Design of Gm-C wavelet filter for on-line epileptic EEG detection

Copyright © 2019 The Institute of Electronics, Information and Communication EngineersAnalog filter implementation of continuous wavelet transform is considered as a promising technique for on-line spike detection applied in wearable electroencephalogram system. This Letter proposes a novel method to construct analog wavelet base for analog wavelet filter design, in which the mathematical approximation model in frequency domain is built as an optimization problem and the genetic algorithm is used to find the global optimum resolution. Also, the Gm-C filter structure based on LC ladder simulation is employed to synthesize the obtained analog wavelet base. The Marr wavelet filter is designed as an example using SMIC 1V 0.35μm CMOS technology. Simulation results show that the proposed method can give a stable analog wavelet filter with higher approximation accuracy and excellent circuit performance, which is well suited for the design of low-frequency low-power spike detector.Peer reviewe

Time-domain optimization of amplifiers based on distributed genetic algorithms

Thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Electrical and Computer EngineeringThe work presented in this thesis addresses the task of circuit optimization, helping the designer facing the high performance and high efficiency circuits demands of the market and technology evolution. A novel framework is introduced, based on time-domain analysis, genetic algorithm optimization, and distributed processing. The time-domain optimization methodology is based on the step response of the amplifier. The main advantage of this new time-domain methodology is that, when a given settling-error is reached within the desired settling-time, it is automatically guaranteed that the amplifier has enough open-loop gain, AOL, output-swing (OS), slew-rate (SR), closed loop bandwidth and closed loop stability. Thus, this simplification of the circuit‟s evaluation helps the optimization process to converge faster. The method used to calculate the step response expression of the circuit is based on the inverse Laplace transform applied to the transfer function, symbolically, multiplied by 1/s (which represents the unity input step). Furthermore, may be applied to transfer functions of circuits with unlimited number of zeros/poles, without approximation in order to keep accuracy. Thus, complex circuit, with several design/optimization degrees of freedom can also be considered. The expression of the step response, from the proposed methodology, is based on the DC bias operating point of the devices of the circuit. For this, complex and accurate device models (e.g. BSIM3v3) are integrated. During the optimization process, the time-domain evaluation of the amplifier is used by the genetic algorithm, in the classification of the genetic individuals. The time-domain evaluator is integrated into the developed optimization platform, as independent library, coded using C programming language. The genetic algorithms have demonstrated to be a good approach for optimization since they are flexible and independent from the optimization-objective. Different levels of abstraction can be optimized either system level or circuit level. Optimization of any new block is basically carried-out by simply providing additional configuration files, e.g. chromosome format, in text format; and the circuit library where the fitness value of each individual of the genetic algorithm is computed. Distributed processing is also employed to address the increasing processing time demanded by the complex circuit analysis, and the accurate models of the circuit devices. The communication by remote processing nodes is based on Message Passing interface (MPI). It is demonstrated that the distributed processing reduced the optimization run-time by more than one order of magnitude. Platform assessment is carried by several examples of two-stage amplifiers, which have been optimized and successfully used, embedded, in larger systems, such as data converters. A dedicated example of an inverter-based self-biased two-stage amplifier has been designed, laid-out and fabricated as a stand-alone circuit and experimentally evaluated. The measured results are a direct demonstration of the effectiveness of the proposed time-domain optimization methodology.Portuguese Foundation for the Science and Technology (FCT

Accurate Settling-Time Modeling and Design Procedures for Two-Stage Miller-Compensated Amplifiers for Switched-Capacitor Circuits

