W min respectively setting the maximum value and the minimum value of the inertia weight of the movement speed of the heating power particles, wherein i is the iteration number; t is the maximum iteration timeThe number of the heating power particles gradually decreases along with the increase of the iteration times, and the influence of the moving speed of the heating power particles of the previous generation on the moving speed of the heating power particles of the previous generation is smaller;
C 1 、C 2 for the heating power particle moving speed learning factors are constants, rand1 and rand2 are random numbers in the range of (0, 1), p (n) is the optimal position of the n-th heating power particle history, namely the position corresponding to the minimum value of the light power of the through end of the heating power particle history after multiple iterations, and g is the optimal position of the heating power particle group of the previous generation;
boundary processing is performed when V (n) is not at [ V ] min ，V max ]When the range is within, updating the range to a random value in the range;
s32, updating the heating power particle positions and carrying out boundary processing:
x(n)＝x(n)+v(n)
the n-th heating power particle moves v (n) on the original position x (n) to obtain a new heating power particle position;
boundary processing is performed when x (n) is not at [0, P max ]When a range is reached, it is updated to a random value within the range.
S4, obtaining a new generation heating power particle group through end light power value Y (n), updating a heating power particle history optimal position p (n) and a new generation heating power particle group global optimal position g, and judging the global optimal value Y obtained after iteration m Whether it is less than a threshold; the method comprises the following steps:
and sequentially applying the new heating power particle swarm x (n) to the micro-ring resonator to obtain a new straight-through end optical power value y (n), comparing the straight-through end optical power value y (n) corresponding to x (n) of each heating power particle in the new generation particle swarm with y (n) of the heating power particle of the previous generation, if the y (n) of the new generation is smaller than the y (n) of the previous generation, updating the heating power x (n) corresponding to the y (n) of the new generation into an n-th particle historical optimal position p (n), otherwise, keeping p (n) unchanged.
Comparing the new generation Y (n) to obtain the minimum value Y of the light power of the direct end m Is the optimal value of the heating power particle group of the new generation, and Y m Corresponding heating power X m Is the optimal position g of the new generation particle swarm.
Judging the optimal value Y of the through end of the heating power particle swarm obtained after iteration m If the power is smaller than the threshold, entering a local locking stage, otherwise repeating the step S3 and the step S4 until the through end optimal value and the heating power particle optimal position which are smaller than the threshold are found and entering the local locking stage.
In this embodiment, a simulation experiment was performed in matlab to search for the optimal heating power globally. In the experiment, maximum heating power P max The maximum value of the output of the straight-through end is set to be 1000mW, the minimum value is set to be-60 dBm, the threshold value is set to be-30 dBm, the population number of heating power particles is 15, the maximum iteration number is 20, and the learning factor C is set to be 1 、C 2 Are all set to 1.5, and the maximum value and the minimum value W of the inertia weight are max 、W min Set to 0.8 and 0.4, respectively, maximum and minimum speeds V max And V min Setting to 10 and-10 respectively, setting threshold comparison in the whole program, immediately exiting iteration when the heating position smaller than the threshold appears, and obtaining simulation results as shown in fig. 3a and 3b, wherein the maximum heating times are 20 times and the average heating times are 29 times as seen from a histogram of the heating times in 1000 simulation experiments; and the converged wavelength obtained in 1000 experiments was [1549.95nm,1550.05nm]In the range, the bandwidth of 3dB less than that of the micro-ring is 0.2nm, which shows that the algorithm can search to obtain the global optimal heating power. If the step size is set to 1mw using the conventional scan search method, it theoretically takes 500 times on average to get the global optimum heating power. In summary, compared with the traditional global searching method, the algorithm provided by the invention is improved by about 18 times on the heating times.
It should also be noted that in this specification, terms such as "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. The micro-ring resonance wavelength searching method combined with the particle swarm algorithm is characterized by comprising the following steps of:
s1, randomly initializing a heating power particle group to obtain straight-through end light power values corresponding to n heating power particles, a straight-through end light power optimal value of the heating power particle group and an optimal position of the heating power particle group;
s2, judging whether the minimum value of the light power of the ith generation of direct end is smaller than a threshold value; if the judgment result is negative, entering a step S3, otherwise, entering a local locking stage by the micro ring;
s3, updating the moving speed and the moving position of each heating power particle in the particle swarm;
s4, obtaining a light power value y (n) ', an updated heating power particle history optimal position p (n) and a new generation heating power particle group optimal position g' of the through end of the new generation heating power particle group, and judging whether the minimum value of the light power of the through end obtained after iteration is smaller than a threshold value; if not, entering step S3, otherwise, entering the partial locking stage by the micro ring.
2. The method for searching for micro-ring resonance wavelength in combination with particle swarm optimization according to claim 1, wherein step S1 comprises the following steps:
at [0, P max ]Randomly selecting n initial heating power particles x (n) in a range, respectively applying the plurality of initial heating power particles to the micro-ring resonator, and obtaining optical power y (n) corresponding to each initial heating power particle at the straight-through end of the micro-ring; wherein P is max Is the maximum heating power;
comparing the y (n) to obtain the minimum value of the light power of the through end, namely the optimal value of the light power of the through end of the 0 th generation heating power particle swarm, wherein the heating power corresponding to the optimal value of the light power of the through end of the 0 th generation heating power particle swarm is the optimal position of the 0 th generation heating power particle swarm.
