𝟏. 𝐁𝐞 𝐚 𝐠𝐨𝐨𝐝 𝐫𝐨𝐥𝐞 𝐦𝐨𝐝𝐞𝐥: Your PhD students follow you in many ways – how you approach a research problem, how you present, how you collaborate, and so on. Subconsciously, the students inherit many research traits from you. Be a great model to produce great researchers.
𝟐. 𝐋𝐞𝐚𝐝 𝐛𝐲 𝐞𝐱𝐚𝐦𝐩𝐥𝐞: Lead your team by example. If you expect the student to be on time for a meeting, you should be on time too. Similarly, if you expect the student to be well-prepared for the meeting or presentation, so should you be. This will give an impression to the student that you have a keen interest in his/her PhD.
𝟑. 𝐒𝐡𝐨𝐰 𝐯𝐚𝐥𝐮𝐞: Instead of asking the student to do a task, show him/her the value of the assigned task. For example, doing task A will help you get this skill and lead to a publication too. This way the student is more likely to do the task in an effective way.
𝟒. 𝐁𝐞 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜: Generic guidance can leave a student in confusion instead of putting him/her on a concrete path. In fact, a student can get generic guidance from anywhere. Be concrete and specific, especially in the first year. Also, give the student the confidence to comfortability asks for any clarifications.
𝟓. 𝐋𝐨𝐨𝐤 𝐢𝐧𝐰𝐚𝐫𝐝 𝐭𝐨𝐨: If a student is not progressing, the supervisor should look inward too. Assess in what other ways you can help the student to progress.
𝟔. 𝐍𝐨𝐭 𝐞𝐯𝐞𝐫𝐲 𝐬𝐭𝐮𝐝𝐞𝐧𝐭 𝐢𝐬 𝐀𝐥𝐛𝐞𝐫𝐭 𝐄𝐢𝐧𝐬𝐭𝐞𝐢𝐧: Do not expect the same kind of excellence and productivity from every student. Every student is different – different IQ levels, different personal circumstances, and so on. Do not compare them with yourself either.
𝟕. 𝐋𝐞𝐚𝐯𝐞 𝐰𝐢𝐭𝐡 𝐚𝐜𝐭𝐢𝐨𝐧 𝐩𝐨𝐢𝐧𝐭𝐬: A meeting with your student should end with concrete action points. If a student leaves the meeting in an increased state of confusion, the meeting has served no purpose. One effective way is that the student shares the meeting minutes and action points with the supervisor.
𝟖. 𝐄𝐧𝐜𝐨𝐮𝐫𝐚𝐠𝐞 𝐭𝐨 𝐝𝐞𝐯𝐞𝐥𝐨𝐩 𝐬𝐨𝐟𝐭 𝐬𝐤𝐢𝐥𝐥𝐬: Encourage your student to take part in activities that develop the student’s soft skills such as communication, presentation, networking, and so on. Of course, this should not be at the cost of primary research. Keep track of the trade-offs if any.
𝟗. 𝐏𝐫𝐨𝐯𝐢𝐝𝐞 𝐭𝐢𝐦𝐞𝐥𝐲 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤: PhD students are often short of time, especially at the end of the PhD. Whilst you are very busy, try to give timely feedback, especially on papers. This will help them to complete their PhD on time.
𝟏𝟎. 𝐁𝐞 𝐤𝐢𝐧𝐝: Being kind is a very rare trait – try to be one. We don’t know exactly what the other person is exactly going through. Put yourself in the shoes of the student. Try to understand the student’s concerns and support him/her in whatever way possible.
Here are 10 traits of highly effective PhD students:
𝟏. 𝐂𝐮𝐫𝐢𝐨𝐮𝐬: A good PhD student is curious about the various aspects of his/her research. The student is eager to find answers to what, why, how, and when. Anything not known triggers him/her to dig deeper and find it out. The student asks high-quality questions.
2. 𝐎𝐩𝐞𝐧 𝐭𝐨 𝐝𝐢𝐬𝐜𝐮𝐬𝐬𝐢𝐨𝐧𝐬: An effective PhD student does not restrict oneself to his/her cabin. The student discusses the ideas with supervisors, lab mates, and other relevant people. The student is willing to provide feedback and happy to take feedback.
3. 𝐂𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐭𝐡𝐢𝐧𝐤𝐞𝐫: A good PhD student develops the ability of critical thinking early in PhD journey. He/she reflects on research pieces to determine what are its strengths and weaknesses, how we can improve the weaknesses, where are the gaps, and so on.
4. 𝐎𝐩𝐞𝐧 𝐭𝐨 𝐜𝐨𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐯𝐞 𝐜𝐫𝐢𝐭𝐢𝐜𝐢𝐬𝐦: An effective PhD student does not take criticism of his/her research personally. He/she reflects on the criticism to understand how the research can be improved. He/she is not afraid of putting his/her research in front of others just because of the fear of criticism.
5. 𝐑𝐞𝐬𝐩𝐞𝐜𝐭𝐟𝐮𝐥: A good PhD student is respectful to his/her supervisors, lab mates, other researchers, and their research. He/she is able to communicate their points in a respectful manner. He/she does not boost his/her own research nor does undermine other people’s research
6. 𝐆𝐫𝐞𝐚𝐭 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐨𝐫: A good PhD student is an excellent communicator. He/she can communicate research in different ways such as through research papers, presentations, posters, and so on. He/she can communicate complex concepts in simple words
7. 𝐊𝐧𝐨𝐰𝐬 𝐡𝐨𝐰 𝐭𝐨 𝐡𝐚𝐧𝐝𝐥𝐞 𝐟𝐚𝐢𝐥𝐮𝐫𝐞: PhD life can have failures in one form or another. A good PhD student knows how to handle failures and does not get motivated easily. He/she works hard consistently and does not expect a big return in little time.
