Visible to the public Biblio

Filters: Keyword is elemental semiconductors  [Clear All Filters]
Lisec, Thomas, Bodduluri, Mani Teja, Schulz-Walsemann, Arne-Veit, Blohm, Lars, Pieper, Isa, Gu-Stoppel, Shanshan, Niekiel, Florian, Lofink, Fabian, Wagner, Bernhard.  2019.  Integrated High Power Micro Magnets for MEMS Sensors and Actuators. 2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems Eurosensors XXXIII (TRANSDUCERS EUROSENSORS XXXIII). :1768–1771.
Back-end-of-line compatible integration of NdFeB-based micro magnets onto 8 inch Si substrates is presented. Substrate conditioning procedures to enable further processing in a cleanroom environment are discussed. It is shown that permanent magnetic structures with lateral dimensions between 25μm and 2000μm and a depth up to 500μm can be fabricated reliably and reproducibly with a remanent magnetization of 340mT at a standard deviation as low as 5% over the substrate. To illustrate post-processing capabilities, the fabrication of micro magnet arrangements embedded in silicon frames is described.
Ye, J., Yang, Y., Gong, Y., Hu, Y., Li, X..  2018.  Grey Zone in Pre-Silicon Hardware Trojan Detection. 2018 IEEE International Test Conference in Asia (ITC-Asia). :79-84.

Pre-Silicon hardware Trojan detection has been studied for years. The most popular benchmark circuits are from the Trust-Hub. Their common feature is that the probability of activating hardware Trojans is very low. This leads to a series of machine learning based hardware Trojan detection methods which try to find the nets with low signal probability of 0 or 1. On the other hand, it is considered that, if the probability of activating hardware Trojans is high, these hardware Trojans can be easily found through behaviour simulations or during functional test. This paper explores the "grey zone" between these two opposite scenarios: if the activation probability of a hardware Trojan is not low enough for machine learning to detect it and is not high enough for behaviour simulation or functional test to find it, it can escape from detection. Experiments show the existence of such hardware Trojans, and this paper suggests a new set of hardware Trojan benchmark circuits for future study.

Ziegler, A., Luisier, M..  2017.  Phonon confinement effects in diffusive quantum transport simulations with the effective mass approximation and k·p method. 2017 International Conference on Simulation of Semiconductor Processes and Devices (SISPAD). :25–28.

Despite the continuous shrinking of the transistor dimensions, advanced modeling tools going beyond the ballistic limit of transport are still critically needed to ensure accurate device investigations. For that purpose we present here a straight-forward approach to include phonon confinement effects into dissipative quantum transport calculations based on the effective mass approximation (EMA) and the k·p method. The idea is to scale the magnitude of the deformation potentials describing the electron-phonon coupling to obtain the same low-field mobility as with full-band simulations and confined phonons. This technique is validated by demonstrating that after adjusting the mobility value of n- and p-type silicon nanowire transistors, the resulting EMA and k·p I-V characteristics agree well with those derived from full-band studies.

Keeler, G. A., Campione, S., Wood, M. G., Serkland, D. K., Parameswaran, S., Ihlefeld, J., Luk, T. S., Wendt, J. R., Geib, K. M..  2017.  Reducing optical confinement losses for fast, efficient nanophotonic modulators. 2017 IEEE Photonics Society Summer Topical Meeting Series (SUM). :201–202.

We demonstrate high-speed operation of ultracompact electroabsorption modulators based on epsilon-near-zero confinement in indium oxide (In$_\textrm2$$_\textrm3$\$) on silicon using field-effect carrier density tuning. Additionally, we discuss strategies to enhance modulator performance and reduce confinement-related losses by introducing high-mobility conducting oxides such as cadmium oxide (CdO).

Yang, L., Murmann, B..  2017.  SRAM voltage scaling for energy-efficient convolutional neural networks. 2017 18th International Symposium on Quality Electronic Design (ISQED). :7–12.

State-of-the-art convolutional neural networks (ConvNets) are now able to achieve near human performance on a wide range of classification tasks. Unfortunately, current hardware implementations of ConvNets are memory power intensive, prohibiting deployment in low-power embedded systems and IoE platforms. One method of reducing memory power is to exploit the error resilience of ConvNets and accept bit errors under reduced supply voltages. In this paper, we extensively study the effectiveness of this idea and show that further savings are possible by injecting bit errors during ConvNet training. Measurements on an 8KB SRAM in 28nm UTBB FD-SOI CMOS demonstrate supply voltage reduction of 310mV, which results in up to 5.4× leakage power reduction and up to 2.9× memory access power reduction at 99% of floating-point classification accuracy, with no additional hardware cost. To our knowledge, this is the first silicon-validated study on the effect of bit errors in ConvNets.

Perez, R..  2015.  Silicon systems security and building a root of trust. 2015 IEEE Asian Solid-State Circuits Conference (A-SSCC). :1–4.

This paper briefly presents a position that hardware-based roots of trust, integrated in silicon with System-on-Chip (SoC) solutions, represent the most current stage in a progression of technologies aimed at realizing the most foundational computer security concepts. A brief look at this historical progression from a personal perspective is followed by an overview of more recent developments, with particular focus on a root of trust for cryptographic key provisioning and SoC feature management aimed at achieving supply chain assurances and serves as a basis for trust that is linked to properties enforced in hardware. The author assumes no prior knowledge of these concepts and developments by the reader.