Test generation based diagnosis of device parameters for analog circuits
Proceedings of the conference on Design, automation and test in Europe
Fault Models and Test Generation for OpAmp Circuits—The FFM
Journal of Electronic Testing: Theory and Applications
Journal of Electronic Testing: Theory and Applications
Structural Fault Based Specification Reduction for Testing Analog Circuits
Journal of Electronic Testing: Theory and Applications
Prediction of Analog Performance Parameters Using Oscillation Based Test
VTS '04 Proceedings of the 22nd IEEE VLSI Test Symposium
Journal of Electronic Testing: Theory and Applications
Optimized wafer-probe and assembled package test design for analog circuits
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Statistical Test Compaction Using Binary Decision Trees
IEEE Design & Test
Study of an Electrical Setup for Capacitive MEMS Accelerometers Test and Calibration
Journal of Electronic Testing: Theory and Applications
Test Generation Algorithm for Linear Systems Based on Genetic Algorithm
Journal of Electronic Testing: Theory and Applications
System-level specification testing of wireless transceivers
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Reducing test cost of integrated, heterogeneous systems using pass-fail test data analysis
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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ATPG approaches for analog circuits in the past have targeted the testing of catastrophic and parametric faults. It has been shown recently that analog circuit specifications can be predicted from the transient response of the circuit under test.In this paper, we present a new ATPG algorithm for synthesizing a test stimulus that enables accurate prediction of circuit specifications from its response to the test stimulus. Use of simple linear models for ATPG and more complex nonlinear models for specification prediction results in a test generation procedure that is both accurate and simulation efficient. Due to the incorporation of measurement noise during test optimization, robust specification prediction is possible.