| Title | Every Test Makes a Difference: Compressing Analog Tests to Decrease Production Costs |
| Author | Seyed Nematollah Ahmadyan (University of Illinois at Urbana-Champaign, U.S.A.), Suriyaprakash Natarajan (Intel, U.S.A.), *Shobha Vasudevan (University of Illinois at Urbana-Champaign, U.S.A.) |
| Page | pp. 539 - 544 |
| Keyword | Stress Test, Compression, Random tree, Optimization |
| Abstract | We introduce a methodology for automated test compression during electrical stress testing of analog and mixed signal circuits. This methodology optimally extracts only portions of a functional test that electrically stress the nets and devices of an analog circuit. We model test compression as a problem of optimizing functional of the transient response. We present a random tree based approach to find optimal solutions for these computationally hard integrals. We demonstrate with an op-amp, VCO and CMOS inverter that the method consistently reduces the length of each test by an average of 93%. |
| Title | Re-thinking Polynomial Optimization: Efficient Programming of Reconfigurable Radio Frequency (RF) Systems by Convexification |
| Author | Fa Wang, Shihui Yin, Minhee Jun, *Xin Li, Tamal Mukherjee, Rohit Negi, Larry Pileggi (Carnegie Mellon University, U.S.A.) |
| Page | pp. 545 - 550 |
| Keyword | Polynomial Optimization, Sequential Semidefinite Programming |
| Abstract | Reconfigurable radio frequency (RF) system has emerged as a promising avenue to achieve high communication performance while adapting to versatile commercial wireless environment. In this paper, we propose a novel technique to optimally program a reconfigurable RF system in order to achieve maximum performance and/or minimum power. Our key idea is to adopt an equation-based optimization method that relies on general purpose, non-convex polynomial performance models to determine the optimal configurations of all tunable circuit blocks. Most importantly, our proposed approach guarantees to find the globally optimal solution of the non-convex polynomial programming problem by solving a sequence of convex semidefinite programming (SDP) problems based on convexification. A reconfigurable RF front-end example designed for WLAN 802.11g demonstrates that the proposed method successfully finds the globally optimal configuration, while other traditional techniques often converge to local optima. |
| Title | An Efficient Trajectory-based Algorithm for Model Order Reduction of Nonlinear Systems via Localized Projection and Global Interpolation |
| Author | Chenjie Yang, *Fan Yang, Xuan Zeng (Fudan University, China), Dian Zhou (Fudan University, University of Texas at Dallas, China) |
| Page | pp. 551 - 556 |
| Keyword | Trajectory, Model Order Reduction |
| Abstract | In this paper, we propose a new, efficient trajectory- based model order reduction algorithm for nonlinear systems via localized projection and global interpolation. We employ an efficient procedure to transform the smaller localized reduced-order models into a set of equivalent reduced-order models with consistent global coordinate. The reduced-orders for the nonlinear systems are then obtained by globally interpolating the much smaller localized reduced-order models. |
| Title | STORM: A Nonlinear Model Order Reduction Method via Symmetric Tensor Decomposition |
| Author | Jian Deng, Haotian Liu, Kim Batselier, Yu-Kwong Kwok, *Ngai Wong (The University of Hong Kong, Hong Kong) |
| Page | pp. 557 - 562 |
| Keyword | circuit modeling, nonlinear circuit, model order reduction, symmetric tensor decomposition, polynomial system |
| Abstract | In this paper, a novel symmetric tensor-based order-reduction method (STORM) is presented for simulating large-scale nonlinear systems. Compared to the recent tensor-based nonlinear model order reduction (TNMOR) algorithm, STORM shows advantages in two aspects. First, STORM avoids the assumption of the existence of a low-rank tensor approximation. Second, with the use of the symmetric tensor decomposition, STORM allows significantly faster computation and less storage complexity than TNMOR. Numerical experiments demonstrate the superior computational efficiency and accuracy of STORM. |