8. 𝐄𝐚𝐠𝐞𝐫 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧: An effective PhD student does not shy away from learning something new. He/she is always willing to learn new things and apply them in their research in the best possible way. The student is not lazy to settle for the bare minimum.
𝟗. 𝐈𝐧𝐝𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐭: The purpose of PhD is to convert the student into an independent researcher. A good PhD student does not expect to be spoon-fed. At the same time, he/she discusses and seeks feedback from the supervisors. As the PhD progress, the student becomes more and more independent.
𝟏𝟎. 𝐑𝐢𝐬𝐤 𝐭𝐚𝐤𝐞𝐫: A good PhD does not shy away from taking risks. He/she explores adventurous ideas. Moreover, the student takes initiative with well-defined aims as to what to take away from the initiative.
Writer: Faheem Ullah Assistant Professor Computer Science, Australia
These 10 things will 10x your chances of getting a good job or a PhD position.
𝟏. 𝐏𝐮𝐛𝐥𝐢𝐬𝐡 𝐚𝐭 𝐥𝐞𝐚𝐬𝐭 𝐨𝐧𝐞 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐩𝐚𝐩𝐞𝐫: During your master/MPhil, you will work on several projects some of which will be research-focused especially your thesis project. Make sure that you work on projects with the potential to get you a published paper. Don’t wait for the right time, write and submit your papers.
𝟐. 𝐂𝐡𝐨𝐨𝐬𝐞 𝐚 𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐨𝐫 𝐚𝐜𝐭𝐢𝐯𝐞 𝐢𝐧 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡: While selecting a supervisor for your master/MPhil thesis, go for the one who is publishing good quality papers. This supervisor will help you to publish papers and introduce you to future opportunities.
𝟑. 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐜𝐨𝐮𝐩𝐥𝐞 𝐨𝐟 𝐢𝐧𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐜𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧𝐬: It’s always good to start collaborating with international professors. Build these collaborations during your master’s/MPhil. You can volunteer at this time. This collaboration may get you a PhD position after completing your master/MPhil.
𝟒. 𝐒𝐞𝐥𝐞𝐜𝐭 𝐜𝐨𝐮𝐫𝐬𝐞𝐬 𝐭𝐡𝐚𝐭 𝐡𝐚𝐯𝐞 𝐡𝐢𝐠𝐡 𝐝𝐞𝐦𝐚𝐧𝐝 𝐢𝐧 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐲 𝐚𝐧𝐝 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡: You will the option to select courses from a large pool of courses. Don’t go for the easy courses. Instead, select courses that either make you ready for industry or research.
𝟓. 𝐃𝐞𝐯𝐞𝐥𝐨𝐩 𝐚𝐭 𝐥𝐞𝐚𝐬𝐭 𝟑 𝐡𝐚𝐫𝐝 𝐬𝐤𝐢𝐥𝐥𝐬: Pick at least 3 hard skills and master these skills. For example, if you are in IT/computer science, you can pick Python programming, data engineering, and ML to master.
𝟔. 𝐃𝐞𝐯𝐞𝐥𝐨𝐩 𝐚𝐭 𝐥𝐞𝐚𝐬𝐭 𝟑 𝐬𝐨𝐟𝐭 𝐬𝐤𝐢𝐥𝐥𝐬: Similarly, pick at least 3 soft skills and master them during these 2 years. These skills can be presentation, networking, and interview skills.
𝟕. 𝐋𝐞𝐚𝐫𝐧 𝐡𝐨𝐰 𝐭𝐨 𝐜𝐫𝐞𝐚𝐭𝐞 𝐨𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬: Opportunities won’t knock at your door themselves. You need to learn how to create opportunities such as an industry internship, a research visit, establishing collaboration, and landing your dream position after the completion of your degree.
𝟖. 𝐖𝐨𝐫𝐤 𝐚𝐬 𝐚 𝐭𝐞𝐚𝐜𝐡𝐢𝐧𝐠 𝐚𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭 𝐨𝐫 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐚𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭: If your schedule allows you to work as a teaching assistant or research assistant during master’s, go for it. It will get you skills that would help you in getting future jobs.
𝟗. 𝐆𝐞𝐭 𝐠𝐨𝐨𝐝 𝐠𝐫𝐚𝐝𝐞𝐬: While doing the rest, don’t forget to get good grades as they do help to get you a good job or PhD position afterward.
𝟏𝟎. 𝐃𝐨𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐝𝐨 𝐢𝐭 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐬𝐚𝐤𝐞 𝐨𝐟 𝐚 𝐝𝐞𝐠𝐫𝐞𝐞: Some students just take courses without significant learning and complete the degree. Don’t take this path. Do master with a clear purpose such as learning skills, landing a specific job, or getting a top-notch PhD position. Once the purpose is defined, then execute accordingly.
Writer: Faheem Ullah Assistant Professor Computer Science, Australia
8 Way to identify the research gap for your PhD and MS/MPhil:
A research gap is an unanswered question or problem in your field. Answering this question or solving this problem will be the objective of your PhD/MS research.
