Education – تعليم

In electronics, why is digital better than analog? by Khaled Alashmouny

Answer by Khaled Alashmouny:

Not sure why you think digital is better/superior than analog!
The truth is that almost anything you as a human interface with is analog by nature.
I would suggest to ask why convert natural signals to digital and do the processing there. Here are some quick reasons:
1. Noise: digital is 0/1 and susceptibility to noise is far less than analog
2. Scalability: digital can scale well with the technology. Analog does not scale as much (I can explain why later on)
3. Power: supply voltage scales with technology (getting slower though) also capacitance reduces as well.
4. Speed

All the process scaling is motivated therefore by the digital circuits

After processing you would need to convert back to analog (most of the time)  for us as humans to understand what’s going on

I hope this helps

In electronics, why is digital better than analog?

What is the best methodology to design different op-amps in nanometer technologies? by Khaled Alashmouny

Answer by Khaled Alashmouny:

I would break this question to multiple pieces and try to answer each piece.

1) Is the design methodology for opamps (or say analog circuits) that people used in deep-sub-um technologies any different than what people should use in latest nm technologies?


Not really. The physics did not change and the back-of-envelop calculations won’t change either. You will still consider second-order effects the same way you used to do it before.

In recent technologies you have more complex effects due to layout and technology restrictions, but it is very hard to use hand calculations for them. So, you would still understand their root cause, but you would rely on the foundry providing accurate model for these effects.

2) What are the challenges for analog (or opamp) design in these deep technologies?


There are some challenges and limitations due to the technology itself. This includes the layout-dependent effects mentioned before, the discrete dimensions designers should use, the poor intrinsic gain as you move from one technology to another, the limitations on the supply range you can use if your design requires high-voltage .. and so on. One key here is to make sure that you know exactly the region covered by your models and to be extra careful if your innovation is based on biasing devices in regions not covered by the model. While you can see the simulation results working, you will never get the performance if the model does not support your use case.

Another type of challenges is due to the fact that your opamp is in a nm technology for a reason. It won’t scale much in area is its fellow digital blocks. In this case, since the chip is almost dominant by digital circuits then you want to make sure the integration does not cause failure to your analog design. There can be further restrictions on the supply, extra guard-rings needed to isolate from digital noise, and many others that can be discovered when you work on full-chip solutions.

3) What methodology should you use?


Again, it is not really different from before. You should understand the limitations of models, the limitations of device choices, do the most simple and intuitive method for hand calculation, understand the devices limitations and try to do some characterization for each device in simulation by itself to get parameters such as gm/Id vs. Id, Ids vs. Vgs and Vds, and other simple relations with different device geometries. Understand when saturation occurs per device width. Check the definition of threshold voltage according to the foundry … etc. Extract the parameters to use them in your hand calculations to get the most accuracy out of it. Once you feel you are comfortable with your knowledge about the technology go ahead and do a simple design, say a differential amplifier. Try cascoding and see what gain do you gain out of it. See if this matches your previous device characterization for output impedance and intrinsic gain.

Finally, go ahead and do your design in small steps to understand how each piece work, then assemble parts together and run simulations. It will match your intuition and your understanding of physics and you will know at which direction you need to tweak current, device size, loading …etc

You are not done yet, you still have to check how PVT variations affects you. Later to make your design robust you need to consider reliability and aging effects.

This answer is not complete and it is not meant to be complete. Analog design comes from the understanding of basics and the amount of experience you build from different design and from always thinking about why or why not it may work. So, don’t be overwhelmed. Just get started and build strong basic and intuition. You will get there.

What is the best methodology to design different op-amps in nanometer technologies?

I am in my PhD 2nd year. Completed my proposal, courses. Now am focused on my research. What m… by Khaled Alashmouny

Answer by Khaled Alashmouny:

The first thing I would do is check with my advisor.

Read more papers and try to dig deeper in the history of the research you are working on. See how it started, who contributed mostly to your area, and what kind of other related problems that your research can solve.

In addition to research work, you may need to spend sometime to make yourself stronger in math, physics, and programming. This may sound trivial, but I can tell you that a lot of people overlook it.

Aside from the technical training and coursework, try to be an active volunteer in your department/school. In most Universities you are allowed to take one free course. Check the courses in Business School and try to take something relevant to entrepreneurship.

You can also start looking for summer internships relevant to your research. Improve your soft skills, time management, and writing skills.

The bottom line here is that time is very precious and you should not waste a moment.

Good luck!

I am in my PhD 2nd year. Completed my proposal, courses. Now am focused on my research. What more should I do?

When you write a statement of purpose, you need to imagine yourself a big success in the field. Then, THINK! what makes successful people successful in this area.

Let’s take an example, you are applying to school to get an MBA or a degree in Entrepreneurship. You have a tong passion, but passion should always come with knowledge. Otherwise, the admission office at the school would think that you did not REALLY do your homework.

So, what makes a successful Entrepreneur? Here are some example not in a particular order:
– Innovation?
– Valuing Diversity and Networking?
– Hard work and over-achievements?
– Fast learner and apply what is learned?
– Leadership?
– Result-driven, Objective-driven?
– Social?
– Internship or trainings that show maturity?

Think of some good merits ..  You can use LinkedIn and follow articles/groups written by successful people in your area and list those merits.

At the end, people would like to see that you “know”, you are “passionate”, and you have “delivered” something.

You can download my thesis from this link. This work was done under supervision of Prof. Euisik Yoon at the University of Michigan. In other posts in this website, I will try to summarize some of the topics covered in this work. Below you can read the abstract.



Understanding dynamics of the brain has tremendously improved due to the progress in neural recording techniques over the past five decades. The number of simultaneously recorded channels has actually doubled every 7 years, which implies that a recording system with a few thousand channels should be available in the next two decades. Nonetheless, a leap in the number of simultaneous channels has remained an unmet need due to many limitations, especially in the front-end recording integrated circuits (IC).

This research has focused on increasing the number of simultaneously recorded channels and providing modular design approaches to improve the integration and expansion of 3-D recording microsystems. Three analog front-ends (AFE) have been developed using extremely low-power and small-area circuit techniques on both the circuit and system levels. The three prototypes have investigated some critical circuit challenges in power, area, interface, and modularity.

The first AFE (16-channels) has optimized energy efficiency using techniques such as moderate inversion, minimized asynchronous interface for data acquisition, power-scalable sampling operation, and a wide configuration range of gain and bandwidth. Circuits in this part were designed in a 0.25μm CMOS process using a 0.9-V single supply and feature a power consumption of 4μW/channel and an energy-area efficiency of 7.51×1015 in units of J-1Vrms-1mm-2.

The second AFE (128-channels) provides the next level of scaling using dc-coupled analog compression techniques to reject the electrode offset and reduce the implementation area further. Signal processing techniques were also explored to transfer some computational power outside the brain. Circuits in this part were designed in a 180nm CMOS process using a 0.5-V single supply and feature a power consumption of 2.5μW/channel, and energy-area efficiency of 30.2×1015 J-1 Vrms-1mm-2.

The last AFE (128-channels) shows another leap in neural recording using monolithic integration of recording circuits on the shanks of neural probes. Monolithic integration may be the most effective approach to allow simultaneous recording of more than 1,024 channels. The probe and circuits in this part were designed in a 150 nm SOI CMOS process using a 0.5-V single supply and feature a power consumption of only 1.4μW/channel and energy-area efficiency of 36.4×1015 J-1Vrms-1mm-2, which is the highest reported efficiency to date.