PARAMETRIC DEVIATION BASED ANALOG TEST AND DIAGNOSIS SYSTEM

2011 ◽  
Vol 20 (07) ◽  
pp. 1323-1340 ◽  
Author(s):  
KASTURI GHOSH ◽  
ARABINDA ROY ◽  
SEKHAR MONDAL ◽  
BAIDYANATH RAY
Keyword(s):  
Cellular Automata ◽  
Analog Circuits ◽  
Significant Enhancement ◽  
Performance Parameters ◽  
Analog Test ◽  
Diagnosis System ◽  
Response Data ◽  
Circuit Components ◽  
Diagnostic Resolution

This paper reports a comprehensive solution for the problem of test and diagnosis of OTA based analog circuits. Based on the parametric deviation of circuit components, a test and diagnosis methodology are proposed. Compressed signature generated out of multiple performance parameters has resulted in significant enhancement in fault diagnosing capability. The voluminous response data has been handled with Cellular Automata (CA) based classifier to achieve excellent diagnostic resolution.

Optimization of amplifier circuits by using gradient boosted trees and probability annealing policy

2020 ◽  
Vol 15 (3) ◽  
pp. 1-5
Author(s):  
Evelyn Cristina de Oliveira Lima ◽  
André Borges Cavalcante ◽  
João Viana Da Fonseca Neto
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Analog Circuits ◽  
Design Automation ◽  
Optimization Problem ◽  
State Of The Art ◽  
Circuit Parameter ◽  
Learning Module ◽  
Circuit Components ◽  
Parameter Values

One important step of the optimization of analog circuits is to properly size circuit components. Since the quantities that define specification may compete for different circuit parameter values, the optimization of analog circuits befits a hard and costly optimization problem. In this work, we propose two contributions to design automation methodologies based on machine learning. Firstly, we propose a probability annealing policy to boost early data collection and restrict electronic simulations later on in the optimization. Secondly, we employ multiple gradient boosted trees to predict design superiority, which reduces overfitting to learned designs. When compared to the state-of-the art, our approach reduces the number of electronic simulations, the number of queries made to the machine learning module required to finish the optimization.

Qualitative dynamic diagnosis of circuits

1993 ◽  
Vol 7 (1) ◽  
pp. 53-64 ◽  
Author(s):  
Alessandra Fanni ◽  
Paolo Diana ◽  
Alessandro Giua ◽  
Marco Perezzani
Keyword(s):  
Analog Circuits ◽  
Qualitative Data ◽  
Diagnostic System ◽  
Data Conversion ◽  
Qualitative Dynamic ◽  
Qualitative State ◽  
Test Points ◽  
Circuit Components ◽  
Localized Faults ◽  
Dynamic Diagnosis

We describe ACDS, an automatic diagnostic system. ACDS is capable of diagnosing faults on analog circuits in dynamic conditions. The circuit's dynamic behavior is studied by means of a series of intrastate simulations during which the qualitative state of the circuit does not change. An acquistion board collects the value of a set of quantities corresponding to accessible test points. These measurements are converted into qualitative values and are used for two purposes: first, to determine the state of the circuit components; second, to trigger the diagnostic procedure whenever a discrepancy between observed and predicted behavior is found. The main difficulty in this phase of measurement interpretation is in obtaining meaningful numerical-qualitative data conversion for values of quantities approaching a boundary between two different qualitative intervals. System performance has been verified through a number of simulations, which have shown the proposed approach to be efficient both in terms of localized faults and of flexibility in adapting to different circuits.

scholarly journals A new adaptive analog test and diagnosis system

2000 ◽  
Vol 49 (2) ◽  
pp. 223-227 ◽  
Author(s):  
E.F. Cota ◽  
M. Negreiros ◽  
L. Carro ◽  
M. Lubaszewski
Keyword(s):  
Analog Test ◽  
Diagnosis System

scholarly journals Reliable Low Voltage Circuit Design Techniques

2017 ◽  
Vol 3 (4) ◽  
pp. 32
Author(s):  
P. John Paul ◽  
Raj N
Keyword(s):  
Circuit Design ◽  
Analog Circuits ◽  
Low Voltage ◽  
Floating Gate ◽  
Performance Parameters ◽  
Operational Transconductance Amplifier ◽  
Transconductance Amplifier ◽  
Signal Path ◽  
Simulation Results ◽  
Design Techniques