Here is one way to identify the gap.
𝟏. 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐚𝐫𝐞𝐚: Before identifying the gap, you need to identify the area. This is quite easy. The area can either come from your previous interests or your supervisors can give it to you. For example, detecting cyber-attacks is a research area.
𝟐. 𝐑𝐞𝐚𝐝 𝟓-𝟏𝟎 𝐥𝐢𝐭𝐞𝐫𝐚𝐭𝐮𝐫𝐞 𝐫𝐞𝐯𝐢𝐞𝐰𝐬 𝐢𝐧 𝐭𝐡𝐞 𝐚𝐫𝐞𝐚: Once the area is identified, search for 5-10 most relevant literature reviews/secondary studies in the area. These papers have already reported a summarized view of the existing primary studies. Read these papers carefully to understand what literature already exists in the area.
𝟑. 𝐅𝐨𝐜𝐮𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐢𝐧 𝐭𝐡𝐞 𝐥𝐢𝐭𝐞𝐫𝐚𝐭𝐮𝐫𝐞 𝐫𝐞𝐯𝐢𝐞𝐰𝐬: While reading these 5-10 literature reviews, focus on the future research areas, open challenges, and discussion section. Identify 3-5 research directions from these literature reviews. Detecting data exfiltration attacks is a research direction.
𝟒. 𝐂𝐡𝐞𝐜𝐤 𝐞𝐱𝐢𝐬𝐭𝐢𝐧𝐠 𝐥𝐢𝐭𝐞𝐫𝐚𝐭𝐮𝐫𝐞 𝐫𝐞𝐥𝐚𝐭𝐞𝐝 𝐭𝐨 𝐭𝐡𝐞 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐢𝐞𝐝 𝐝𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧𝐬: Just to make sure that you don’t end up doing something that already exists, search primary studies related to the research directions. Drop the ones where exactly similar works exist.
𝟓. 𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐢𝐞𝐝 𝐝𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐲𝐨𝐮𝐫 𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐨𝐫𝐬: Make a few slides to present the remaining directions to your supervisors. From here, you should pick the direction where you and your supervisor see the most potential.
𝟔. 𝐂𝐨𝐧𝐝𝐮𝐜𝐭 𝐚 𝐥𝐢𝐭𝐞𝐫𝐚𝐭𝐮𝐫𝐞 𝐫𝐞𝐯𝐢𝐞𝐰 𝐨𝐧 𝐭𝐡𝐞 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐢𝐞𝐝 𝐝𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧: Once the direction is picked, do a literature review on the specific direction. If no paper exists at all in this direction, this could mean two things – either the topic is not worth doing research or the topic is good but too new.
𝟕. 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐜𝐫𝐢𝐩𝐬 𝐠𝐚𝐩𝐬 𝐯𝐢𝐚 𝐭𝐡𝐞 𝐥𝐢𝐭𝐞𝐫𝐚𝐭𝐮𝐫𝐞 𝐫𝐞𝐯𝐢𝐞𝐰: This literature review process should get you the crisp gap. However, it won’t come automatically. While reading each paper, note down the points that you think could be worth future research. This will become part of your discussion or future research section. For example, detecting data exfiltration attacks in real-time is a gap.
𝟖. 𝐂𝐡𝐞𝐜𝐤 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐝 𝐭𝐨 𝐟𝐢𝐥𝐥 𝐭𝐡𝐞 𝐠𝐚𝐩: Once you have identified the research gap, check what kind of resources, data, infrastructure, etc, you need to conduct this research. Make sure that you can have access to these resources before you start working on the gap.
Writer: Faheem Ullah Assistant Professor Computer Science, Australia
𝟏. 𝐔𝐬𝐞 𝐋𝐚𝐭𝐞𝐱, 𝐧𝐨𝐭 𝐌𝐒 𝐖𝐨𝐫𝐝: Writing a 100+ page document in MS word can become a headache. Arranging headings, tables of content, references, etc can become a challenge. So, instead of MS word, use Latex. It will take care of all such things. Also, Latex has many add-ons available that can help with difficult stuff like making tables in Latex.
𝟐. 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐞𝐱𝐞𝐦𝐩𝐥𝐚𝐫 𝐭𝐡𝐞𝐬𝐞𝐬: Before starting your thesis, identify 10-15 exemplar theses that is within your research area or the PhD is carried out in a similar fashion as yours. Skim through them especially the first chapter to understand how to structure your thesis.
𝟑. 𝐁𝐮𝐢𝐥𝐝 𝐚 𝐬𝐭𝐨𝐫𝐲: During your PhD, you work on different papers that might not be totally linked in a straightforward way. Put these different pieces in front of yourself and think about how to make them link with each other and make a smooth story.
𝟒. 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐢𝐬 𝐭𝐡𝐞 𝐦𝐚𝐤𝐞 𝐨𝐫 𝐛𝐫𝐞𝐚𝐤: This chapter summarizes your whole thesis and leaves an impression on the reader/examiner. Invest the most amount of time in writing this chapter. Amongst others, clearly mention upfront the research papers you have published during your PhD.
𝟓. 𝐂𝐫𝐢𝐬𝐩 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐬𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐜𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬: Mention within 3-4 lines the concrete problem you have solved during your PhD. Also, examiners look for 3-4 solid contributions made by the PhD student. Don’t make them search for them. Present these contributions upfront in the Introduction chapter.
