CoE/ECE/EE 196

From Microlab Classes
Revision as of 11:32, 6 July 2022 by Ryan Antonio (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search

Paper Readings for CoE/ECE/EE 196

Below is a list of paper readings for possible topics in Microlab. The list mostly consists of general reading and survey papers. Some also have lectures or webinars. Most of the links here are only accessible using your EEE account and the school's VPN.

General Reading

All students must at least read the general readings. No need to be meticulous in details but at least understand where and how the field of microelectronics is progressing.

  • Cavin, R.K.; Lugli, P.; Zhirnov, V.V.; , “Science and Engineering Beyond Moore’s Law,” Proceedings of the IEEE , vol.100, no. Special Centennial Issue, pp.1720-1749, May 13 2012 (URL)
  • L. Xiu, "Time Moore: Exploiting Moore's Law From The Perspective of Time," in IEEE Solid-State Circuits Magazine, vol. 11, no. 1, pp. 39-55, Winter 2019, doi: 10.1109/MSSC.2018.2882285. (URL)

Low Voltage Circuit Design

  • Dreslinski, R.G.; Wieckowski, M.; Blaauw, D.; Sylvester, D.; Mudge, T.; , “Near-Threshold Computing: Reclaiming Moore’s Law Through Energy Efficient Integrated Circuits,” Proceedings of the IEEE , vol.98, no.2, pp.253-266, Feb. 2010 (URL)
  • Kinget, P.R.; , “Designing analog and RF circuits for ultra-low supply voltages,” Solid State Circuits Conference, 2007. ESSCIRC 2007. 33rd European , vol., no., pp.58-67, 11-13 Sept. 2007 (URL)
  • Kinget, P.; Chatterjee, S.; Tsividis, Y.; , “Ultra-Low Voltage Analog Design Techniques for Nanoscale CMOS Technologies,” Electron Devices and Solid-State Circuits, 2005 IEEE Conference on , vol., no., pp. 9- 14, 19-21 Dec. 2005 (URL)

Digitally Assisted Analog Circuits

  • Murmann, B., “Digitally Assisted Analog Circuits,” Micro, IEEE , vol.26, no.2, pp.38,47, March-April 2006 (URL)
  • Murmann, B., “Digitally assisted data converter design,” ESSCIRC (ESSCIRC), 2013 Proceedings of the , vol., no., pp.24,31, 16-20 Sept. 2013 (URL)
  • Murmann, B., “A/D converter trends: Power dissipation, scaling and digitally assisted architectures,” Custom Integrated Circuits Conference, 2008. CICC 2008. IEEE , vol., no., pp.105,112, 21-24 Sept. 2008 (URL)

Spintronics

  • Dieny, B., Prejbeanu, I.L., Garello, K.; et al.; , “Opportunities and challenges for spintronics in the microelectronics industry,” Nat Electron 3, 446–459 (2020) (URL 1, URL 2)
  • Bhatti, S.; Sbiaa, R.; Hirohata, A.; et al.; , “Spintronics based random access memory: a review,” Materials Today, vol. 20, no. 9, pp. 530-548, Nov. 2017 (URL)
  • Jain, S.; Sapatnekar, S.; Wang, J.; Roy, K.; Raghunathan, A.; , “Computing-in-memory with spintronics,” 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), Dresden, 2018, pp. 1640-1645, Mar. 2018. (URL 1, URL 2)
  • Grollier, J.; Querlioz, D.; Stiles, M.D. ; , “Spintronic Nanodevices for Bioinspired Computing,” Proceedings of the IEEE, vol. 104, no. 10, pp. 2024-2039, Oct. 2016 (URL 1, URL 2)
    • TEDx Talk by Grollier, J. (2018, Jan 25). How artificial nano-neurons can fix computers’ energy addiction? | Julie Grollier | TEDxSaclay. TEDx Talks.
  • Seminar by Stiles, M. D. (2020, Aug 12). Using magnetic tunnel junctions to compute like the brain. Online SPICE-SPIN+X Seminars.

