TY - JOUR
T1 - An Efficient and General Automated Power Amplifier Design Method Based on Surrogate Model Assisted Hybrid Optimization Technique
AU - Liu, Bo
AU - Xue, Liyuan
AU - Fan, Haijun
AU - Ding, Yuan
AU - Imran, Muhammad A.
AU - Wu, Tao
PY - 2024/12/31
Y1 - 2024/12/31
N2 - In layout-level optimization-oriented power amplifier (PA) design, the need for a good quality initial design and the high computational cost of electromagnetic (EM) simulations are remaining challenges. To address these challenges, a new method called efficient and general Bayesian neural network (BNN)-assisted hybrid optimization algorithm for PA design (E-GASPAD), is proposed. The key innovations of E-GASPAD include the introduction of BNN to model the PA design landscape and a new hybrid optimization algorithm co-working with BNN prediction for efficient PA design optimization. The performance of E-GASPAD is demonstrated by a 27–31 GHz class-AB PA and a 24–31 GHz wideband Doherty PA. Considering around 30 design variables with wide search ranges, the complete set of PA performance specifications, and full-wave EM simulations, layout-level high-performance designs are obtained automatically within a few hundred simulations (i.e., less than 72 h).
AB - In layout-level optimization-oriented power amplifier (PA) design, the need for a good quality initial design and the high computational cost of electromagnetic (EM) simulations are remaining challenges. To address these challenges, a new method called efficient and general Bayesian neural network (BNN)-assisted hybrid optimization algorithm for PA design (E-GASPAD), is proposed. The key innovations of E-GASPAD include the introduction of BNN to model the PA design landscape and a new hybrid optimization algorithm co-working with BNN prediction for efficient PA design optimization. The performance of E-GASPAD is demonstrated by a 27–31 GHz class-AB PA and a 24–31 GHz wideband Doherty PA. Considering around 30 design variables with wide search ranges, the complete set of PA performance specifications, and full-wave EM simulations, layout-level high-performance designs are obtained automatically within a few hundred simulations (i.e., less than 72 h).
KW - Bayesian neural network (BNN)
KW - Doherty power amplifier (PA)
KW - evolutionary algorithm
KW - optimization
KW - PA
KW - surrogate modeling
KW - wideband
UR - http://www.scopus.com/inward/record.url?scp=85214124687&partnerID=8YFLogxK
U2 - 10.1109/TMTT.2024.3518913
DO - 10.1109/TMTT.2024.3518913
M3 - Article
SN - 0018-9480
SP - 1
EP - 12
JO - IEEE Transactions on Microwave Theory and Techniques
JF - IEEE Transactions on Microwave Theory and Techniques
ER -