𝟔. 𝐓𝐡𝐞𝐬𝐢𝐬 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐯𝐢𝐚 𝐚 𝐟𝐢𝐠𝐮𝐫𝐞: PhD thesis is a very long document. Navigating through it can be a challenge. Include a figure in the Introduction section that shows the organization of the thesis including the various chapters. A reviewer can just print this figure and keep it in front of himself/herself to navigate through the whole thesis.
𝟕. 𝐆𝐢𝐯𝐞 𝐢𝐭 𝐭𝐢𝐦𝐞: Don’t leave thesis writing until the very end. Depending upon the situation, at least leave 4 months for thesis writing.
𝟖. 𝐒𝐞𝐞𝐤 𝐟𝐞𝐞𝐝𝐛𝐚𝐜𝐤: Manage your writing in a way that each part gets reviewed. If you are running short of time, you can send each chapter separately as it completes to your supervisors for feedback.
𝟗. 𝐓𝐡𝐨𝐫𝐨𝐮𝐠𝐡𝐥𝐲 𝐩𝐫𝐨𝐨𝐟𝐫𝐞𝐚𝐝: One of the most common comments from thesis reviewers is to fix the typos. Proofread your entire thesis a couple of times before submission to avoid getting this comment.
𝟏𝟎. 𝐋𝐢𝐧𝐤 𝐜𝐡𝐚𝐩𝐭𝐞𝐫𝐬 𝐭𝐨 𝐞𝐚𝐜𝐡 𝐨𝐭𝐡𝐞𝐫: Make sure that the chapters are linked together. For example, it shouldn’t appear that when the reviewer starts reading chapter 4, it is completely different from chapter 3. At the start of chapter 4 or end of chapter 3, mention how they are linked.
Writer: Faheem Ullah Assistant Professor Computer Science, Australia
𝟏. 𝐄𝐱𝐩𝐨𝐬𝐮𝐫𝐞: You will likely travel to another country or city for your PhD. During your PhD, you will also travel to different countries for conferences, workshops, research visits, and field trips. All of this will get you a lot of exposure. You will learn and experience many interesting things.
𝟐. 𝐂𝐚𝐫𝐞𝐞𝐫 𝐠𝐫𝐨𝐰𝐭𝐡: One undeniable benefit of PhD is that it will boost your career. This is especially true for academics. Other than this, PhD qualification is also required to work in advanced positions in industry and government agencies.
𝟑. 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐢𝐧𝐠 𝐡𝐚𝐫𝐝 𝐬𝐤𝐢𝐥𝐥𝐬: You will develop deep technical skills in your field. For example, you will learn the implementation of ML/DL systems if your PhD is focused on AI. These deep technical skills will make you stand out among your competitors.
𝟒. 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐢𝐧𝐠 𝐬𝐨𝐟𝐭 𝐬𝐤𝐢𝐥𝐥𝐬: PhD will enrich your personality with several soft skills such as presentation, communication, collaboration, networking, critical thinking, and so on. You will often practice these during your PhD.
𝟓. 𝐆𝐫𝐨𝐰𝐭𝐡 𝐢𝐧 𝐲𝐨𝐮𝐫 𝐩𝐫𝐨𝐟𝐞𝐬𝐬𝐢𝐨𝐧𝐚𝐥 𝐧𝐞𝐭𝐰𝐨𝐫𝐤: You will interact with several individuals during your PhD. These interactions will happen in multiple places – conferences, workshops, seminars, and so on. This network will open doors for future opportunities.
𝟔. 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐜𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬: You will contribute to the body of knowledge in your field. This will get you a strong feeling of accomplishment. Your findings may have a direct impact on the well-being of people.
𝟕. 𝐑𝐞𝐬𝐢𝐥𝐢𝐞𝐧𝐜𝐞: Your PhD journey will test you in many ways – paper rejections, criticisms, financial hardships, and so on. All of this will make you resilient. Eventually, you will be unbreakable in front of many such challenges.
𝟖. 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐬𝐨𝐥𝐯𝐞𝐫: You will become a problem solver. Since most of the PhD is about identifying and solving complex problems, your mind will get trained for it. Then, this mindset can help you solve many non-research problems too.
𝟗. 𝐁𝐞𝐢𝐧𝐠 𝐩𝐚𝐢𝐝 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧: Most PhD students have scholarships covering their tuition fees and living expenses. In return, you work on things you are passionate about and learn many skills.
𝟏𝟎. 𝐆𝐞𝐭 𝐲𝐨𝐮 𝐭𝐡𝐞 𝐭𝐢𝐭𝐥𝐞 ‘𝐃𝐫’: If you are interested in titles, PhD will get you that too. You can call yourself ‘Dr.’ after successfully completing your PhD :).
Writer: Faheem Ullah Assistant Professor Computer Science, Australia
𝟏. 𝐓𝐡𝐞𝐫𝐞 𝐢𝐬 𝐧𝐨 𝐚𝐥𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐯𝐞 𝐭𝐨 𝐡𝐚𝐫𝐝 𝐰𝐨𝐫𝐤: When you put your blood and sweat into it, you will be rewarded. The same goes true for PhD. Hard work always pays off. It’s not easy but it’s worth it – this you will find out after successfully completing a solid PhD.
