An Efficient Kriging-based Constrained Multi-objective Evolutionary Algorithm for Analog Circuit Synthesis via Self-adaptive Incremental Learning

Automatic Design of a Broadband Directional Coupler via Bayesian Optimization

“…Local BO, sparse GPR model [85] GNN + WEI Amp (C-2, D-8), driver (C-1, D-6) Parasitic-aware, GNN w/ dropout [86] GPR + Acq ensemble Amp-1,2,3 (C-10, C-12, C-24), etc., Batch BO enabled by the ensemble [87] Add-GPR + UCB Amp, DC (C-na) LDE-aware, high-dimensional [89] GPR + WEI Op-Amp (C-na) Bi-level BO, compensation design [90] GPR + EI Amp-1,2 (C-10, C-12) Local penalization 1 [92] GPR + modified TS Amp-1,2 (C-11, C-43) Applied to technology migration [105] Online GPR Op-Amp-1,2 (C-11, C-21) Self-adaptive incremental learning [102] GPR + wPESC Amp-1,2 (C-10, C-11) Automatically choose test benches [103] GPR + EIM Op-Amp-1,2 (C-11, C-21) Asynchronous BO [104] Online GPR + EIM Op-Amp-1,2 (C-11, C-26), etc., Self-adaptive incremental learning [100] GPR + LCB/EI CP (C-36), Amp (C-12) Search in one-dimensional subspace [101] MT-GPR + EI Transformer (C-4), LNA (C-15), etc., Multitask NN as GPR kernel [229] GPR + WEI 5 OTAs, 2 VCOs, 2 SCFs (D-na), etc., Wire sizing, GPR guided by GNN [96] GPR + LCB Voltage regulator (C-17 + D-10), etc., Novel evolutionary algorithm [97] GPR + TS LNA (C/D-17) 2…”

“…While the basic problem formulation remains unchanged, a variety of new and effective optimization techniques have emerged. Among these methods, Bayesian optimization (BO) stands out as particularly attractive in recent decades [72]- [105]. This section will review the utilization of BO for a diverse range of optimization challenges in circuit design.…”

“…The core of Bayesian approaches relies on Bayes' theorem, where people encode their knowledge about the problem in a prior distribution, and then a posterior distribution is derived from it after observing some data [56], [57]. Since its introduction, we have witnessed a large number of works successfully applying Bayesian methods to address stochastic problems, initially in circuit modeling and analysis under process variation [49]- [51], [58]- [70], and later in circuit optimization [71]- [105].…”