Fayçal Saffih's career and research insights

FAYÇAL SAFFIH, https://www.linkedin.com/in/faycals
Email: fsaffih@uwaterloo.ca, fsaffih@gmail.com
Seminars channel: http://bit.ly/Seminars_Channel_FaycalS
Seminars Map: http://bit.ly/Seminars_Map_FaycalS
Fundamentals Micro-Electronics course: http://bit.ly/Microelectronics_Spring2017_FaycalS
VIP LAB, SYSTEMS DESIGN ENGINEERING DEPARTMENT, UNIVERSITY OF WATERLOO

• Bachelor: Solid-State Physics (Best Honors, Sétif, Algeria, 1996)
• Master: Digital-Implementation of Neural Networks (UM, KL, Malaysia, 1998)
• Ph. D.: Smart CMOS Image Sensors (Un. of Waterloo, Canada, 2005)
RESEARCH AREA: Intelligence-Design in Devices, Circuits and Systems
CAREER SPAN: Academical, Industrial & Entrepreneurial endeavors  www.linkedin.com/in/faycals

•Inspire young generations of researchersAim (Why):
•Tri-Lingual: English, Arabic and FrenchCarrier (How):
•Multi-disciplinary: Micro-electronics, Solid-state
Physics and EducationTopics (What):
Blended seminar

Many “Intelligence” definitions emerged since/before Greek civilisation to the current era:
 "I know that I am intelligent, because I know that I know nothing“, Socrates, 470BC-399BC
 Self-awareness  for Learning
…some of them contradictory!
"The true sign of intelligence is not knowledge but imagination.“, A. Einstein
Creativity  for Making & Designing
…has many types: analytic, linguistic, emotional…etc. most of which are related to Psychology
However, psychologists and neuroscientists disagree over whether these intelligences are linked or
whether they exist independently from one another
Multi-dimensional  Versatility!
The bottom Line!  There is No concensus on what is/constitutes intelligence.
This multifaceted identity is, excitingly, parallel to the wave-particle duality of the light nature!!
‫أحمد‬ ‫بن‬ ‫الخليل‬ ‫قال‬:‫أربعة‬ ‫الناس‬:‫عالم‬ ‫فذاك‬ ‫يدري‬ ‫انه‬ ‫ويدري‬ ‫يدري‬ ‫رجل‬‫عنه‬ ‫فخذوا‬
‫ناس‬ ‫فذاك‬ ‫يدري‬ ‫انه‬ ‫يدري‬ ‫ال‬ ‫وهو‬ ‫يدري‬ ‫ورجل‬‫فذكروه‬‫ال‬ ‫انه‬ ‫يدري‬ ‫وهو‬ ‫يدري‬ ‫ال‬ ‫ورجل‬
‫طالب‬ ‫فذاك‬ ‫يدري‬،‫فعلموه‬‫يدري‬ ‫ال‬ ‫انه‬ ‫يدري‬ ‫وال‬ ‫يدري‬ ‫ال‬ ‫ورجل‬‫فارفضوه‬ ‫أحمق‬ ‫فذاك‬.

SYSTEMS DESIGN ENGINEERING TEACHES THE STUDENT HOW TO ACQUIRE AND
INTEGRATE KNOWLEDGE ACROSS MULTIPLE DISCIPLINES. THE FRAMEWORK WE USE TO
DO THIS IS SYSTEMS THEORY THROUGH WHICH WE VIEW THE WORLD AS COMPRISING
SYSTEMS THAT INTERACT. EXAMPLES OF THE SYSTEMS WE MAY CONSIDER INCLUDE
HUMAN PHYSIOLOGICAL AND PSYCHOLOGICAL SYSTEMS, ECOLOGICAL SYSTEMS,
TRANSPORTATION SYSTEMS, COMMUNICATION SYSTEMS, ENERGY SYSTEMS AND
MECHATRONIC SYSTEMS. IT IS THROUGH SYSTEMS THINKING, MODELING AND ANALYSIS
THAT WE LEARN TO KNOW THE WORLD
Source: https://uwaterloo.ca/systems-design-engineering/about-systems-design-engineering/what-systems-design-engineering
“
”