𝟐. 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐢𝐚𝐭𝐞 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐚𝐧𝐝 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐚𝐛𝐨𝐮𝐭 𝐰𝐨𝐫𝐤: Working is when you are productive – writing a paper, analyzing data, preparing a presentation, etc. Thinking about work is when you are worried but not productive. It just creates an illusion of work. When it’s time to work, work, and when it’s time to enjoy, forget about work.
𝟑. 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐚𝐧𝐝 𝐫𝐞𝐦𝐨𝐯𝐞 𝐝𝐢𝐬𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧𝐬: PhD requires good focus. Anything that goes into your head and negatively distracts you, remove them. This could be a person in your surrounding, a social media platform, or an activity. You can do good research when you are happy and focused.
𝟒. 𝐓𝐡𝐞𝐫𝐞 𝐢𝐬 𝐧𝐨 𝐬𝐮𝐜𝐡 𝐭𝐡𝐢𝐧𝐠 𝐚𝐬 𝐩𝐞𝐫𝐟𝐞𝐜𝐭𝐢𝐨𝐧: PhD is to be completed in a specific period of time. Don’t wait for perfect outcomes – be that a paper, the outcome of an experiment, or a presentation. Have it in decent shape, discuss it with your supervisors, and get it out such as submission to a journal.
𝟓. 𝐈𝐭’𝐬 𝐧𝐞𝐯𝐞𝐫 𝐠𝐨𝐢𝐧𝐠 𝐭𝐨 𝐛𝐞 𝐢𝐝𝐞𝐚𝐥: My PhD topic is not good, my dataset is not good, and the reviewers are not good. Understand that it’s never going to be ideal. Champions are the ones who win despite hurdles – be a champion.
𝟔. 𝐂𝐡𝐞𝐫𝐢𝐬𝐡 𝐬𝐦𝐚𝐥𝐥 𝐬𝐮𝐜𝐜𝐞𝐬𝐬: Succes in PhD is often defined by a paper acceptance or thesis clearance. This means only 4-5 moments of success. So, from where you will get motivation? You should cherish small achievements such as getting good results from an experiment, your work getting cited, receiving appraisal for your idea, and so on.
7. 𝐒𝐭𝐚𝐫𝐭 𝐚𝐧𝐝 𝐟𝐢𝐠𝐮𝐫𝐞 𝐢𝐭 𝐨𝐮𝐭 𝐨𝐧 𝐭𝐡𝐞 𝐰𝐚𝐲: Don’t look for a perfect research gap. Start your research and you will figure it out on the way. Don’t pick dead topics. It’s easy to find which topics are dead. One way is to check the last 3 years’ proceedings of top conferences and journals. If there are no papers on the topic, it is a red signal.
8. 𝐔𝐭𝐢𝐥𝐢𝐳𝐞 𝐲𝐨𝐮𝐫 𝐬𝐮𝐩𝐞𝐫𝐯𝐢𝐬𝐨𝐫𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐦𝐚𝐱: Seek concrete feedback from your supervisors. Learn how they modify your paper draft or slides. Ask explicit questions – should I use method X or Y for data analysis and why?
9. 𝐏𝐡𝐃 𝐢𝐬 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐲𝐨𝐮𝐫 𝐥𝐢𝐟𝐞, 𝐧𝐨𝐭 𝐲𝐨𝐮𝐫 𝐞𝐧𝐭𝐢𝐫𝐞 𝐥𝐢𝐟𝐞: Don’t overburden yourself with PhD activities. Keep a good balance. If you are happy and healthy, it will contribute to your PhD too. So, be happy and make others around you feel the same.
Writer: Faheem Ullah Assistant Professor Computer Science, Australia
𝟏. 𝐀𝐯𝐨𝐢𝐝 𝐬𝐨𝐜𝐢𝐚𝐥 𝐦𝐞𝐝𝐢𝐚 𝐢𝐧 𝐭𝐡𝐞 𝐟𝐢𝐫𝐬𝐭 𝐡𝐚𝐥𝐟 𝐨𝐟 𝐭𝐡𝐞 𝐝𝐚𝐲: Social media is not controllable. As we scroll, we see content irrespective of whether or not we really want to see it. Some of the content deeply impacts us such as a horrible incident or something bad happened to a loved one. If it happens, you might not be productive for the rest of the day. So, better avoid it in the first half.
𝟐. 𝐀𝐭 𝐥𝐞𝐚𝐬𝐭 𝟒 𝐡𝐨𝐮𝐫𝐬 𝐨𝐟 𝐝𝐚𝐢𝐥𝐲 𝐟𝐨𝐜𝐮𝐬: Our mind does not work to its maximum potential all the time. However, it does work in a focused way for 3-4 hours. Make sure to work with full focus for at least 4 hours a day. Get yourself disconnected from mobile phones, emails, etc, and work on the most challenging task. It shouldn’t be 4 consecutive hours, though.
𝟑. 𝐋𝐞𝐚𝐯𝐞 𝐥𝐢𝐠𝐡𝐭 𝐭𝐚𝐬𝐤𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐞𝐧𝐝 𝐨𝐟 𝐭𝐡𝐞 𝐝𝐚𝐲: Not all PhD tasks require equal focus. For example, replying to some emails, marking student submissions, and voluntary tasks for a conference do not require too much mental focus. Work on them at times when you are not too productive.