In this paper, non-conventional circuit design techniques has been reviewed. The techniques discussed are widely used for realizing low voltage low power analog circuits. The discussed techniques in this paper are: Bulk Driven, Floating and Quasi-floating Gate followed by operating of Bulk Driven MOSFET in Floating and Quasi-floating Gate mode. In all the approach, the threshold voltage restriction is removed or reduced from the input signal path thereby reducing the power consumption. However, the adverse effect lies is terms of reduced performance parameters of MOSFET compared to conventional gate driven MOSFET parameters as shown in this paper through simulation results. The comparative analysis of MOSFET parameters results in encouragement of two approaches: Quasi-floating Gate and Bulk Driven Quasi-floating Gate MOSFET. Each of these approaches has its advantage in specific domains. Further in this paper, an Operational Transconductance Amplifier is proposed which use the Bulk Driven Quasi-floating Gate MOSFET technique and the same is amplifier under similar conditions is also realized using Bulk Driven MOSFET so as to highlight the advantage of Bulk Driven  Quasi-floating Gate MOSFET over Bulk Driven MOSFET. All the performances metrics are achieved with the help of HSpice simulator using MOSFET models of 180nm technology provided by UMC.

Fault Tolerant Reversible Gate Based Sequential Quantum Dot Cellular Automata Circuits: Design and Contemplation

2020 ◽  
Vol 15 (3) ◽  
pp. 331-344 ◽  
Author(s):  
Rupali Singh ◽  
Devendra Kumar Sharma
Keyword(s):  
Cellular Automata ◽  
Quantum Dot ◽  
Low Power ◽  
Cost Function ◽  
High Speed ◽  
Superior Performance ◽  
Performance Parameters ◽  
Quantum Dot Cellular Automata ◽  
Reversible Gate ◽  
Cost Efficient

In the era of quantum computing, Quantum Dot Cellular Automata (QCA) is a phenomenal technology which can produce low power, high speed and area efficient circuits. On the other hand, reversible logic is a promising paradigm which is used to construct low power circuits. This paper presents a design of a unique reversible gate based on QCA. This gate can facilitate the design of complex, cost efficient sequential circuits. The proposed gate is examined for various performance parameters such as realization of standard Boolean functions, cost function, energy dissipation and fault characterization. It is observed that the proposed gate exhibits superior performance as compared to the previously reported cost efficient designs in all the performance parameters. Furthermore, to evaluate the efficacy of the proposed QCA gate, reversible sequential latches are designed. The proposed structures of latches excel over the similar existing designs and have shown 50% improvement in latency, 58% improvement in effective cell area and around 70% improvement in cost function. The proposed latches are further investigated for temperature alterations to find the operating range of temperature for the circuits. The reversible QCA gate, proposed in this paper can be effectively used to design D latch, T latch, JK latch with improved performance. Hence, the proposed gate can find extensive scope in designing cost effective, low power, reversible sequential and combinational circuits.

scholarly journals A symmetry based anomaly detection in brain using cellular automata for computer aided diagnosis

2018 ◽  
Vol 14 (1) ◽  
pp. 471
Author(s):  
Nisha V M ◽  
L Jeganathan
Keyword(s):  
Cellular Automata ◽  
Early Stage ◽  
Computer Aided Diagnosis ◽  
Brain Images ◽  
Cellular Automata Model ◽  
Computing Power ◽  
Diagnosis System ◽  
Proposed Model ◽  
Computer Aided ◽  
Aided Diagnosis

Computer aided diagnosis (CAD) is an advancing technology in medical imaging. CAD acts as an additional computing power for doctors to interpret the medical images which leads to a more accurate diagnosis of the disease.CAD system increases the chances of detection of brain lesions by assisting the physicians in decreasing the observational oversight in the early stage of diseases.This paper focuses on the development of a cellular automata based model to find the anomaly prone areas in human brains.Because of the bilateral symmetric nature of human brain, a symmetry based cellular automata model is proposed.An algorithm is designed based on the proposed model to detect the anomaly prone areas in brain images. The proposed model can be a standalone model or it can be incorporated to a sophisticated computer aided diagnosis system. By incorporating asymmetry information into a computer aided diagnosis system, enhances its performance in identifying the anomalies exists in bilaterally symmetrical brain images.

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