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Technical Seminar


Digitally-Assisted Analog Circuits for a 10 Gbps, 395 fJ/b Optical Receiver in 40 nm CMOS

DATE/TIME  Friday, March 16, 2012 (10:00am to 11:00am)
PLACE  AMD Fort Collins Campus (Fort Collins, CO)
DIRECTIONS

From I-25, take Harmony Road Exit (Exit 265) westbound, and enter AMD campus on right immediately following Harmony/Ziegler intersection.  AMD is located on the NW corner of Harmony Road and Ziegler Road.  Proceed to 3rd floor for escort to seminar auditorium.  Non-AMD employees:  please arrive at 9:45am for security sign-in and escort.

COST    Free.  As always, food & drinks will be provided.
RSVP    Send e-mail to https://gomartin.net/sscs/2012/rsvp_2012_03_16.htm

ABSTRACT
This presentation is an extended encore of a contributed paper delivered at the 2011 Asian Solid-State Circuits Conference (A-SSCC) in Jeju, Korea.

Digital “assist” circuits can improve the efficiency of traditionally analog circuit blocks, especially as technologies scale to the detriment of analog blocks. We apply some of these techniques to a 10 Gbps optical reciever, and demonstrate 395 fJ/b energy efficiency. Digital calibration blocks wrapped around a simple analog core enabled offset compensation, TIA biasing, and DLL re-timing, and cost negligible performance and power overhead. The assist circuits cost around 40% area overhead.

PRESENTATION SLIDES  pdf (IEEE copyright)
REFERENCE  A-SSCC paper (IEEE copyright)

PHILIP AMBERG (Oracle Labs, Redwood Shores, CA)

Philip Amberg received his BS Engineering degree from Harvey Mudd College, Claremont, CA in 2008.  After graduating, he joined the VLSI Research Group at Sun Microsystems Laboratories, Menlo Park, CA, which is now Oracle Labs. During this time, he enrolled in the MSEE program at Stanford University and graduated in January 2012 with an emphasis in analog and digital circuit design. At Sun/Oracle, he has worked with chip-to-chip I/O technologies such as Proximity Communication and optics.  He has also experimented with low-power database processing for large scale analytics.