With the increasing complexity of engineering design and stringent time-to-market requirements, traditional experience and trial-and-error-based design methods are facing severe challenges. They already show challenges and even failure in many high-tech areas. Using the traditional design method supported by existing tools, the design process often suffers from the following issues:
- Difficulty in obtaining highly optimized designs or satisfying stringent design specifications.
- A long design time that may not meet the stringent time-to-market.
- Strong expertise and design experience that may be difficult to acquire are required for designers.
AI techniques bring a light to address the above challenges. Machine learning and intelligent optimization techniques are showing exciting results in some electronic design problems. However, there are many obstacles on this road, both from the AI techniques themselves and from interaction with designers. The Artificial Intelligence-Driven Design Automation and Computer-Aided Engineering lab (AIDAC) has been established to overcome these obstacles and promote the intelligent design trend for electronic engineering. Our current focuses are AI-driven design methodology of microwave devices (antennas, filters, couplers), RF ICs, analog ICs and systems, and micro-electromechanical systems.
In particular, the following research directions are critical :
- Novel data-driven optimization and machine learning algorithms for electronic design;
- Mixed intelligence electronic design methodologies hybridizing AI techniques, domain knowledge, and the designer’s intelligence;
- AI-driven design in real-world electronic engineering;
- Computer-aided design tools implementing AI-driven electronic design and data analysis methods.
Key Publications
Z. Zhang, B. Liu, Y. Yu, and Q. S. Cheng, “A Microwave Filter Yield Optimization Method Based on Offline Surrogate Model Assisted Evolutionary Algorithm,” in IEEE Transactions on Microwave Theory and Techniques, vol. 70, no. 6, pp. 2925-2934, 2022.
A. Budak, M. Gandara, W. Shi, D. Pan, N. Sun and B. Liu, “An Efficient Analog Circuit Sizing Method Based on Machine Learning Assisted Global Optimization,” in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 51, no. 5, pp. 1209-1221, May 2022.
B. Liu, M. O. Akinsolu, C. Song, Q. Hua, P. Excell, Q. Xu, Y. Huang, and M. A. Imran, “An Efficient Method for Complex Antenna Design Based on a Self Adaptive Surrogate Model-Assisted Optimization Technique,” in IEEE Transactions on Antennas and Propagation, vol. 69, no. 4, pp. 2302-2315, April 2021.
M. O. Akinsolu, B. Liu et al., “Efficient Design Optimization of High-Performance MEMS Based on a Surrogate-Assisted Self-Adaptive Differential Evolution,” in IEEE Access, vol. 8, pp. 80256-80268, April 2020.
Y. Yu, B. Liu, Y. Wang, M. J. Lancaster and Q. S. Cheng, “A General Coupling Matrix Synthesis Method for All-Resonator Diplexers and Multiplexers,” in IEEE Transactions on Microwave Theory and Techniques, vol. 68, no. 3, pp. 987-999, March 2020.
V. Grout, M. O. Akinsolu, B. Liu, P. I. Lazaridis, K. K. Mistry, and Z. D. Zaharis, “Software Solutions for Antenna Design Exploration: A Comparison of Packages, Tools, Techniques, and Algorithms for Various Design Challenges,” in IEEE Antennas and Propagation Magazine, vol. 61, no. 3, pp. 48-59, June 2019.
M. O. Akinsolu, B. Liu, V. Grout, P. I. Lazaridis, M. E. Mognaschi and P. D. Barba, “A Parallel Surrogate Model Assisted Evolutionary Algorithm for Electromagnetic Design Optimization,” in IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 3, no. 2, pp. 93-105, April 2019.
B. Liu, M. O. Akinsolu, N. Ali, and R. A. Abd‐Alhameed, “Efficient Global Optimisation of Microwave Antennas based on a Parallel Surrogate Model‐assisted Evolutionary Algorithm,” in IET Microwaves, Antennas and Propagation, vol. 13, no. 2, pp. 149-155, Nov. 2018.
