Abstract
This paper presents an emerging theory on the effects of unavoidable process variations during the fabrication of MEMS and other microsystems. The effects of parametric variations on device performance and design yield of the microsystems devices are analyzed and presented. A novel methodology in the design cycle of MEMS and other microsystems is introduced. The methodology is based on the concept of worst-case analysis having colossal advantages to offer. This paper describes some steps of this methodology with elaborated results. Also described in this paper is how each step contributes to counteract the effects produced by the parametric variations in the product cycle of microsystems.
Similar content being viewed by others
References
Antreich KJ, Graeb HE, Wieser CU (1994) Circuit analysis and optimization driven by worst-case distances. C.U. Comput Aided Des Integr Circ Syst IEEE Trans 13(1):57--71
Boning DS, McIlrath MB, Penfield P Jr., Sachs EM (1992) A general semiconductor process modeling framework. IEEE Trans Semi Manuf 5(4):266–280
Clark JV, Zhou N, Bindel D, Schenato L, Wu W, Demmel J, Pister KSJ (2000) 3D MEMS simulation modeling using modified nodal analysis. In: Proceedings of the microscale systems: mechanics and measurements symposium, Orlando, pp 68–75
Director S, Maly W, Strojwas A (1990) VLSI design for manufacturing: yield enhancement. Kluwer, USA
Jing Q, Fedder G (1998) NODAS 1.3—nodal design of actuators and sensors. In: Proceedings of 1998 IEEE/VIUF international workshop on behavioral modeling and simulation (BMAS ‘98), Orlando, FL, USA
Moyne WP (2000) Enhancing MEMS design using statistical process information. Ph.D. thesis, MIT, Cambridge
Peters D, Bechtold St, Laur R (1999) Optimized behavioral model of a pressure sensor including the touch-down effect. In: Technical proceedings of the second conference on modeling and simulation of microsystems, Puerto Rico, vol 1, pp 237–240
Ren H, Jog A, Fair RB (2001) Statistical optimal design of microelectromechanical system (MEMS). Nanotech, vol 1, Hilton Head Island, pp 169–172
Schenato L, Wu WC, Ghaoui LE, Pister K (2000) Process variation analysis for MEMS design. SPIE, Melbourne
Schenkel F, Pronath M, Zizala S, Schwencker R, Graeb HE, Antreich K (2001) Mismatch analysis and direct yield optimization by Spec-Wise linearization and feasibility-guided search. DAC
Vudathu SP, Duganapalli KK, Laur R, Kubalinska D, Bunse-Gerstner A (2006) Parametric yield analysis of MEMS via statistical methods. DTIP, Stresa
Wong KS, Boning BS (1995) On in-situ etch rate estimation from interferometric signals. In: Proceedings of process control, diagnostics, and modeling in semiconductor manufacturing I, 187th Electrochemical Society Meeting, Reno, NV, pp 360–371
Zhou N, Clark JV, Pister KSJ (1998) Nodal simulation for MEMS design using SUGAR v0.5. In: International conference on modeling and simulation of microsystems semiconductors, sensors and actuators, Santa Clara, CA, pp. 308–313
Acknowledgments
This work is sponsored by the European Union funded “Patent-Design for Micro & Nano Manufacture” (PATENT-DfMM) project.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Vudathu, S.P., Duganapalli, K.K., Laur, R. et al. Yield analysis via induction of process statistics into the design of MEMS and other microsystems. Microsyst Technol 13, 1545–1551 (2007). https://doi.org/10.1007/s00542-006-0263-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00542-006-0263-3