1. Interconnected neural network: Intelligence is a faculty/ ‫ة‬َ‫ك‬َ‫ل‬َ‫م‬ /”dent”/talent/capacity that is inherently found in humans and
animals (and even any physical system in general!), surmised (inferred) responsible of problem solving (optimization): Heuristic
(non-algorithmic) Procession
2. Functionality Integration: Related to the fact that one organ (Brain) is responsible of processing/orchestrating many
functionality:
✓ Sensing: seeing, hearing…etc.
✓ Control: muscles, eyes, legs…etc.
✓ Signal processing and Feedback: playing (music, games) painting
 Example: iPhone!, all-in-one devices
3. Abstraction: Extracting ideas/knowledge from the physical world by Observation (Math, Physics origin) to solve problems!
4. Stratagem Intelligence (Systems Design Eng.) understand how the Universe Systems Interact and Mimic/Develop it 
5. Extracting “meaningful” information from physical observations: security application!
Origin:
late 15th century (originally denoting a military
ploy): from French stratagème, via Latin from
Greek stratēgēma, from stratēgein ‘be a
general,’ from stratēgos, from stratos ‘army’ +
agein ‘to lead.’ (Oxford Dictionaries)

“Nature does nothing uselessly!”, Politics (Book),
Aristotle, Greek philosopher, physicist, & zoologist (384 BC - 322 BC)
(https://en.wikiquote.org/wiki/Aristotle)
“The diversity of the phenomena of nature is so great, and the treasures
hidden in the heavens so rich, precisely in order that the human mind shall
never be lacking in fresh nourishment”, Cosmos (Book)
Johannes Kepler, German mathematician, astronomer and astrologer (physicist) (1571-1630)
(https://en.wikiquote.org/wiki/Johannes_Kepler)

The Need: To Face multi-faceted challenges of Engineering Design (Imaging as
an example)
The Known: Biological systems are proven over long time “of learning/adapting
from/to its environment to fit to their multi-faceted challenges.
The Solution: Bio-mimicry of biological systems’ intelligence and its
implementation into smart electronic systems at the system, circuit and device
level: Intelligence Design!
Application: Artificially Intelligent CMOS Imaging (AI2)

Ramon Cajal(1852-1934) identified the retina basic anatomical
structure. Shown here is his sketch of the interconnectivity
configurations of photocells rodes (f) and cones (e) [4]
The fovea is located near the center of
the macula. It is a small pit that contains
the largest concentration of cone cells

http://www.ibnalhaytham.com
This plane is where the
image sensor is placed

Charge Coupled Devices (CCD) [1970’s]
CCD pixel anatomy
http://www.siliconimaging.com/
commons.wikimedia.org
www.techbriefs.com
CMOS Image Sensing (CIS) [1990’s] CIS versus CCD
CCD Photo-Charge Transfer

CIS Vs CCD CIS Future Market Trend

CMOS Active Pixel Color Imaging Array
Digital Logic for
• User Interface
• Sensor Setup
• Timing Generator
• Digital Signal
Processing
–Color Processing
–White Balance
–Image Enhancement
• Data Output
Formatting
Analog Signal Processing
• Data Sampling
• Noise Reduction
• Gain
Analog-To-Digital Conversion
Source: www2.informatik.hu-berlin.de

1. AI2 System Level Implementation (Time Domain Fovea)
• Pyramidal CMOS Image Sensor
• Architecture
• Physical Design
• Scanning
• Foveated Dynamic Range Enhancement
• High-Speed Imaging of Pyramidal CMOS Imager
• Low Pyramidal Imager FPN Perception by HVS
2. AI2 Circuit Level Implementation (Spatial Domain Fovea)
• Multiresolution CMOS Image Sensor
• Architecture
• Multiresolution Active Pixel Sensor
• Results
3. AI2 Device Level Implementation
• Photopillar CMOS Image Sensors
• Biological Inspiration
• Photopillar Sensitivity Analysis and Biological verification