We present modeling techniques for accurate estimation of settling errors in switched-capacitor (SC) circuits built with Miller-compensated operational transconductance amplifiers (OTAs). One distinctive feature of the proposal is the computation of the impact of signal levels (on both the model parameters and the model structure) as they change during transient evolution. This is achieved by using an event-driven behavioral approach that combines small- and large-signal behavioral descriptions and keeps track of the amplifier state after each clock phase. Also, SC circuits are modeled under closed-loop conditions to guarantee that the results remain close to those obtained by electrical simulation of the actual circuits. Based on these models, which can be regarded as intermediate between the more established small-signal approach and full-fledged simulations, design procedures for dimensioning SC building blocks are presented whose targets are system-level specifications (such as ENOB and SNDR) instead of OTA specifications. The proposed techniques allow to complete top-down model-based designs with 0.3-b accuracy.Ministerio de Educación y Ciencia TEC2006-03022Junta de Andalucía TIC-0281

Automating defects simulation and fault modeling for SRAMs

The continues improvement in manufacturing process density for very deep sub micron technologies constantly leads to new classes of defects in memory devices. Exploring the effect of fabrication defects in future technologies, and identifying new classes of realistic functional fault models with their corresponding test sequences, is a time consuming task up to now mainly performed by hand. This paper proposes a new approach to automate this procedure. The proposed method exploits the capabilities of evolutionary algorithms to automatically identify faulty behaviors into defective memories and to define the corresponding fault models and relevant test sequences. Target defects are modeled at the electrical level in order to optimize the results to the specific technology and memory architecture

유전알고리즘 및 강화학습을 사용한 고속 회로 설계 자동화 프레임워크

학위논문(석사) -- 서울대학교대학원 : 융합과학기술대학원 지능정보융합학과, 2022.2. 전동석.Although design automation is a key enabler of modern large-scale digital systems, automating the transistor-level circuit design process still remains a challenge. Some recent works suggest that deep learning algorithms could be adopted to find optimal transistor dimensions in relatively small circuitry such as analog amplifiers. However, those approaches are not capable of exploring different circuit structures to meet the given design constraints. In this work, we propose an automatic circuit design framework that can generate practical circuit structures from scratch as well as optimize the size of each transistor, considering performance and reliability. We employ the framework to design level shifter circuits, and the experimental results show that the framework produces novel level shifter circuit topologies and the automatically optimized designs achieve 2.8-5.3× lower PDP than prior arts designed by human experts.설계 자동화는 대규모 디지털 시스템을 가능하게 하는 핵심 요소이지만 트랜지스터 수준에서 회로 설계 프로세스를 자동화하는 것은 여전히 어려운 과제로 남아 있습니다. 최근 연구에서는 아날로그 앰프와 같은 비교적 작은 회로에서 최적의 성능을 보이는 트랜지스터 크기를 찾기 위해 deep learning 알고리즘을 활용할 수 있다고 말합니다. 그러나 이러한 접근 방식은 주어진 설계 constraint를 충족하는 다른 회로 구조 탐색에 적용하기 어렵습니다. 본 연구에서는 성능과 신뢰성을 고려하여 각 트랜지스터의 크기를 최적화할 뿐만 아니라 처음부터 실용적인 회로 구조를 생성할 수 있는 자동 회로 설계 framework를 제안합니다. 우리는 framework를 사용하여 level shifter 회로를 설계했으며 실험 결과는 프레임워크가 새로운 level shifter 회로 토폴로지를 생성하고 자동으로 최적화된 설계가 인간 전문가가 설계한 선행 기술보다 2.8-5.3배 더 낮은 PDP를 달성한다는 것을 보여줍니다.Abstract i Contents ii List of Tables iv List of Figures v List of Algorithms vi 1 Introduction 1 2 Related work 6 2.1 Genetic Algorithm 6 2.2 NeuroEvolution of Augmenting Topologies (NEAT) 7 2.3 Reinforcement Learning (RL) 10 2.4 DDPG, D4PG, and PPO 12 2.5 Level Shifter 14 3 Proposed circuit design framework 17 3.1 Topology Generator 17 3.2 Circuit Optimizer 25 4 Experiment Result 32 4.1 Level Shifter Design 32 4.2 Topology Generation 34 4.3 Circuit Optimization 36 4.4 Test Chip Fabrication 42 4.5 Applicability of Topology Generator 47 5 Conclusion 50 Abstract (In Korean) 57석