𝟒. 𝐉𝐮𝐬𝐭 𝐬𝐭𝐚𝐫𝐭: Procrastinating PhD tasks is quite common. Don’t procrastinate. Follow the 5-second rule. If you don’t jump towards the task in the first 5 sec, your brain starts pushing you away from it. Everything is difficult before you do it, just do it.
𝟓. 𝐓𝐫𝐲 𝐏𝐨𝐦𝐨𝐝𝐨𝐫𝐨: It helps especially during paper writing. Some students follow this technique and have shown good results. Set a timer to 25 mins and solely focus on writing during these 25 mins. Once the timer hits, take a 5 min break and come back again.
𝟔. 𝐓𝐚𝐤𝐞 𝐧𝐨𝐭𝐞𝐬: As a PhD student, you should be eager for ideas. These ideas you can take from many places – meetings, conferences, and your own thoughts. Don’t let them fly away. You can easily take notes even on your mobile phone. Take notes and reflect on them later.
𝟕. 𝐃𝐨𝐧’𝐭 𝐥𝐞𝐚𝐯𝐞 𝐬𝐡𝐨𝐫𝐭𝐞𝐫 𝐭𝐚𝐬𝐤𝐬 𝐟𝐨𝐫 𝐭𝐨𝐦𝐨𝐫𝐫𝐨𝐰: Replying to an email, passing your paper through Grammarly, and sending a group meeting agenda are shorter tasks. These tasks take 5-10 min. However, when you are on the way back ending your day, you will feel like you have done several tasks – a feeling of accomplishment for the day.
𝟖. 𝐀𝐯𝐨𝐢𝐝 𝐭𝐨𝐱𝐢𝐜𝐢𝐭𝐲: Some activities, people, and content around you can be toxic. A 5-sec toxic engagement can ruin your day. It can demotivate you and keeps you unproductive for the whole day. Identify these and keep yourself away.
𝟗. 𝐃𝐨𝐧’𝐭 𝐰𝐨𝐫𝐫𝐲: Being worried is something not unusual for PhD students. However, being worried doesn’t solve any problems but the right actions do. For the right actions, you first need to get yourself out of the worry bubble. Taking PhD worries out of your head will double your productivity.
Writer: Faheem Ullah Assistant Professor Computer Science, Australia
𝟏. 𝐋𝐢𝐧𝐤𝐬 𝐭𝐨 𝐲𝐨𝐮𝐫 𝐩𝐫𝐨𝐟𝐢𝐥𝐞𝐬: In the personal information section at the top of your CV, include links to your online profiles such as google scholar, LinkedIn, DBLP, homepage, and research gate. It will help the assessor to view your profile via a familiar forum.
𝟐. 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐚𝐫𝐞𝐚𝐬: After the personal information section, mention your 4-5 research areas such as machine learning, cyber security, big data analytics, and so on. This directly shows whether or not your profile is relevant to the advertised position.
𝟑. 𝐍𝐨 𝐏𝐚𝐫𝐚𝐠𝐫𝐚𝐩𝐡𝐬: Do not include paragraphs in your CV. It makes it hard to read. Instead of paragraphs, include concrete bullet points.
𝟒. 𝐍𝐨 𝐨𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐬: Often students mention one big paragraph at the start as an objective/aim. This is not required as the assessor already knows the position for which you have applied. Hence, this becomes redundant.
𝟓. 𝐇𝐲𝐩𝐞𝐫𝐥𝐢𝐧𝐤: As much as possible, add hyperlinks. For example, you can add links to your university, your workplaces, and so on. This helps the assessor to directly check where you studied or worked.
𝟔. 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭 𝐲𝐨𝐮𝐫 𝐬𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬: If you have something in your profile that makes you unique, bring it to the first page. For example, if you are a gold medalist in your undergraduate or you have won some programming competition, add them as achievements on the first page. This will make you stand out in comparison to other applicants.
𝟕. 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐩𝐮𝐛𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬: If you have publications even under review, do mention them upfront. If the papers are already online, add a link to each paper so that the assessor can directly check it out. Add all relevant details to each publication such as journal/conference ranking and impact factors.
𝟖. 𝐍𝐨 𝐌𝐒 𝐰𝐨𝐫𝐝 𝐟𝐨𝐫𝐦𝐚𝐭: Do not share or submit your CV in MS word format. It does not look good even at times MS word formatting is distorted. Submit or share your CV in PDF format.
𝟗. 𝐌𝐞𝐧𝐭𝐢𝐨𝐧 𝐄𝐧𝐠𝐥𝐢𝐬𝐡 𝐒𝐜𝐨𝐫𝐞: If you have undertaken IELTS/TOEFL kinds of tests, do mention your scores. These English scores are one of the admission requirements and help a professor in student selection.
𝟏𝟎. 𝐀𝐬𝐤 𝐭𝐨 𝐫𝐞𝐯𝐢𝐞𝐰 𝐲𝐨𝐮𝐫 𝐂𝐕: At least ask 2 experienced people to review your CV. This will help to polish your CV by removing any typos, grammar, and evident issues.
Writer: Faheem Ullah Assistant Professor Computer Science, Australia
You might be wondering how a shark could be related to the surface roughness topic? As a child, I always thought animals have teeth similar to human beings until I watched the deep blue sea movie. Similarly, I always thought that copper foils and metallic surfaces are quite smooth in nature until I learned the nuances of the copper foil manufacturing process. We will discuss in this topic of how the shark-like teeth of copper (surface roughness) impacts the signal integrity performance.
