Novel Quantum Materials for Low Power Electronics

Table: CHEM1
Experimentation location: School, Home
Regulated Research (Form 1c): No
Project continuation (Form 7): No

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Research Plan:

In this study, papers from the past 20 years were reviewed and data from these papers was used to analyze and develop the model. A few review papers were chosen first for developing a broad understanding of the current state of the field. Some of the papers with relevant experimental results were extracted from the reference sections of these papers. Other literature was found through key-word searches. A full list of papers is included in the references section. From the papers, data was selected, initially relating to the relationship between tungsten concentration (atomic percent) and insulator-metal transition temperature (Kelvin). The data collected was then recorded in tables in a spreadsheet, alongside information regarding the procedures for material synthesis, any relevant definitions or notes, and the reference. Once data compilation was completed, the data was plotted, and lines of best fit were generated. Following this, the source material was re-read to gain an understanding of the properties observed. A similar procedure was followed to study the effect of dopant ionic radius (pm) on the IMT temperature (K) at a fixed concentration (at. %) and on the rate of change in the IMT (K/at. %), and the effect of tungsten concentration on activation energy (meV) and threshold voltage (V). 

Through the literature search, the Adler model was found. After reading more about the Adler model, it was compared with the linear model developed to evaluate the predicted values of change in the insulator-metal transition temperature. This comparison was done through using varying values of dopant concentration to determine the magnitude of difference in the transition temperature. The exact value of the transition temperature was not directly compared because of the difference in scaling factor between the two models.

            Following the literature search, the software package SimNRA was used to simulate the Rutherford Backscattering Spectroscopy (RBS) spectra of varying compositions of WxV1-xO2. Four concentrations were simulated: Pure VO2, 0.5 at. % tungsten (W0.005V0.995O2), 1 at. % tungsten (W0.01V0.99O2) and 1.6 at. % tungsten (W0.016V0.984O2). The spectra were overlaid to examine differences. The doping concentrations were chosen such that the transition temperature is shifted down towards room temperature that is of interest to brain-inspired computing and thermochromic windows.

Questions and Answers

1. What was the major objective of your project and what was your plan to achieve it? 

       a. Was that goal the result of any specific situation, experience, or problem you encountered? 

       b. Were you trying to solve a problem, answer a question, or test a hypothesis?

Global energy consumption is rapidly growing with widespread access to electronic devices. Two primary contributors are artificial intelligence and computing devices, and thermal energy regulation systems. To address these challenges, new energy-efficient technologies have been developed, such as brain-inspired computing and thermochromic windows. These technologies both use quantum materials, with one of the most promising candidates being vanadium dioxide (VO2). VO2 undergoes a sharp insulator-metal transition (IMT) just above room temperature, and by doping with tungsten (W 6+), the transition can be lowered to room temperature. 

Currently, there is a lack of predictive models/theory due to limited data, leading to an incomplete understanding of tungsten-doped vanadium dioxide’s (W-VO2) insulator-metal transition (IMT) properties (transition temperature, threshold voltage). This causes delays in the implementation of W-VO2 in mainstream and future energy-efficient technologies. I tried to address the problem of the lack of predictive models through my project. I was trying to answer these 5 questions:

  1. How does tungsten-doping concentration affect the IMT temperature?
  2. How does the ionic radii of the dopant affect the IMT temperature?
  3. How does threshold voltage relate to the IMT temperature?
  4. Can semiconductor models estimate IMT properties?
  5. Can tungsten-doping concentration be measured in VO2?

The main objective of my project was to build models that would determine the relationships between tungsten-doping and W-VO2’s insulator-metal transition properties (transition temperature, threshold voltage, etc.). To achieve this, I conducted a comprehensive literature review through which I was able to compile all published experimental data from the past 20+ years. I analyzed this data to determine relationships and to create my models.


2. What were the major tasks you had to perform in order to complete your project?

       a. For teams, describe what each member worked on.

There were four major tasks that I had to perform to complete my project. This was an individual project, so I completed the tasks by myself.

