## 5 Conclusions  
In this paper, we study the signal processing algorithm of micro-vibration measurement, and propose a micro-vibration signal denoising algorithm of spectral morphology fitting based on variational mode decomposition. The variational mode decomposition algorithm cannot effectively identify the low-amplitude noise information. Therefore, the intrinsic mode function components are mixed with low-amplitude noise which is randomly distributed in the whole spectrum range. In this paper, the spectral morphology fitting method of the asymmetric generalized Gaussian mixture model is used to separate the main frequency information from the secondary frequency information and the measurement noise on it, so as to improve the denoising performance of the algorithm. In order to address the problems of nonlinear curve fitting algorithms that tend to fall into local optimum solutions and convergence speed, this paper improves the whale optimization algorithm. The adaptive weight is introduced to improve the convergence speed and global optimization ability of the algorithm. When the parameters of nonlinear complex functions are fitted, the algorithm has higher accuracy. In the fitting of complex micro-vibration acceleration signal data, the algorithm proposed in this paper has higher flexibility. It effectively solves the problem that the effective frequency band information is lost or the excess noise  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal Type** & **Peak-to-Peak Value of** & **Peak-to-Peak Error of** \\  & **Acceleration** & **Acceleration** \\ \hline Original signal & 26.50 mg & 27.51\% \\ GD & 21.88 mg & 5.30\% \\ AGMD & 21.09 mg & 1.52\% \\ Laser signal & 20.78 mg & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Signal comparison results before and after denoising  
Figure 15: Comparison of acceleration time curves after algorithm denoising.  
information is introduced due to the inaccurate parameter fitting of the traditional curve fitting algorithm.  
Through numerical simulation examples and comparative experimental analysis, the superiority of the proposed algorithm in the denoising effect is verified. The acceleration peak-to-peak error was decreased from 27.51% to 1.52%. The denoising algorithm can effectively solve the problem of noise interference. The system and the denoising algorithm in this paper can realize the real-time and accurate grade evaluation of environmental micro-vibration. It has important significance for the scientific design of semiconductor production line and the safety production of semiconductor products.  
Conceptualization, [PERSON] ([PERSON]); methodology, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); software, [PERSON] ([PERSON]) and [PERSON] ([PERSON]); validation, [PERSON] ([PERSON]); formal analysis, [PERSON] ([PERSON]); investigation, [PERSON] ([PERSON]); resources, C.Y. and C.S.; data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, P.W. and C.S.; visualization, [PERSON] ([PERSON]); supervision, C.Y. and C.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.