Stable Model-Order Reduction of Active Circuits

4 Model order reduction in microelectronics

“…In [87] a projection framework is presented for constructing stable reduced macromodels for stable active linear circuits. To this end, the right-projection matrix V ∈ ℝ N×m is formed through implicitly matching the first m moments of the original circuit equations as described in Sections 4.3.5 and 4.3.6.…”

Model Reduction of Discrete-Time Interval Type-2 T–S Fuzzy Systems

“…R ij and R ij are matrices with appropriate dimensions. According to Schur complement, (9) is obtained by (37), which means that the error system (8) is asymptotically stable with an H ∞ norm error performance γ.…”

“…Model reduction is introduced to deal with such problem, which aims to find a simplified reduced-order model to approximate the original complex high-order model. Model reduction has been widely used in many engineering fields such as power systems [34], filters design [35], [36], circuit simulations [37], [38]. Besides, in the past few decades, many methods have been introduced for solving the problem of model reduction such as Hankel norm based methods [39], [40], H ∞ norm based methods [41], [42], H 2 norm based methods [43], and H 2 -H ∞ based methods [44].…”

Interpolatory Nonlinear Model Order Reduction and its Application in Circuit Simulation

“…Through Galerkin projection [65,86] and using the projection matrix Q which satisfies the above two conditions, the reduced order model (4.3a) and the original (4.1) will share the first m moments about the expansion frequency point s o . This is key to ensure that the reduced system will approximate the original system with good accuracy [49,69].…”

“…Model-order reduction (MOR) has proven to be an effective tool in reducing the computational complexity of simulating large systems. MOR has been successfully used in a broad spectrum of linear and nonlinear applications [41][42][43][44][45][46][47][48] such as microelectronics [39,40,[49][50][51], high-speed and RF circuits [52][53][54][55], uncertainty quantification [9,[56][57][58], electromagnetic [59,60] and thermal analysis [61]. The recent evolution of MOR techniques have also been fueled by their popularity and success in broader fields such as mechanical, biomedical, civil, and aerospace engineering [62][63][64].…”