Deep Multilabel CNN for Forensic Footwear Impression Descriptor Identification

In recent years deep neural networks have become the workhorse of computer vision. In this paper, we employ a deep learning approach to classify footwear impression's features known as \emph{descriptors} for forensic use cases. Within this process, we develop and evaluate an effective technique for feeding downsampled greyscale impressions to a neural network pre-trained on data from a different domain. Our approach relies on learnable preprocessing layer paired with multiple interpolation methods used in parallel. We empirically show that this technique outperforms using a single type of interpolated image without learnable preprocessing, and can help to avoid the computational penalty related to using high resolution inputs, by making more efficient use of the low resolution inputs. We also investigate the effect of preserving the aspect ratio of the inputs, which leads to considerable boost in accuracy without increasing the computational budget with respect to squished rectangular images. Finally, we formulate a set of best practices for transfer learning with greyscale inputs, potentially widely applicable in computer vision tasks ranging from footwear impression classification to medical imaging

Assembly of thin micro-chiplets using laser-induced forward transfer

Precise assembly and handling of thin micro-chiplets (i.e., thickness <100 μm) during heterogenous integration is quite challenging with very demanding requirements. Advanced packaging techniques are continuously been developed to cope up with the continuously increasing demands of semiconductor industries. Broadly, two techniques are commonly explored for mass transfer of micro-chiplets. First, the in-contact transfer printing which is quite mature technology and second, the noncontact laser-based mass transfer technique which is in its embryonic stage. These laser-based mass transfer techniques have added advantages of being non-contact, very selective and flexible with respect to the micro-chiplet’s dimensions and shape in addition to high transfer rates. In this research work, laser induced forward transfer (LIFT) of very thin micro-chiplets are presented. Additionally, the present study reports the effect of micro-chiplet’s edge chipping obtained by the conventional dicing process, and laser beam spot’s alignment on the transfer accuracy of micro-chiplets during LIFT printing for heterogeneous integration applications

Void Formation and Intermetallic Growth in Pulse Electrodeposited Cu-Sn Layers for MEMS Packaging

Electrodeposited copper (Cu)-tin (Sn) based solid-liquid interdiffusion (SLID) bonding is becoming popular in wafer-scale packaging of inertial Micro-Electro-Mechanical-Systems (MEMS) sensors due to its inherent advantages of lower cost, low processing temperatures and less stringent surface uniformity requirements. However, eliminating micron-size voids within intermetallic compounds (IMCs) and bond interfaces has remained a challenging task. The present study focuses upon IMC growth and void formation at varying temperatures and times. Stacks of varying thickness of Cu and Sn were fabricated by electrodeposition, and the samples were annealed at temperature ranging up to 300 degrees C. Scalloped shaped Cu6Sn5 (eta-phase) and comparatively uniform Cu3Sn (epsilon-phase) intermetallics were observed. Experimental results show that the growth of metastable Cu6Sn5 dominates IMC formation at lower temperatures but as temperature increases, Cu3Sn dominates over the Cu6Sn5 growth. This IMC growth transition from Cu6Sn5 dominant growth to Cu3Sn dominant growth depends on the annealing temperature and has a critical time duration. The IMC thicknesses are compared with those obtained by numerical simulation models. For given annealing temperatures, intermittent voids formed in the IMC layers show increasing size and decreasing void fraction trends with increasing annealing times. The results suggest that Cu-Sn SLID bonding performed at 275 degrees C yields reliable bonding since the void growth is minimal. Based on these results, a test vehicle containing a kelvin structure and daisy chains (having large number of Cu-Sn bonded structure), was fabricated, resulting in electrical resistances lower than 30 m-ohms and 6 ohms, respectively