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Design Optimization of 16-nm Bulk FinFET Technology via Geometric Programming
(2014-01-01)
Su, Ping-Hsun
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Li, Yiming
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傳播研究所
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Institute of Communication Studies
Design rule is an important interface between design and manufacturing. It becomes more complex as the process advances to 16-nm and beyond. Current approaches to generate design rules are empirical shrink and lithographic simulation. However, it is time-consuming and costly to revise design rules for performance boost and yield improvement after design rules are frozen. Early performance gains in early design rule development without cost increase and yield loss will benefit semiconductor industry. In this work, we for the first time consider 16-nm bulk FinFET standard cell performance, yield, area, and layout style simultaneously to optimize design rules to meet ITRS by using geometric programming. Optical proximity correction, and electromagnetic field and circuit simulations are performed for objective function evaluation. The result achieves more than 100%-delay and 50%-yield improvement without area change by this systematic and statistical approach.
A Novel Approach to Compact Model Parameter Extraction for Excimer Laser Annealed Complementary Thin Film Transistors
(2004-10-01)
Li, Yiming
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Yu, Shao-Ming
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資訊工程學系
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電信工程研究所
;
友訊交大聯合研發中心
A unified physical-based model parameter extraction technique for excimer laser annealed lower temperature polycrystalline silicon (LTPS) complementary thin film transistors (TFTs) is for the first time proposed. For two well-known compact models of LTPS TFT, Rensselaer Polytechnic Institute (RPI) V1 and V2 models, our approach sequentially optimizes the model parameters in the regions of linear, subthreshold, saturation, and leakage. Compared with the measured results, the extracted I-D - V-G, I-D - V-D, transconductance, and output conductance are within 3% of accuracy. The agreement with the experimental data is excellent for the n- and p-type LTPS TFTs with different length and width. This extraction technique bridges the fabrication of LTPS TFTs and the design of complementary system on panel circuits.
Hybrid evolutionary approach to optimal design of CMOS LNA integrated circuits
(2005)
Li, Yiming
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Chou, Hung-Mu
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電信工程研究所
;
Institute of Communications Engineering
In this work we propose a hybrid intelligent circuit optimization technique for low noise amplifier (LNA) circuit. This method combines with the genetic algorithm (GA), Levenberg-Maxquardt (LM) method, and circuit simulator to perform automatic LNA circuit optimization. For a given LNA circuit, the optimization method considers the electrical specification such as S parameters: S-11, S-12, S-21, S-22, K factor, the noise figure, and the input third-order intercept point, simultaneously. The optimization procedure starts with loading the necessary parameters for circuit simulation, and then calls the circuit simulator for circuit simulation and evaluation. Sixteen optimized parameters of the LNA circuit composed with 0.18 mu m metal-oxide-silicon filed effect transistors (MOS-FETs) are acquired by our developed optimization prototype, where the aforementioned seven specifications axe all matched. The proposed circuit optimization method shows its robustness and practicability on radio-frequency (RF) circuit and wireless system on chip (SoC) design.
A unified optimization framework for microelectronics industry
(2006)
Li, Yiming
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Chen, Cheng-Kai
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Cho, Yen-Yu
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電信工程研究所
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Institute of Communications Engineering
In this paper, an object-oriented unified optimization framework (UOF) for general problem optimization is proposed. Based on evolutionary algorithms, numerical deterministic methods, and C++ objective design, the UOF possesses real-world applications for various optimization problems. The UOF is with interface between defining a general problem and generic solver; hence the two areas are bridged. The UOF's components ate categorized into problem and solver parts, and they work independently. Therefore, the reusable high level code allows the adaptation to new problem and solver quickly. Practical applications in microelectronics industry confirm the ftinctionality of the developed unified optimization framework.
A unified optimization framework for real world problems
(2006)
Li, Yiming
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Yu, Shao-Ming
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電信工程研究所
;
Institute of Communications Engineering
In this paper, an object-oriented unified optimization framework (UOF) for general problem optimization is developed. Based on global evolutionary algorithms, numerical deterministic methods, and C++ objective design, the UOF itself has great potential to perform optimization operations on kind of problems. The UOF provides basic interfaces to define a general problem and generic solver;, hence the two different parts are bridged. The UOF components are categorized into problem and solver parts and work independently. Therefore, reutilization of the high level code allows an adaptation to new problem and solver quickly. We customize the UOF to deal with some real world optimization problems.
