Effect of process parameters on the surface morphology and mechanical performance of silicon structures after deep reactive ion etching (DRIE)

Abstract

Abstract-The ability to predict and control the influence of process parameters during silicon etching is vital for the success of most MEMS devices. In the case of deep reactive ion etching (DRIE) of silicon substrates, experimental results indicate that etch performance as well as surface morphology and post-etch mechanical behavior have a strong dependence on processing parameters. In order to understand the influence of these parameters, a set of experiments was designed and performed to fully characterize the sensitivity of surface morphology and mechanical behavior of silicon samples produced with different DRIE operating conditions. The designed experiment involved a matrix of 55 silicon wafers with radiused hub flexure (RHF) specimens which were etched 10 min under varying DRIE processing conditions. Data collected by interferometry, atomic force microscopy (AFM), profilometry, and scanning electron microscopy (SEM), was used to determine the response of etching performance to operating conditions. The data collected for fracture strength was analyzed and modeled by finite element computation. The data was then fitted to response surfaces to model the dependence of response variables on dry processing conditions. The results showed that the achievable anisotropy, etching uniformity, fillet radii, and surface roughness had a strong dependence on chamber pressure, applied coil and electrode power, and reactant gases flow rate. The observed post-etching mechanical behavior for specimens with high surface roughness always indicated low fracture strength. For specimens with better surface quality, there was a wider distribution in sample strength. This suggests that there are more controlling factors influencing the mechanical behavior of specimens. Nevertheless, it showed that in order to achieve high strength, fine surface quality is a necessary requisite. The mapping of the dependence of response variables on dry processing conditions produced by this systematic approach provides additional insight into the plasma phenomena involved and supplies a practical set of tools to locate and optimize robust operating conditions.[684]Index Terms-Deep reactive ion etching (DRIE), fracture strength, MEMS, plasma etching, silicon, surface morphology.

Single Molecular Detection via Micro-Scale Polymeric Opto-Electro-Mechanical Systems

“…This simplified expression is acquired by substituting the deflection dependant non-linear spring constant [86] for the pull-in displacement [87]. From this equation, the width and length of flexures can be determined for specific resonance frequencies and maximum displacements.…”

“…The DRIE method resulted in the scallops on the sidewall [86]. These scalloped surfaces were not desirable since there was light scattering on the replicated PDMS surface from the mold.…”

Predicting instrumental mass fractionation (IMF) of stable isotope SIMS analyses by response surface methodology (RSM)

“…15,16 Introduced by Box and Wilson, RSM has been applied to build up multivariate statistical models in a wide variety of industrial, engineering and experimental processes. 17 Successful RSM applications can be found in, e.g., Riley [18][19][20][21][22][23][24][25][26][27][28][29][30] In addition, the mathematical and statistical aspects of RSM and related experimental techniques are covered in e.g., Box, Box, and [31][32][33][34][35][36][37] Simultaneously, the statistical prediction models have been recognized as powerful tools for e.g., exploring the underlying causal relationships below the datasets, building and/or assessing new knowledge and improving previous models. 38 While explanatory statistical modelling is based on the causal relationships among previous theoretical constructions, the predictive statistical modelling works on associations of measurable variables.…”

Predicting instrumental mass fractionation (IMF) of stable isotope SIMS analyses by response surface methodology (RSM) [Dataset]

“…15,16 Introduced by Box and Wilson, RSM has been applied to build up multivariate statistical models in a wide variety of industrial, engineering and experimental processes. 17 Successful RSM applications can be found in, e.g., Riley [18][19][20][21][22][23][24][25][26][27][28][29][30] In addition, the mathematical and statistical aspects of RSM and related experimental techniques are covered in e.g., Box, Box, and [31][32][33][34][35][36][37] Simultaneously, the statistical prediction models have been recognized as powerful tools for e.g., exploring the underlying causal relationships below the datasets, building and/or assessing new knowledge and improving previous models. 38 While explanatory statistical modelling is based on the causal relationships among previous theoretical constructions, the predictive statistical modelling works on associations of measurable variables.…”