✓Time Domain Approach:
 Optical Dynamic Range (ODR) can be enhanced using multi-exposure technique
 Exposure = incident light (x) integration time
ODR topology can be created by varying integration time topology
✓Design Approach:
 Keep the standard APS structure while changing the Architectural Sampling structure & Timing
Pyramidal CMOS Image Sensor

Classical CMOS Imager Architecture Pyramidal CMOS Imager Architecture
1D-Row Sampling 2D-Ring Sampling
Vertical Busses Diagonal Busses

Layout image of the Pyramidal ImagerMicrographic image of the Pyramidal Imager

Rolling/raster scan versus Bouncing Scan

   splsssplsspls RTRTTRrTrTTTRrTin 22,,, 22

  splsplsspls TrTrTTTrTout 22,, 2


Pinned Foveated Dynamic Range Enhancement

Dynamic Range is usually
associated to the number of
“meaningful” bits of the digital
output of an imager.
Data volume of the imager
follows a Foveal distribution
3D view of the pinned FDR enhancement expressed in binary bits

+( )x½= ,
Demonstration of Foveated Dynamic Range Enhancement at the Peripheral Rings
Demonstration of Foveated Dynamic Range Enhancement at the Foveal Rings
+( )x½= ,
Inward scan Outward scan Fused Image Rolling scan
Inward scan Outward scan Fused Image Rolling scan

Oblique effect found in the contrast sensitivity of the HVS at relatively high frequenciesAverage spatial power spectrum
distribution of ~ 500 natural scenes

Pyramidal imager
Standard imager
2D-Fourier
Spectrum

✓Spatial Domain Approach
 Image Sampled at highest resolution in all pixels
 Only Regions-Of-Interest (ROI) are kept at their highest resolution
(no-charge distribution)
 Regions-Of-Less-Interest (ROLI) are selectively down-resolved by a
charge-sharing mechanism to get a single value representing their
average.
 Previous implementations were chip and column level approaches
using the same concept.
 The suggested approach is pixel-based to ensure the expandability of
the architecture to any imager size without increasing complexity.
✓Design Approach:
 Keep the standard sampling architecture and modifying APS structure
Multiresolution CMOS Image Sensor

Decoder for Column Select of
Correlated Double Sampling (CDS)
Multiresolution Decoder for
Row-Average Support & Column-Average
MultiresolutionDecoder
forRowresetandselect
MultiresolutionDecoderfor
Row-AverageandSampling
Output
Image
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
Output
Image
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
Output
Image
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
Output
Image
Output
Image
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
CDSBlock
Multiresolution Implementation

Multiresolution Active Pixel Sensor layout (left) and schematic (right)Multiresolution Active Pixel Sensor layout (left) and schematic (right)

https://www.google.com/patents/US7402789#npl-citations

A multiresolution image of
centric foveation
A multiresolution image of
random foveation
A multiresolution image with
a horizontal kernel averaging
A multiresolution image with
a vertical kernel averaging

Source: “Trends in IC Technology”, by Krishna Sarawat, Stanford University

CMOS Imaging established 3D sensors through Back-Side Illumination (BSI)

CCD imaging is fundamentally planar technology, whereas CMOS
counter part is flexible and is evolving toward 3D (ex. FinFet)

pathof
incident
light
pathof
incident
light
pathofincidentlightpathofincidentlight
pathofincidentlightpathofincidentlight
Biological Sensing
H

Fayçal Saffih, Amro M. Elshurafa,M. A. Mohammad, N. N. Fitzpatrick, S. Evoy, “Fabrication of Bio-mimetic CMOS-Compatible
Nanopillars for Next-Generation Photon Sensors”, International IEEE NEWCAS Conference, Montreal, Canada, June 2012.