3. The method for searching for micro-ring resonance wavelength in combination with particle swarm optimization according to claim 1, wherein step S2 comprises the following steps:
assuming that when the light power of the micro-ring through end is at a larger value level, the resonance wavelength of the micro-ring is not aligned with the signal wavelength, and when the light power of the micro-ring through end is at a minimum value level, the resonance wavelength of the micro-ring is completely aligned with the signal wavelength;
setting a proper threshold value;
when the optimal value of the optical power of the through end of the ith generation of heating power particle swarm is smaller than a threshold value, the resonance wavelength of the micro-ring is approximately aligned with the signal wavelength, at the moment, the micro-ring enters a local locking stage, adjustment is performed on the basis of the heating power corresponding to the optimal value of the optical power of the through end of the ith generation of heating power particle swarm, the output optical power of the through end of the micro-ring is ensured to be at the minimum level, at the moment, the resonance wavelength of the micro-ring is completely aligned with the signal wavelength, and the local wavelength locking stage of the micro-ring is completed;
otherwise, the next iteration is performed to obtain a new heating power particle swarm through end optical power optimal value and a heating power particle swarm optimal position, and step S3 is performed.
4. The method for searching for a micro-ring resonance wavelength in combination with a particle swarm algorithm according to claim 2, wherein step S3 comprises:
s31, calculating to obtain a new heating power particle movement speed value and carrying out boundary processing;
s32, updating the positions of the heating power particles and performing boundary processing.
5. The method for searching for micro-ring resonance wavelength in combination with particle swarm optimization according to claim 4, wherein step S31 comprises the following steps:
calculating a heating power particle moving speed value, wherein a calculation formula is as follows:
v(n)′＝w*v(n)+C 1 *rand1*[p(n)-x(n)]+C 2 *rand2*[g-x(n)]
wherein V (n) is the moving speed value of the nth heating power particle, and the initial moving speed value is [ V ] min ，V max ]Randomly select within the range V max 、V min Respectively a maximum value and a minimum value of the moving speed of the heating power particles; w is a dynamic inertia weight value, and the calculation formula is as follows:
w＝W max -(W max -W min )*i/T
wherein W is max 、W min Respectively setting a maximum value and a minimum value of the inertia weight of the movement speed of the heating power particles, wherein i is the iteration number, and T is the maximum iteration number;
as the iteration number increases, the dynamic inertia weight gradually decreases, and the influence of the moving speed of the heating power particle of the previous generation on the moving speed of the heating power particle of the previous generation is smaller;
C 1 、C 2 for the heating power particle moving speed learning factors are constants, rand1 and rand2 are random numbers in the range of (0, 1), p (n) is the optimal position of the n-th heating power particle history, namely the position corresponding to the minimum value of the light power of the through end of the heating power particle history after multiple iterations, and g is the optimal position of the heating power particle group of the previous generation;
boundary processing is performed when V (n)' is not at [ V ] min ，V max ]When a range is reached, it is updated to a random value within the range.
6. The method for searching for micro-ring resonance wavelength in combination with particle swarm optimization according to claim 5, wherein step S32 comprises:
updating the heating power particle position, specifically:
x(n)′＝x(n)+v(n)′
the n-th heating power particle moves v (n) 'on the original position x (n) to obtain a new heating power particle position x (n)';
boundary processing is performed when x (n)' is not at [0, P max ]When a range is reached, it is updated to a random value within the range.
7. The method for searching for micro-ring resonance wavelength in combination with particle swarm optimization according to claim 6, wherein step S4 comprises:
sequentially applying new heating power particles x (n) ' to the micro-ring resonator to obtain new straight-through end light power values y (n) ', comparing the straight-through end light power value y (n) ' corresponding to each heating power particle x (n) ' in the new generation of particle swarm with y (n) of the previous generation of heating power particles, if y (n) ' is smaller than y (n), updating the heating power particle x (n) ' corresponding to y (n) ' to be the n-th particle history optimal position p (n), otherwise, keeping p (n) unchanged;
comparing y (n) 'to obtain the minimum value of the light power of the straight-through end, namely the optimal value of the light power of the straight-through end of the heating power particle swarm of the new generation, wherein the heating power corresponding to the optimal value of the light power of the straight-through end of the heating power particle swarm of the new generation is the optimal position g' of the heating power particle swarm of the new generation;
and judging whether the minimum value of the through-end optical power obtained after iteration is smaller than a threshold value, if so, entering a local locking stage, otherwise, repeating the steps S3 to S4 until the minimum value of the through-end optical power and the optimal position of the heating power particle swarm which are smaller than the threshold value are found, and entering the local locking stage.
8. The method for searching for micro-ring resonance wavelength in combination with particle swarm optimization according to claim 3 or 7, wherein the threshold is specifically set to 1/2 of the maximum and minimum output optical power of the micro-ring through terminal.
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