Additive manufacturing (AM) enables the production of complex lattice structures that cannot feasibly or economically be manufactured any other way. However, there are complicating factors that engineers are likely to confront when designing fine AM lattice structures: geometric inaccuracy and anisotropic material properties.
Many additively manufactured polymers exhibit anisotropic mechanical properties which must be accounted for by engineers designing with these materials. This case study illustrates the importance of testing additively manufactured polymers at many orientations to identify the range of isotropic behavior as well as the optimal build orientation.
Stiction in MEMS devices can occur during manufacturing, testing, and operation in the field. Veryst Engineering approaches this problem through design and manufacturing processing to assure that stiction is eliminated in MEMS structures.
MEMS mirrors raster the laser beam in many next-generation LiDAR system designs. Constructing a finite element model of a MEMS mirror is challenging, as it is difficult to represent the large number of comb fingers in the comb drives that actuate these devices. Veryst addressed this problem by using mixed analytic and finite element approaches to construct accurate finite element models.
How long will a product last? This is an essential question during product development, but accurately predicting product end of life can be hampered by limited data. Veryst provides a method for the reliability engineer to predict end of life with a small sample size and shows how the proper lifetime prediction method can eliminate unexpected field failures.
Veryst consults in clean manufacturing, which is common for precision products that are deleteriously affected by particulate, molecular contamination, and human contamination. Key areas are: target contamination control levels for the room and equipment, detection and measurement of contamination, transport and deposition of contamination, removal of contamination, and sources of contamination.
Veryst works with clients to develop high-performance, reliable, and manufacturable medical devices. We apply advanced characterization technologies, engineering analysis, and sophisticated simulation software to provide cost-effective solutions to time-critical engineering problems.
Veryst assists clients with MEMS and sensors consulting through failure analysis, reliability, lifetime prediction, yield enhancement, micro-contamination analysis, and microfluidics and multiphysics simulations. We provide a synergistic approach of combining analytical characterization, empirical studies, and simulation. Veryst scientists are well versed in packaging reliability as well.
Veryst offers expertise in a full range of analytical tools and techniques for non-destructive and destructive failure analysis. Choosing the right analytical method is critical for determining the root cause of a failure. Some of the non-destructive methods we use begin with high magnification
Ms. Allyson Hartzell has recently joined Veryst Engineering, bringing more than three decades of professional experience in emerging technologies. Ms. Hartzell is an internationally recognized expert in MEMS reliability, and has expertise in surface chemistry and analytical techniques for failure analysis.
MEMS expert Allyson Hartzell addressed the challenges functional fabric developers face in accommodating sensors in their products with clever new interconnects such as conductive fabrics and bendable electronics.
Allyson Hartzell co-chaired the Mechanical Reliability session, provided a one-hour tutorial on “Reliability Considerations for Flexible Hybrid Electronics" as part of the Structural and Physical Health Monitoring track, and more.
Dr. James Ransley offered a webinar on Multiphysics Modeling of MEMS. This event was sponsored by COMSOL.
Allyson Hartzell offered a webinar addressing some of the newer MEMS and sensor devices with advanced packaging and flexible interconnects for wearable technologies, and addressed how to assess and model reliability.