Biological Proof: Bullfrogs and Haplochromis burtoni [fish] control their
vision sensitivity by the contraction/expansion of their cone photocells.

CMOS Image sensor Readout Integrated Circuitry (ROIC) + AC-DC conversion + Energy Storage
Silicon substrate
Si-glassPads Pads
Photo-Energy Harvesting
Photo-Signal Sensing
Biomedical: Wireless Endoscopic Imaging Camera phone Imaging Automotive Imaging
SensArvesting© CMOS Imaging
Patent: "Bio-inspired Nanostructures for Implementing Vertical PN-junction", US patent. January 29, 2010. Reference numbers WO2011092601. (PCT/IB2011/000432)

As planar CMOS imaging spun from modified CMOS fabrication, it is also
possible to make other modifications to for Vertical CMOS Imaging.
Biological Intelligent Imaging is inspiring us to suggest advanced imaging
devices and architectures to expand the limits of current E-Imaging.
Current major CMOS imaging technology are:
1.Front-Side-illumination (FSI) is loosing ground due lack of quality at HR
2.Back-Side-Illumination (BSI) is gaining ground but more expensive 
→Now Smart-Imaging Technologies are needed and we suggested it herein

 PATENTS (List of Inventors: Fayçal Saffih)
"Bio-inspired Nanostructures for Implementing Vertical PN-junction", US patent.
January 29, 2010. Reference numbers WO2011092601. (PCT/IB2011/000432)
 BOOK
Fayçal Saffih, “Smart CMOS Imaging Architectures: System and Device level
implementations of Smart CMOS imaging”, Lap-Lambert Academic
Publishing, ISBN 978-3-659-42811-1, 2014
 PUBLICATIONS
1. Fayçal Saffih, Ferhat Aydinoglu, Bo Cui, “Chromium oxide as a hard mask
material better than metallic chromium”, Journal of Vacuum Science &
Technology B, American Institute of Physics (AIP), Vol. 35, issue 6, p. 2166-
2746, November 2017
2. Fayçal Saffih, Asma Ayari-Kanoun, Ferhat Aydinoglu, Bo Cui, “Silicon
nanostructures with very large negatively tapered profile by ICP-RIE”,
Journal of Vacuum Science & Technology B, American Institute of Physics
(AIP), Vol. 34, p. 2166-2746, 06KD01, (2016).
3. Faycal Saffih, Celal Con, Alanoud Alshammari, Mustafa Yavuz, Bo Cui,
“Fabrication of silicon nanostructures with large taper angle by reactive
ion etching”, Journal of Vacuum Science & Technology B, American
Institute of Physics (AIP), Volume 32, Issue 6, November 2014.
4. Fayçal Saffih, Richard Hornsey, “Foveated Dynamic Range of the
Pyramidal CMOS Image Sensors”, IEEE Transactions on Electron Devices,
Vol. 54, N° 12, December 2007, pp. 3422-3425
5. Fayçal Saffih, Richard Hornsey, “Pyramidal CMOS Imager FPN noise
Reduction through Human Visual System Perception” IEEE Transactions on
Circuits and Systems for Video Technology, Vol. 17, Issue: 7, July 2007, pp.
924-930.
6. Faycal Saffih, Alanoud Alshammari, Mustafa Yavuz, Bo Cui
“Fabrication of silicon nanostructures with large taper angle by
reactive ion etching”, 58th International Conference on Electron, Ion
and Photon Beam Technology and Nanofabrication. Washington DC,
2014.
7. Fayçal Saffih, R. Hornsey, “Functional Integration for Smart CMOS
Imagers: Dynamic Range Enhancement & Gamma Correction”, The
2013 Canadian Conference on Electrical and Computer Engineering,
Regina, Canada in May 2013
8. Fayçal Saffih, Amro M. Elshurafa,M. A. Mohammad, N. N. Fitzpatrick, S.
Evoy, “Fabrication of Bio-mimetic CMOS-Compatible Nanopillars for
Next-Generation Photon Sensors”, International IEEE NEWCAS
Conference, Montreal, Canada, June 2012.
9. F. Saffih, B. Cui, S. Evoy, N. N. Fitzpatrick, M. A. Mohammad, S. Evoy,
“Bio-inspired Nano-Photodiode for Low Light, High Resolution and
Crosstalk-Free CMOS Image Sensing”, IEEE International Symposium on
Circuits and Systems, ISCAS 2011, Rio de Janeiro, 15-18 May 2011, pp.
797-800
10. Fayçal Saffih, Richard Hornsey, Hugh R. Wilson, “Human perception of
fixed pattern noise in pyramidal CMOS image sensor”, Proceedings of
SPIE Vol. 5578, pp. 400-410, September 2004, Ottawa, Ontario,
Canada.
11. ……

