Analysis of fault-tolerant neurocontrol architectures

The fault-tolerance of analog parallel distributed implementations of a multivariable aircraft neurocontroller is analyzed by simulating weight and neuron failures in a simplified scheme of analog processing based on the functional architecture of the ETANN chip (Electrically Trainable Artificial Neural Network). The neural information processing is found to be only partially distributed throughout the set of weights of the neurocontroller synthesized with the backpropagation algorithm. Although the degree of distribution of the neural processing, and consequently the fault-tolerance of the neurocontroller, could be enhanced using Locally Distributed Weight and Neuron Approaches, a satisfactory level of fault-tolerance could only be obtained by retraining the degrated VLSI neurocontroller. The possibility of maintaining neurocontrol performance and stability in the presence of single weight of neuron failures was demonstrated through an automated retraining procedure of the neurocontroller based on a pre-programmed choice and sequence of the training parameters

Offset-compensated comparator with full-input range in 150nm FDSOI CMOS-3d technology

This paper addresses an offset-compensated comparator with full-input range in the 150nm FDSOI CMOS- 3D technology from MIT- Lincoln Laboratory. The comparator discussed here makes part of a vision system. Its architecture is that of a self-biased inverter with dynamic offset correction. At simulation level, the comparator can reach a resolution of 0.1mV in an area of approximately 220μm2 with a time response of less than 40ns and a static power dissipation of 1.125μW

Time-of-flight CMOS イメージセンサによる高精度・高速距離イメージングに関する研究

Tohoku University博士（工学）thesi

Power-efficient current-mode analog circuits for highly integrated ultra low power wireless transceivers

In this thesis, current-mode low-voltage and low-power techniques have been applied to implement novel analog circuits for zero-IF receiver backend design, focusing on amplification, filtering and detection stages. The structure of the thesis follows a bottom-up scheme: basic techniques at device level for low voltage low power operation are proposed in the first place, followed by novel circuit topologies at cell level, and finally the achievement of new designs at system level. At device level the main contribution of this work is the employment of Floating-Gate (FG) and Quasi-Floating-Gate (QFG) transistors in order to reduce the power consumption. New current-mode basic topologies are proposed at cell level: current mirrors and current conveyors. Different topologies for low-power or high performance operation are shown, being these circuits the base for the system level designs. At system level, novel current-mode amplification, filtering and detection stages using the former mentioned basic cells are proposed. The presented current-mode filter makes use of companding techniques to achieve high dynamic range and very low power consumption with for a very wide tuning range. The amplification stage avoids gain bandwidth product achieving a constant bandwidth for different gain configurations using a non-linear active feedback network, which also makes possible to tune the bandwidth. Finally, the proposed current zero-crossing detector represents a very power efficient mixed signal detector for phase modulations. All these designs contribute to the design of very low power compact Zero-IF wireless receivers. The proposed circuits have been fabricated using a 0.5μm double-poly n-well CMOS technology, and the corresponding measurement results are provided and analyzed to validate their operation. On top of that, theoretical analysis has been done to fully explore the potential of the resulting circuits and systems in the scenario of low-power low-voltage applications.Programa Oficial de Doctorado en Tecnologías de las Comunicaciones (RD 1393/2007)Komunikazioen Teknologietako Doktoretza Programa Ofiziala (ED 1393/2007

Neuromorphic electronic systems

Biological in formation-processing systems operate on completely different principles from those with which most engineers are familiar. For many problems, particularly those in which the input data are ill-conditioned and the computation can be specified in a relative manner, biological solutions are many orders of magnitude more effective than those we have been able to implement using digital methods. This advantage can be attributed principally to the use of elementary physical phenomena as computational primitives, and to the representation of information by the relative values of analog signals, rather than by the absolute values of digital signals. This approach requires adaptive techniques to mitigate the effects of component differences. This kind of adaptation leads naturally to systems that learn about their environment. Large-scale adaptive analog systems are more robust to component degradation and failure than are more conventional systems, and they use far less power. For this reason, adaptive analog technology can be expected to utilize the full potential of wafer-scale silicon fabrication