B. Liu, V. Grout and A. Nikolaeva, “Efficient Global Optimization of Actuator Based on a Surrogate Model Assisted Hybrid Algorithm,” in IEEE Transactions on Industrial Electronics, vol. 65, no. 7, pp. 5712-5721, July 2018.
B. Liu, H. Yang and M. J. Lancaster, “Synthesis of Coupling Matrix for Diplexers Based on a Self-Adaptive Differential Evolution Algorithm,” in IEEE Transactions on Microwave Theory and Techniques, vol. 66, no. 2, pp. 813-821, Feb. 2018.
B. Liu, H. Yang and M. J. Lancaster, “Global Optimization of Microwave Filters Based on a Surrogate Model-Assisted Evolutionary Algorithm,” in IEEE Transactions on Microwave Theory and Techniques, vol. 65, no. 6, pp. 1976-1985, June 2017.
B. Liu, S. Koziel, and N. Ali, “SADEA-II: A Generalized Method for Efficient Global Optimization of Antenna Design,” in Journal of Computational Design and Engineering, vol. 4, no. 2, pp. 86–97, April 2017.
B. Liu, Q. Zhang and G. G. E. Gielen, “A Gaussian Process Surrogate Model Assisted Evolutionary Algorithm for Medium Scale Expensive Optimization Problems,” in IEEE Transactions on Evolutionary Computation, vol. 18, no. 2, pp. 180-192, April 2014.
B. Liu, D. Zhao, P. Reynaert and G. G. E. Gielen, “GASPAD: A General and Efficient mm-Wave Integrated Circuit Synthesis Method Based on Surrogate Model Assisted Evolutionary Algorithm,” in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 33, no. 2, pp. 169-182, Feb. 2014.
B. Liu, H. Aliakbarian, Z. Ma, G. A. E. Vandenbosch, G. Gielen, and P. Excell, “An Efficient Method for Antenna Design Optimization Based on Evolutionary Computation and Machine Learning Techniques,” in IEEE Transactions on Antennas and Propagation, vol. 62, no. 1, pp. 7-18, Jan. 2014.
B. Liu, G. Gielen and F.V. Fernández. Automated design of analog and high-frequency circuits. A computational intelligence approach, Springer: Berlin, Heidelberg, 2014, pp.978-3.
B. Liu, Q. Zhang, F. V. Fernández and G. G. E. Gielen, “An Efficient Evolutionary Algorithm for Chance-Constrained Bi-Objective Stochastic Optimization,” in IEEE Transactions on Evolutionary Computation, vol. 17, no. 6, pp. 786-796, Dec. 2013.
B. Liu, N. Deferm, D. Zhao, P. Reynaert and G. G. E. Gielen, “An Efficient High-Frequency Linear RF Amplifier Synthesis Method Based on Evolutionary Computation and Machine Learning Techniques,” in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 31, no. 7, pp. 981-993, July 2012.
B. Liu, D. Zhao, P. Reynaert and G. G. E. Gielen, “Synthesis of Integrated Passive Components for High-Frequency RF ICs Based on Evolutionary Computation and Machine Learning Techniques,” in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 30, no. 10, pp. 1458-1468, Oct. 2011.
B. Liu, F. V. Fernández, G. G. E. Gielen, Castro-López, and E. Roca. “A Memetic Approach to the Automatic Design of High-performance Analog Integrated Circuits,” in ACM Transactions on Design Automation of Electronic Systems, vol. 14, no. 3, pp. 1-24, June 2009.
B. Liu, Y. Wang, Z. Yu, L. Liu, M. Li, Z. Wang, J. Lu, and F. V. Fernández. “Analog Circuit Optimization System based on Hybrid Evolutionary Algorithms,” in Integration, the VLSI Journal, vol. 42, no. 2, pp. 137-148, Feb. 2009.