Reviewer #1: Minor revisions The authors presented a hybrid prognostics approach to estimate remaining useful life of MEMS and validated its effectiveness with in-situ measurements. The paper is very well-organized and easy to read. However, the following minor issues should be addressed to be published in Microelectronics Reliability. 1. The authors need to trim down "Abstract" section. Instead, please emphasize the authors' contributions in this paper. 2. In the Introduction section, the authors need to more focus on this research (not their project). Please remove or revise sentences that are directly related to this research. Likewise, it will be helpful for readers if the authors provide qualitative literature reviews for MEMS prognostics to understand the trend of MEMS prognostics, what pros and cons of today's MEMS prognostics are, and so forth. If possible, a brief explanation about the difference between the proposed hybrid approach and conventional MEMS prognostics approaches will be welcome. 3. In the fourth paragraph, Section 2.2, page 6, please refer the following reference to properly describe prognostics approaches: model-based (also called physics-of-failure), data-driven, and hybrid (or fusion) prognostics approaches. M. Pecht, Prognostics and Health Management of Electronics, Wiley: New York, NY, 2009. 4. In the 2nd paragraph, Section 2.3, page 8, the authors stated "Improving reliability of MEMS device has several advantages, such as increasing their lifetime and improving their performance." I totally agree with "improving reliability of MEMS device can increase their lifetime". But, I am a bit curious about how "improving reliability of MEMS can improve their performance"? 5. In the 2nd paragraph, Section 2.3, page 8, I would recommend the authors to cite the following reference for the definition of reliability. K. C. Kapur and M. Pecht, Reliability Engineering, Wiley: New York, NJ, 2014. Reviewer #2: Major revisions and re-reviewing The reference could be better used. Although the authors have provided a sufficient amount of references, they are used sequentially after some paragraphs, and the synthesis between them is not apparent. The sources could be used more analytically instead of describing other researchers work. The authors use frequently the descriptive statements (such as page 9 paragraph 2 ) There is too much information on the background of prognostics for a specific application paper. After awhile, I felt like I was reading a tutorial. The particle Filtering method (p 12-16) that is described has been used by many papers before. I struggle to see what is their contribution or novelty. There are even some matlab codes published for this algorithm. Check these papers, they are very close methodologies, so I can't see the novelty, except perhaps the application to MEMS devices, but if this is the case the paper should be refocused on MEMS and not so much on prognostics. 1.M. E. Orchard and G. J. Vachtsevanos, "A particle-filtering approach for on-line fault diagnosis and failure prognosis," Transactions of the Institute of Measurement and Control, 2009. 2.P. Wang and R. X. Gao, "Particle filtering-based system degradation prediction applied to jet engines." 3.B. Saha and K. Goebel, "Modeling li-ion battery capacity depletion in a particle filtering framework," in Proceedings of the annual conference of the prognostics and health management society, 2009, pp. 2909-2924. 4.N. Daroogheh, N. Meskin, and K. Khorasani, "Particle filtering for state and parameter estimation in gas turbine engine fault diagnostics," in American Control Conference (ACC), 2013. IEEE, 2013, pp. 4343- 4349. 5.D. An, J.-H. Choi, and N. H. Kim, "Prognostics 101: A tutorial for particle filter-based prognostics algorithm using matlab," Reliability Engineering & System Safety, vol. 115, pp. 161-169, 2013. Page 25 - The figures contains very few time series, Is it really possible make an accurate filtering? Eq 15 p26 - Reference it, similar formulas are used in many researches and more discussion is required There can be more analysis on predictions Page 22 - "By using Matlab system identification toolbox, the transfer function can be obtained and all the system parameters can be easily identified. " - ??? So what? "Then, in the prediction stage, the prognostics tool propagates the state of the system and determines at what time the failure threshold (FT) is reached." - How do they calculate the failure threshold, according to what? Reviewer #3: Accept The paper can be published essentially as is.