Wireless Sensor Networks

  • Chen, G.; Hanson, S.; Blaauw, D.; Sylvester, D.; , “Circuit Design Advances for Wireless Sensing Applications,” Proceedings of the IEEE , vol.98, no.11, pp.1808-1827, Nov. 2010 (URL)
  • M. A. Alsheikh, S. Lin, D. Niyato and H. Tan, “Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications,” in IEEE Communications Surveys & Tutorials, vol. 16, no. 4, pp. 1996-2018, Fourthquarter 2014, doi: 10.1109/COMST.2014.2320099. (URL)

Microelectromechanical Systems (MEMS)

  • W. L. Engl, H. K. Dirks and B. Meinerzhagen, "Device modeling," in Proceedings of the IEEE, vol. 71, no. 1, pp. 10-33, Jan. 1983, doi: 10.1109/PROC.1983.12524. (URL)
  • Algamili, A.S., Khir, M.H.M., Dennis, J.O. et al. "A Review of Actuation and Sensing Mechanisms in MEMS-Based Sensor Devices," Nanoscale Res Lett 16, 16 (2021).(URL)
  • Zhao, D., Wang, Y., Shao, J., Zhang, P., Chen, Y., Fu, Z., Wang, S., Zhao, W., Zhou, Z., Yuan, Y., Fu, D., & Zhu, Y. (2021). "Temperature and humidity sensor based on MEMS technology," AIP Advances, 11(8), 085126. (URL)
  • Vladimír Kutiš, Jaroslav Dzuba, Juraj Paulech, Justín Murín, Tibor Lalinský, "MEMS Piezoelectric Pressure Sensor-modelling and Simulation," Procedia Engineering, Volume 48, 2012, Pages 338-345, ISSN 1877-7058,(URL)

Hyperdimensional Computing (HDC)

  • Kanerva, P. (2009). "Hyperdimensional computing: An introduction to computing in distributed representation with high-dimensional random vectors, " Cognitive Computation, 1(2), 139–159. https://doi.org/10.1007/s12559-009-9009-8 (URL)
  • Rahimi, A., Datta, S., Kleyko, D., Frady, E. P., Olshausen, B., Kanerva, P., & Rabaey, J. M. (2017). "High-Dimensional Computing as a Nanoscalable Paradigm," IEEE Transactions on Circuits and Systems I: Regular Papers, 64(9), 2508–2521. (URL)
  • Rahimi, A., Kanerva, P., Benini, L., & Rabaey, J. M. (2019). "Efficient Biosignal Processing Using Hyperdimensional Computing: Network Templates for Combined Learning and Classification of ExG Signals,". Proceedings of the IEEE, 107(1), 123–143. (URL)
  • Ge, L., & Parhi, K. K. (2020). "Classification Using Hyperdimensional Computing: A Review," IEEE Circuits and Systems Magazine, 20(2), 30–47. (URL)
  • Hassan, E., Halawani, Y., Mohammad, B., & Saleh, H. (2021). "Hyper-Dimensional Computing Challenges and Opportunities for AI Applications," IEEE Access, 1–15. (URL)

Machine Learning, Deep Learning, Neural Networks, Spiking Neural Networks

Warning: These topics might be too big or too complex but if it interests you. Why not?

  • B. Murmann, "Mixed-Signal Computing for Deep Neural Network Inference," in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 29, no. 1, pp. 3-13, Jan. 2021, doi: 10.1109/TVLSI.2020.3020286. (URL)
  • Maxence Bouvier, Alexandre Valentian, Thomas Mesquida, Francois Rummens, Marina Reyboz, Elisa Vianello, and Edith Beigne. 2019. "Spiking Neural Networks Hardware Implementations and Challenges: A Survey,". J. Emerg. Technol. Comput. Syst. 15, 2, Article 22 (April 2019), 35 pages. (URL)
  • Reuther, Albert & Michaleas, Peter & Jones, Michael & Gadepally, Vijay & Samsi, Siddharth & Kepner, Jeremy. (2019). "Survey and Benchmarking of Machine Learning Accelerators,". 1-9. 10.1109/HPEC.2019.8916327. (URL)