Power spectrum analysis (PSA) is a fast, non-destructive, sensitive method for examining commercial off-the-shelf ( COTS ) electronic components. These features make PSA attractive for both component screening and surveillance in support of component reliability efforts. Current analysis methods limit the utility of PSA due to the need to manually examine the results of analysis to identify anomalous parts. This study demonstrates the development and application of a workflow to automate the screening of COTS electronic components. Further, this study demonstrates the use of multivariate algorithms to assess aging of Zener diodes. These workflows can be readily extended to other components, combining the benefits of PSA and multivariate analysis to screen and evaluate COTS electronic components.
Power Spectrum Analysis (PSA) is a Sandia-developed, non-intrusive, electrical technique that captures distinct frequency-domain signatures of microelectronics devices using an innovative, unconventional biasing scheme (off-normal biasing). PSA can identify subtle differences in devices and is applicable in various areas such as device screening, counterfeit identification, reliability assurance, and trust authentication. From October 2020 to April 2021, Sandia worked with entrepreneurs from a new start-up company, Chiplytics, to commercialize PSA technology through NNSA-sponsored FedTech Program. In September 2021, Sandia received funding through Covid-19 Technical Assistance Program (CTAP) to provide technical assistance to Chiplytics for commercialization. Under the CTAP Statement of Work, Sandia was tasked with providing technical assistance to Chiplytics in PSA pilot testing for Naval Surface Warfare Center (NSWC) at Crane and other pilot participants. Sandia was also tasked with assisting Chiplytics in hardware development and evaluation of Chiplytics prototype system.
The ability to localize defects in order to understand failure mechanisms in complex superconducting electronics circuits, while operating at low temperature, does not yet exist. This work applies thermally-induced voltage alteration (TIVA), to a biased superconducting electronics (SCE) circuit at ambient temperature. TIVA is a commonly used, laser-based failure analysis technique developed for silicon-based microelectronics. The non-operational circuit consisted of an arithmetic logic unit (ALU) in a high-frequency test bed designed at HYPRES and fabricated by MIT Lincoln Laboratory using their SFQ5ee process. Localized TIVA signals were correlated with reflected light images at the surface, and these sites were further investigated by scanning electron microscopy imaging of focused ion-beam cross-sections. The areas investigated, where prominent TIVA signals were observed, showed seams in the Nb wiring layers at contacts to Josephson junctions or inductors and/or disrupted junction morphologies. These results suggest that the TIVA technique can be used at ambient temperature to diagnose fabrication defects that may cause low temperature circuit failure.
We present a new, non-destructive electrical technique, Power Spectrum Analysis (PSA). PSA as described here uses off-normal biasing, an unconventional way of powering microelectronics devices. PSA with off-normal biasing can be used to detect subtle differences between microelectronic devices. These differences, in many cases, cannot be detected by conventional electrical testing. In this paper, we highlight PSA applications related to aging and counterfeit detection.
Laser-based failure analysis techniques demonstrate the ability to quickly and non-intrusively screen deep ultraviolet light-emitting diodes (LEDs) for electrically-active defects. In particular, two laser-based techniques, light-induced voltage alteration and thermally-induced voltage alteration, generate applied voltage maps (AVMs) that provide information on electrically-active defect behavior including turn-on bias, density, and spatial location. Here, multiple commercial LEDs were examined and found to have dark defect signals in the AVM indicating a site of reduced resistance or leakage through the diode. The existence of the dark defect signals in the AVM correlates strongly with an increased forward-bias leakage current. This increased leakage is not present in devices without AVM signals. Transmission electron microscopy analysis of a dark defect signal site revealed a dislocation cluster through the pn junction. The cluster included an open core dislocation. Even though LEDs with few dark AVM defect signals did not correlate strongly with power loss, direct association between increased open core dislocation densities and reduced LED device performance has been presented elsewhere [M. W. Moseley et al., J. Appl. Phys. 117, 095301 (2015)].
Microsystems-enabled photovoltaics (MEPV) can potentially meet increasing demands for light-weight, portable, photovoltaic solutions with high power density and efficiency. The study in this report examines failure analysis techniques to perform defect localization and evaluate MEPV modules. CMOS failure analysis techniques, including electroluminescence, light-induced voltage alteration, thermally-induced voltage alteration, optical beam induced current, and Seabeck effect imaging were successfully adapted to characterize MEPV modules. The relative advantages of each approach are reported. In addition, the effects of exposure to reverse bias and light stress are explored. MEPV was found to have good resistance to both kinds of stressors. The results form a basis for further development of failure analysis techniques for MEPVs of different materials systems or multijunction MEPVs. The incorporation of additional stress factors could be used to develop a reliability model to generate lifetime predictions for MEPVs as well as uncover opportunities for future design improvements.