Surface roughness is a concept that we see at least one DesignCon paper every year. I am expecting the coming DesignCon conferences no different. Upon deeper review of these topics, we will find that they are loaded with mathematical models as well as complicated jargons such as cannon ball model, profilometer measurements and SEM scanning, which makes all of these topics sound like a PHD research topic.
In the end, everything boils down to the following discussion: Is the copper foil a smooth surface or a rough surface. If it is a rough surface, how rough it is and what is the deviation of that roughness from what is considered being smooth ? Why do we need to understand this topic?
The total loss is characterized in the following way:
Total Loss = Insertion Loss of Dielectric + (Surface roughness coefficient * Insertion Loss of conductor)
Let us understand why a copper will introduce loss into the signal. To understand this, let us first discuss what is skin effect and why the currents will flow outside the conductor.
Skin Effect and Skin Depth:
The skin effect is a phenomenon whereby alternating electric current does not flow uniformly with respect to the cross-section of a conductive element, such as a wire [1].
The current density is highest near the surface of the conductor and decreases exponentially as distance from the surface increases. Skin depth is a measure of how far electrical conduction takes place in a conductor, and is a function of frequency. At DC (0 Hz) the entire conductor is used, no matter how thick it is. At microwave frequencies, the conduction takes place at the top surface only. As we go down the surface, the current density decrease. This can be seen even in the animation presented below.
Eddy Currents and Magnetic Fields:
When a good electrical conductor (like copper or aluminum) is exposed to a changing magnetic field, a current is induced in the metal, commonly called an eddy current. Eddy currents flow in closed loops within conductors, in planes perpendicular to the magnetic field.
Why is Copper Foil roughened?
Copper foil is roughened to promote adhesion of the dielectric resin to the conductor in printed circuit boards. Adhesion at the interface between conductor and insulator must be very robust due to conditions during manufacturing, assembly, and standard usage to which a printed circuit board is subjected. This interface is exposed to corrosive chemicals during processing and to high temperature, high humidity, cold, shock, vibration, and shear stresses during use. Technologies that optimize surface and resin chemistries, as well as surface area, are utilized by foil manufacturers and laminators to promote and retain adhesion.
As shown in the Figure3 ( Reference [1]), the 90° peel strength is a measure of how well a conductor adheres to the dielectric material and is directly proportional to the fourth root of the thickness of deformed resin (yo) and other parameters like foil thickness, etc. The adhesive interlayer thickness is correlated to the treatment height. Higher roughness increases the interlayer thickness, the surface area, and the chemical contributions to peel strength. Though roughness contributes positively to peel, it has a negative impact on signal integrity.
Copper Foil Manufacturing Process:
There are various ways of copper foil manufacturing process available in PCB industry. Electro-deposited (ED) copper is widely used in the PCB industry. A finished sheet of ED copper foil has a matte side and drum side. The drum side is always smoother than the matte side.
The matte side is usually attached to the core laminate. For high frequency boards, sometimes the drum side of the foil is laminated to the core. In this case it is referred to as reversed treated (RT) foil. Various foil manufacturers offer ED copper foils with varying degrees of roughness. Each supplier tends to market their product with their own brand name. Presently, there seems to be three distinct classes of copper foil roughness:
Some other common names referring to ULP class are HVLP or eVLP. Profilometers are often used to quantify the roughness tooth profile of electro-deposited copper. Tooth profiles are typically reported in terms of 10-point mean roughness (Rz ) for both sides, but sometimes the drum side reports average roughness (Ra ) in manufacturers’ data sheets. Some manufacturers also report RMS roughness (Rq ). Now we know why we have roughness in the copper profile.
Simulation vs Measurement (Chicken and Egg Story)
It is quite tough to interpret the manufacturer’s data sheet. Some authors argue to use design feedback methodology.
Design a test coupon –>fabricate the board –> Measure the frequency domain parameters performance –>Extract the Dk,Df and surface roughness of the board –> Cross Verify and compare against the simulated parameters –> 100% it will vary –> Now add back into the simulation channel performance –>Re simulate the analysis ! This is called Design feedback methodology. In a competitive cut throat market this is time consuming procedure. Money and resources go for a kick !Consider the analysis demonstrated in Fig 6 and 7, we can observe two different set of designs. Figure 6 consists of measured calibration traces of the same design and same manufacturer and So is with Figure 7 analysis
Which sample should we consider for measuring the material parameters? To solve this issue to certain extent, empirical models will allow us to reach our goal sooner. I always used to listen this statement from my manager: Sometimes an OK answer NOW is better than a good answer. This statement is made by Eric Bogatin.
Empirical Models: Surface Roughness Coefficient
Calculating the surface roughness coefficient is done via an empirical model fit. Finding the right empirical fit to the measured data is the ball game in the research papers and the tussle to be better than previous used models.
Empirical Models that are available in market for calculating the surface roughness of the conductors:
1) Morgan – Hammerstad 2) Groisse Model 3) Huray “Snowball” 4) Hemispherical model (Hall, Pytel) 5) Stochastic models (Sanderson, Tsang). May be there could be more than mentioned but I restricted the analysis for the first three models.
There is one more advanced model developed by Bert Simonvich based on Huray Snowball. Polar instruments has implemented this model in their software. If you want to learn more, all his application notes are sufficient to understand the analysis [4].