An efficient near-ML algorithm with SQRD for wireless MIMO communications in metro transportation systems
(2007)
Pan, Chien-Hung
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Lee, Ta-Sung
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Li, Yiming
;
電信工程研究所
;
Institute of Communications Engineering
A high-speed multiple input multiple output (NEMO) wireless communication system is proposed for metro transportation. In order to better mitigate the inter-antenna interference (LAI) which degrades the quality of the MIMO receiver, we propose an efficient hybrid multiuser detection (MUD) technique. This approach consists of a two-stage procedure to achieve the optimum multiuser detector (OMD) by an acceptable computational complexity. The first stage performs interference cancellation by using sorted QR decomposition (SQRD), and the second stage performs the genetic algorithm (GA). It has two significant advantages: 1) The SQRD scheme provides "good initial setting knowledge" to improve the fitness of the population for GA. 2) The effect of fitness calculation is obtained from the QR decomposition (QRD) of a MIMO channel. Simulation results demonstrate that the two-stage procedure obtains a gain of 3 dB to 25 dB than other well-known MUD schemes. The computational complexity of the two-stage procedure can be reduced by 30% with QRD than other fitness calculation scheme in GA-MUD.
High frequency property optimization of heterojunction bipolar transistors using geometric programming
(2007)
Li, Yiming
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Chen, Ying-Chieh
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電信工程研究所
;
Institute of Communications Engineering
In this work, we theoretically optimize the high frequency property of silicon-germanium heterojunction bipolar transistors (HBTs) using a geometry programming (GP) technique. It is known that the base transit time of semiconductor devices potentially is a function of doping profile, device geometry and materials which significantly dominate the high frequency properly of HBTs. To maximize the cut-off frequency of HBTs, the subject is formulated as a GP optimization problem by physically considering the base transit time as an object function. The GP model is solved numerically so that the cutoff frequency of HBT could be optimized for specified upper and lower bounds of the base doping concentration, and certain Ge composition. This work also shows that for 13% Ge profile, the cut-off frequency may reach a maximal value.
A comparative study of foreign direct investment flow using diffusion models
(2007)
Li, Yiming
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Chiang, Yi-Hui
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Yu, Shao-Ming
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Chiang, Su-Yun
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Hung, C. -H.
In this work, we apply an improvement dynamic model of the foreign direct investment (FDI) flow to analyze the evolution of FDI flow. In comparison with the fundamental growth model of FDI, the simulation result is further accurate if the asymmetric growth pattern and heterogeneity of the potential adopters are considered. According to the result, the internal influence dominates the growth of FDI flow from Taiwan to China during 2001-2006, taking the electronics industry for example.
A simulation-based hybrid optimization technique for low noise amplifier design automation
(2007)
Li, Yiming
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Yu, Shao-Ming
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Li, Yih-Lang
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電信工程研究所
;
Institute of Communications Engineering
In this paper, a simulation-based optimization technique for integrated circuit (IC) design automation is presented. Based on a genetic algorithm (GA), Levenberg-Marquardt (LM) method, and circuit simulator, a window-interfaced prototype of computer-aided design (CAD) is developed for IC design. Considering low noise amplifier (LNA) IC, we simultaneously evaluate specifications including S parameters, K factor, noise figure, and input third-order intercept point in the optimization process. If the simulated results meet the aforementioned constraints, the prototype outputs the optimized parameters. Otherwise, CAD activates GA for global optimization; simultaneously, LM method searches solutions with the results of GA. The prototype then calls a circuit simulator to compute and evaluate newer results until all specifications are matched. More than fifteen parameters including device sizes, passive components, and biasing conditions are optimized for the aforementioned constraints. For LNA IC with 0.18 mu m metal-oxide-silicon filed effect transistors, benchmark results confirm the functionality of the implemented prototype.
Simulation-based evolutionary method in antenna design optimization for WLAN and wireless communication applications
(2007)
Li, Yiming
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Yu, Shao-Ming
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Kuo, Yi-Ting
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Lie, Yih-Lang
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電信工程研究所
In this paper, a simulation-based optimization method for the design of antenna pattern applied for mobile broadcasting and the 802.11a WLAN is presented. Based on genetic algorithm (GA), numerical simulation, and unified optimization framework, the optimization methodology is proposed for the antenna design automation. A Z-shaped antenna is optimized with respect to the return loss by our method. Inspired by the scenario of GA for optical proximity correction in our earlier work, we firstly partition the edges of the antenna into segments, and then adjust the movements of each segment to construct the geometry of new antenna. The external simulator is then called to simulate and evaluate the performance of the new antenna. If the simulated results meet the specific constraints, we output the optimized antenna. Otherwise, the evolutionary algorithms will enable us to search solution again. Our preliminary results confirm the robustness and efficiency of the proposed simulation-based optimization method.

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