This paper presents a new approach to the test design of analog circuits, called characteristic observation inference (COI). The COI method considers parametric as well as catastrophic faults. A strict distinction between the operational environment, defined by the specifications of the circuit, and the test environment, defined by the test configuration and the test equipment, is introduced. A parametric fault model is developed that combines circuit specifications, statistical parameters reflecting parametric faults, and measurements of the circuit under test. These measurements are called characteristic observations. For each specification, a test inference criterion is computed using feature extraction and logistic discrimination analysis. From a set of such criteria the satisfaction or violation of the specifications can be inferred from characteristic observations. Based on these results, additional test criteria for catastrophic faults are determined using test set compaction. Moreover, measurement noise and parasitic effects, which crucially influence the test design, are systematically considered, and a physically interpretable sampling strategy is presented. The COI method applied to two different test designs yields very good results with respect to parametric faults as well as to catastrophic faults.

Earlier approaches dealt with the detection of catastrophic faults based on I/sub DD/ monitoring. Consideration of the more subtle parametric faults and the ADC quantization noise, however, is essential for high-quality analog testing. The paper presents a new design method for analog test of parametric and catastrophic faults by I/sub DD/ monitoring. ADC quantization noise is systematically considered throughout the method. Results prove its effectiveness.

Test design of analog circuits based on statistical methods for decision making is a topic of growing interest. The major problem of such statistical approaches with respect to industrial applicability concerns the confidence with which the determined test criteria can be applied in production testing. This mainly refers to the consideration of measurement noise, to the selected measurements, as well as to the required training and validation samples. These crucial topics are addressed in this paper. On exploiting experience from the statistical design of analog circuits and from pattern recognition methods, efficient solutions to these problems are provided. A very robust test design is achieved by systematically considering measurement noise, by selecting most significant measurements, and by using most meaningful samples. Moreover, parametric as well as catastrophic faults are covered on application of digital testing methods.

In this paper, a new approach to analog test design based on the circuit design process, called Characteristic Observation Inference (COI), is presented. In many situations, it is prohibitive to directly verify the circuit specifications due to the test equipment costs. Our approach considers a given universal set of reasonable input stimuli and measurements that can be performed with the given test equipment. From this universal set, a minimal number of measurements is automatically selected that represent a set of observations characterizing the state of the circuit under test with respect to parametric faults. A parametric fault model is introduced which is related to the individual circuit specifications. For each given circuit specification, a corresponding test inference criterion is computed, based on logistic discrimination analysis. By applying these criteria, the satisfaction or violation of the given circuit specifications can be inferred from the observations of the circuit under test. The COI method applied to a complex operational amplifier yields very encouraging simulated results with respect to parametric faults as well as to catastrophic faults.