Mathematics at Sandia National Laboratories: Trust in Microelectronics Based Systems.
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An apparatus, method, and system, the apparatus including a receiving member dimensioned to receive an array of microelectronic devices; and a linkage member coupled to the receiving member, the linkage member configured to move the receiving member in at least two dimensions so as to modify a spacing between the electronic devices within the array of microelectronic devices received by the receiving member. The method including coupling an array of microelectronic devices to an expansion assembly; and expanding the expansion assembly so as to expand the array of microelectronic devices in at least two directions within a single plane. The system including a support member; an expansion assembly coupled to the support member, the expansion assembly having a plurality of receiving members configured to move in at least two dimensions within a single plane; and a plurality of microelectronic devices coupled to each of the plurality of receiving members.
Silicides and germanides are compounds consisting of a metal and silicon or germanium. In the microelectronics industry, silicides are the material of choice for contacting silicon based devices (over the years, CoSi{sub 2}, C54-TiSi{sub 2}, and NiSi have been adopted), while germanides are considered as a top candidate for contacting future germanium based electronics. Since also strain engineering through the use of Si{sub 1−x}Ge{sub x} in the source/drain/gate regions of MOSFET devices is an important technique for improving device characteristics in modern Si-based microelectronics industry, a profound understanding of the formation of silicide/germanide contacts to silicon and germanium is of utmost importance. The crystallographic texture of these films, which is defined as the statistical distribution of the orientation of the grains in the film, has been the subject of scientific studies since the 1970s. Different types of texture like epitaxy, axiotaxy, fiber, or combinations thereof have been observed in such films. In recent years, it has become increasingly clear that film texture can have a profound influence on the formation and stability of silicide/germanide contacts, as it controls the type and orientation of grain boundaries (affecting diffusion and agglomeration) and the interface energy (affecting nucleation during the solid-state reaction). Furthermore, the texture also has an impact on the electrical characteristics of the contact, as the orientation and size of individual grains influences functional properties such as contact resistance and sheet resistance and will induce local variations in strain and Schottky barrier height. This review aims to give a comprehensive overview of the scientific work that has been published in the field of texture studies on thin film silicide/germanide contacts.
Heterogeneous integration of microelectronic components provides a pathway to improve circuit/component performance; however, this comes with assembly challenges, in particular due to complex interfaces via subsurface bump bonds. The ability of these bonds to transmit electrical signals and conduct heat to the carrier substrate limits component performance. In this work, hyperspectral frequency‐domain thermoreflectance (FDTR) imaging is demonstrated as a robust technique for evaluating the quality of subsurface indium bump bonds in a surrogate microelectronic sample. By performing microscale FDTR imaging with coarse motion image stitching, thermal phase maps that cover a 4 mm by 4 mm field‐of‐view with subsurface feature sensitivity at depths greater than 50 µm are obtained. The resulting FDTR hyperspectral data contains more than three million pixels and reveal the quality of subsurface microbump arrays. Wide‐field analysis of bonded versus gap regions is enabled by deep neural network feature reconstruction, that after training, rapidly provides an interpretable representation of bond quality. Utility of noisy higher frequency FDTR phase maps, i.e., near the computationally predicted sensing depth limit, results in an average prediction error of 11%. Taken together, FDTR with neural network‐based analysis demonstrates subsurface bond monitoring at length scales relevant for heterogeneously integrated microelectronics.