1. How to Engage students in their learning process, experience and journey?
2. What is the objective of teaching/education (other than getting better job ☺)?
3. Do grades check (or reflects) “memory recall efficiency” or “knowledge understanding” ?
4. Does teaching need to be a Master/Slave approach or an Intrinsic Self-driven approach?
APPLY INTELLIGENCE ENGINEERING (IE) IN TEACHING AND EDUCATION

http://bit.ly/Dr_Faycal-Saffih_CETL-Panel-Discussion_Nov17-2016

http://bit.ly/Teaching_Philosophy_FaycalS

 The dynamic process of grasping the knowledge of a specific discipline
 The area whereby the “student factor” is “central”
Student Psychology is pivotal (by learning by example, experiential,…etc.)
Online/offline (Blended learning)
Self-awareness!

Multidisciplinary Approach: Hebbian Associative Learning*: STEM:
Multi-Disciplinary Learning

Math: Tech:
*: https://en.wikibooks.org/wiki/Artificial_Neural_Networks/Hebbian_Learning
http://bit.ly/Vo-T_Math_FaycalS http://bit.ly/Vo-T_Lab_FaycalS

Multichannel Approach (Associative Memory Artificial Intelligence):
Multi-Modal Learning: In-class & off-class
The learning concept
The learning moment

Pre-midterm exam AIM method assessment feedback
6.7%
3.3%
Pre-final exam AIM method assessment feedback

DETAILED REPORT: HTTP://BIT.LY/FM_FALL2016-STUDENT_EVALUATION
http://bit.ly/SMARTeaching_Eval_FaycalS
HTTP://BIT.LY/AIM_FOR_STEM_FAYCALS
To download the paper: http://bit.ly/AIM4STEM_paper_FaycalS

http://bit.ly/My_Student_SelfDriven_FaycalS

Biological Intelligent systems are inspirational to improve Engineering Education
Students Psychology is an important (if not the most important!) factor in this regard
Intelligence, as a natural power embedded in biology, should be used in education by:
By enhancing the Self-Awareness aspect: using experiential learning, self-assessment, feedback…etc.
By interconnecting various engineering/science streams to enhance the “convincing Power” of learning (STEM)
By blending various teaching modalities (digital and others) to improve the recalling aspect of education.

 Intelligence is Pervasive & Ubiquitous in the Universe at every level namely: Biological
and Physical systems
Why?: Biological systems are time-adapted/evolved/tested and Physical systems follows
the least action principle
 Intelligence can be extracted from Nature (like abstract Math) and Implemented
into various technologies from Nanotechnology (currently) to Internet-of-Thing (IoT) (in
progress) and beyond!
 This track can be very beneficial to boost both Fundamental Science and Advanced
Intelligent Electronics which is driving the global semiconductor economy.

Fayçal Saffih's career and research insights

Fayçal Saffih's career and research insights

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