1. Data Collection: I did a comprehensive literature review (20+ years of data), through which I compiled all published experimental data on dopant properties (tungsten concentration, other dopant ionic radii) and VO2’s corresponding insulator metal-transition properties (temperature, threshold voltage, activation energy). 

2. Data Analysis & Model Creation: I analyzed the data to determine relationships and build my models. I developed two linear models for predicting the IMT temperature and threshold voltage respectively as a function of tungsten-doping. 

3. Model Validation: I validated the IMT temperature model by comparing it with the Adler Impurity Conduction Model and other literature reports. 

4. Predictive Simulations: I did predictive simulations of the characterization technique Rutherford Backscattering Spectroscopy to determine if this could be used to measure tungsten-doping concentrations in nanoscale VO2.


3. What is new or novel about your project?

       a. Is there some aspect of your project's objective, or how you achieved it that you haven't done before?

       b. Is your project's objective, or the way you implemented it, different from anything you have seen?

       c. If you believe your work to be unique in some way, what research have you done to confirm that it is?

My work establishes one of the first universal, quantitative, predictive models for determining the insulator-metal transition (IMT) properties of VO2 as a function of tungsten (W) doping concentration. This is important because it reduces dependence on experimentation for determining the properties of W-VO2. Experiments are expensive, time-consuming, and resource-intensive, so having predictive models will help to reduce costs associated with determining the IMT properties of W-VO2. Previously, such a comprehensive review of literature has not been conducted to develop predictive relationships between the IMT properties and tungsten concentration.

Through my literature review, I did not find other papers that have summarized literature in the way that I have, confirming that my work is original. I presented my work at the Materials Research Society 2023 Fall Meeting where I received feedback that confirmed that my work is novel and of great interest to the scientific community. 


4. What was the most challenging part of completing your project?

      a. What problems did you encounter, and how did you overcome them?

      b. What did you learn from overcoming these problems?

One of the most challenging parts of completing my project was the model validation. As a high schooler, I did not have access to experimentation, so I was not able to validate my model through my own experiments. To overcome this, I found a few ways to validate my model, including comparing it with the Adler Impurity Conduction Model. The Adler Model combines classical semiconductor theory with the insulator-metal transition (IMT) behavior seen in quantum materials to predict a difference in the IMT temperature as a function of doping concentration. I also compared my model with predicted values for the rate of change in transition temperature from a study that used two Density Functional Theory (DFT) approaches. After I developed my model, I compared my model’s predicted value with new, published experimental data that was not included in my model. Through these comparisons I was able to validate my model. I found that my model agreed closely with the Adler Model and the DFT study, and the predicted values were similar to the experimental data.

Additionally, there was a steep learning curve in this project. Before taking on this project, I did not know much about quantum materials or materials science, so I initially struggled to understand the papers that I was trying to read. To overcome this, I studied textbooks and watched lectures online. 

These challenges taught me the importance of being resourceful and persistent in research, as I realized that I had to always keep trying to find more information and creative approaches, even when I felt I was running into roadblocks.


5. If you were going to do this project again, are there any things you would you do differently the next time?

If I did this project again, I would change the way I did my literature review and data collection. I conducted the review by starting with a couple review papers, which I read in-depth. Then, I began to read the papers that were listed in the reference section which had data. I initially skimmed the papers, to get a basic understanding, and then collected the data. After finishing data collection, I reread the papers in-depth. If I had to do this again, I would read all papers in-depth before extracting data from them. I believe this would help me speed up the data collection process, as I would have been able to organize my time better between the literature review and data extraction and gain a better appreciation for the data.


6. Did working on this project give you any ideas for other projects? 

This project gave me many ideas for new projects!

1. I would like to extend my study of the IMT properties to optical reflectance. Understanding how W-doping affects optical reflectance is incredibly important for technologies like thermochromic windows, so extending my study to include a model for this would be of great interest.

2. I would like to develop a model to relate the RBS spectral peak features to tungsten doping concentration. This will allow the peak features to act as a spectroscopic fingerprint, which would enable predictions of the IMT properties based on the spectra.


7. How did COVID-19 affect the completion of your project?

COVID-19 did not affect the completion of my project because it was done computationally.