Also, I want to mention about Yuriy Shlepnev, Simberian Inc application notes regarding this topic. The following paper : Conductor surface roughness modeling: From “snowballs” to “cannonballs” discusses in detail about the myth surrounding the models [5]
Morgan -Hammerstad:
Power absorption factor Ksr is defined as ratio of power dissipated by the eddy currents in a conductor per unit area when the surface is rough, Pa,rough, to the power dissipated when the surface is smooth, Pa,smooth. Consider the formulation no 4 and 5 which are interpreted as per above discussion.
Ksr is used as a correction factor to account for the conductor surface roughness effect when calculating attenuation. The attenuation constant, αc,rough, for wave propagation in waveguides involving conductors with rough surfaces can be estimated by multiplying the attenuation constant calculated for a smooth conductor, αc,smooth, with Ksr.
Samuel Morgan: Reference Paper -[2]
Highlights of research paper: Morgan determined that, at 10 GHz, current flow transverse to periodic structures could increase loss by up to 100%! If the current flow was parallel, the losses increased by up to 33%. Morgan hypothesized the cause for this additional loss assumed that the loss was a function of the RMS distortion of a rough surface relative to the electromagnetic skin depth of a perfectly smooth metal surface with conductivity equal to that of the bulk metal.
In the paper, Morgan experimented the analysis on equilateral, triangular and square shapes and considered the cases to be treated as two dimensional; the surface roughness is assumed to consist of infinitely long parallel grooves or scratches either normal to or parallel to the direction of induced current flow. From the analysis, Morgan figured out that transverse grooves have a considerably greater adverse effect than grooves parallel to the current.
Morgan -Hammerstad Empirical Fit:
In 1975, Hammerstad proposed an empirical fit for the Morgan’s published data and it was is quite ok at that time, however it saturates for the rough conductor at low frequencies to a value of 2.
As observed in the results, the empirical fit is very limited and hence advised not to use this method for validating the copper profile roughness.
Groisse Model (Available in the ANSYS Software):
I would give a very brief analysis about this model. This model is slightly better than Hammerstad model. However even this model saturates to a value of 2. The symbols used in the equation holds the same as explained earlier. This model is seldom used at our end.
Huray Model (Snow Ball Model):
Before we move into this model, there are few insights Paul.G.Huray provided along with the other authors in this paper. I wish all SI engineers read this reference paper without fail. It is quite lengthy and may take time but worth a read.
Does surface roughness increase the length and resistance ? Does the current take longer time to travel in case of rough surface ? Actually the answer is NO. In case you felt yes, then the propagation delay should have been increased in case of rough copper surface.
Let us understand what does this author means: The answer is that local surface charge density (Quantity of charge per unit area) is formed by the displacement of conduction electrons transverse to the surface profile so that a wave of charge density propagates at whatever speed is needed to support the external electric field intensity in the transmission line. No charged particles actually move at relativistic speeds ( a speed comparable to speed of light).
A current flowing on the rough surface of a conductor cannot be regarded as a current flowing on a flat surface of the conductor with a longer equivalent path. When current flows in a conductor, assuming that the current distribution decays exponentially within the depth of the conductor, the model is not correct either.
As discussed earlier, high profile samples resemble copper nodule pyramid structures arranged in a nominal hexagonal pattern on a matte finish surface. By comparison, the low profile samples appear to be made up of similar size copper nodules randomly scattered on a flat plane; the nodules vary in radius but the average size is about 1 μm in diameter.
Now let us come back to the empirical model developed by Paul Huray, we shall neglect the non uniform model architecture developed by the team as it does not contribute much but gave a pathway to develop EDA based empirical model.
Uniform Snowball Architecture:
So now that the Huray Model equation is understood, the next task is how to fit these complicated equations into the EDA toolkit. Below is the simulation analysis for various empirical model fits.
What do I do in my daily life simulation and measurement analysis:
Get from the vendor (TTM/Sanmina/Founder) the data sheet of the dielectric materials being used in the stack up design. It could be either MEG 6 or MEG 7 or Tachyon 100 G and obtain the surface roughness measurement value (eg Rz) which is roughly divided into low/medium/high roughness, understand the roughness difference between the top surface and the bottom surface, and apply all of them into the simulation model. For Dk and Df fit, I use Djordjevic-Sarkar model (Wide Debye Model). Then use the design feedback methodology.
That’s all Amigos ! This took really a lot of time as I went back and forth to understand. Next time, when you see the canon ball or surface roughness as topic at DesignCon, you will be more educated on this topic 😉
Thanks for reading, I hope you enjoyed reading this discussion.
References:
1) Non-Classical Conductor Losses due to Copper Foil Roughness and Treatment by Gary Brist and Stephen Hall Intel Corporation Hillsboro, Sidney Clouser, Tao Liang
2)S.P. Morgan Jr., “Effect of Surface Roughness on Eddy Current Losses at Microwave Frequencies,” Journal of Applied Physics, Vol. 20, p. 352-362, April 1949.
3) Practical methodology for analyzing the effect of conductor roughness on signal losses and dispersion in interconnects.
4)Simonovich, Bert, “Practical Method for Modeling Conductor Surface Roughness Using The Cannonball Stack Principle”, White Paper.
5)Conductor surface roughness modelling: From “snowballs” to “cannonballs” Yuriy Shlepnev, Simberian Inc.
