Eeprom As An Analog Storage Device, With Particular Applications In Neural Networks

IEEE Transactions on Electron Devices

Volumn 39, Issue 6, 1992, Pages 1410-1419

  Kramer, Alan ^b   Hu, V ^b   Chu, Robert R ^b   Ko, Ping K ^b  

^a INTEL CORPORATION   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0001221385     PISSN: 00189383     EISSN: 15579646     Source Type: Journal    
DOI: 10.1109/16.137321     Document Type: Article

References (7)

1. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” Proc. of Nat. Acad. Sci. USA, vol. 79, pp. 2554–2558, 1982.
2. J. Alspectore et. al., “A neuromorphic VLSI learning system,” in Proc. 1987 Stanford Conf. on Advanced Research in VLSI, 1987, pp. 313–349.
3. S. M. Sze, Physics of Semiconductor Devices, 2nd ed. New York: Wiley, 1981.
4. M. Jeng et al., “A deep-submicrometer MOSFET model for analog/digital circuit simulations,” in IEDM Tech. Dig., 1988, pp. 114–117.
5. C. Sodini et al., “The effect of high fields on MOS device and circuit performance,” IEEE Trans. Electron Devices, vol. ED-31, no. 10, p. 1386, 1984.
6. R. Muller and T. Kamins, Device Electronics for Integrated Circuit, 2nd ed. New York: Wiley, 1986.
7. A. Kramer et al., “Compact EEPROM-base weight functions,” in Advances in Neural Information Processing Systems 3. San Mateo, CA: Morgan Kaufmann, 1991, pp. 1001–1007.