Quiescent current testing of CMOS data converters

Power supply quiescent current (IDDQ) testing has been very effective in VLSI circuits designed in CMOS processes detecting physical defects such as open and shorts and bridging defects. However, in sub-micron VLSI circuits, IDDQ is masked by the increased subthreshold (leakage) current of MOSFETs affecting the efficiency of I¬DDQ testing. In this work, an attempt has been made to perform robust IDDQ testing in presence of increased leakage current by suitably modifying some of the test methods normally used in industry. Digital CMOS integrated circuits have been tested successfully using IDDQ and IDDQ methods for physical defects. However, testing of analog circuits is still a problem due to variation in design from one specific application to other. The increased leakage current further complicates not only the design but also testing. Mixed-signal integrated circuits such as the data converters are even more difficult to test because both analog and digital functions are built on the same substrate. We have re-examined both IDDQ and IDDQ methods of testing digital CMOS VLSI circuits and added features to minimize the influence of leakage current. We have designed built-in current sensors (BICS) for on-chip testing of analog and mixed-signal integrated circuits. We have also combined quiescent current testing with oscillation and transient current test techniques to map large number of manufacturing defects on a chip. In testing, we have used a simple method of injecting faults simulating manufacturing defects invented in our VLSI research group. We present design and testing of analog and mixed-signal integrated circuits with on-chip BICS such as an operational amplifier, 12-bit charge scaling architecture based digital-to-analog converter (DAC), 12-bit recycling architecture based analog-to-digital converter (ADC) and operational amplifier with floating gate inputs. The designed circuits are fabricated in 0.5 μm and 1.5 μm n-well CMOS processes and tested. Experimentally observed results of the fabricated devices are compared with simulations from SPICE using MOS level 3 and BSIM3.1 model parameters for 1.5 μm and 0.5 μm n-well CMOS technologies, respectively. We have also explored the possibility of using noise in VLSI circuits for testing defects and present the method we have developed

[Delta] IDDQ testing of a CMOS 12-bit charge scaling digital-to-analog converter

This work presents design, implementation and test of a built-in current sensor (BICS) for ∆IDDQ testing of a CMOS 12-bit charge scaling digital-to-analog converter (DAC). The sensor uses power discharge method for the fault detection. The sensor operates in two modes, the test mode and the normal mode. In the test mode, the BICS is connected to the circuit under test (CUT) which is DAC and detects abnormal currents caused by manufacturing defects. In the normal mode, BICS is isolated from the CUT. The BICS is integrated with the DAC and is implemented in a 0.5 μm n-well CMOS technology. The DAC uses charge scaling method for the design and a low voltage (0 to 2.5 V) folded cascode op-amp. The built-in current sensor (BICS) has a resolution of 0.5 μA. Faults have been introduced into DAC using fault injection transistors (FITs). The method of ∆IDDQ testing has been verified both from simulation and experimental measurements

Voltage sensing based built-in current sensor for IDDQ test

Quiescent current leakage test of the VDD supply (IDDQ Test) has been proven an effective way to screen out defective chips in manufacturing of Integrated Circuits (IC). As technology advances, the traditional IDDQ test is facing more and more challenges. In this research, a practical built-in current sensor (BICS) is proposed and the design is verified by three generations of test chips. The BICS detects the signal by sensing the voltage drop on supply lines of the circuit under test (CUT). Then the sensor performs analog-to-digital conversion of the input signal using a stochastic process with scan chain readout. Self-calibration and digital chopping are used to minimize offset and low frequency noise and drift. This non-invasive procedure avoids any performance degradation of the CUT. The measurement results of test chips are presented. The sensor achieves a high IDDQ resolution with small chip area overhead. This will enable IDDQ of future technology generations

A Behavioral Model of a Built-in Current Sensor for IDDQ Testing

IDDQ testing is one of the most effective methods for detecting defects in integrated circuits. Higher leakage currents in more advanced semiconductor technologies have reduced the resolution of IDDQ test. One solution is to use built-in current sensors. Several sensor techniques for measuring the current based on the magnetic field or voltage drop across the supply line have been proposed. In this work, we develop a behavioral model for a built-in current sensor measuring voltage drop and use this model to better understand sensor operation, identify the effect of different parameters on sensor resolution, and suggest design modifications to improve future sensor performance

Variance reduction and outlier identification for IDDQ testing of integrated chips using principal component analysis

Integrated circuits manufactured in current technology consist of millions of transistors with dimensions shrinking into the nanometer range. These small transistors have quiescent (leakage) currents that are increasingly sensitive to process variations, which have increased the variation in good-chip quiescent current and consequently reduced the effectiveness of IDDQ testing. This research proposes the use of a multivariate statistical technique known as principal component analysis for the purpose of variance reduction. Outlier analysis is applied to the reduced leakage current values as well as the good chip leakage current estimate, to identify defective chips. The proposed idea is evaluated using IDDQ values from multiple wafers of an industrial chip fabricated in 130 nm technology. It is shown that the proposed method achieves significant variance reduction and identifies many outliers that escape identification by other established techniques. For example, it identifies many of the absolute outliers in bad neighborhoods, which are not detected by Nearest Neighbor Residual and Nearest Current Ratio. It also identifies many of the spatial outliers that pass when using Current Ratio. The proposed method also identifies both active and passive defects

Fault modelling and accelerated simulation of integrated circuits manufacturing defects under process variation

As silicon manufacturing process scales to and beyond the 65-nm node, process variation can no longer be ignored. The impact of process variation on integrated circuit performance and power has received significant research input. Variation-aware test, on the other hand, is a relatively new research area that is currently receiving attention worldwide.Research has shown that test without considering process variation may lead to loss of test quality. Fault modelling and simulation serve as a backbone of manufacturing test. This thesis is concerned with developing efficient fault modelling techniques and simulation methodologies that take into account the effect of process variation on manufacturing defects with particular emphasis on resistive bridges and resistive opens.The first contribution of this thesis addresses the problem of long computation time required to generate logic fault of resistive bridges under process variation by developing a fast and accurate modelling technique to model logic fault behaviour of resistive bridges.The new technique is implemented by employing two efficient voltage calculation algorithms to calculate the logic threshold voltage of driven gates and critical resistance of a fault-site to enable the computation of bridge logic faults without using SPICE. Simulation results show that the technique is fast (on average 53 times faster) and accurate (worst case is 2.64% error) when compared with HSPICE. The second contribution analyses the complexity of delay fault simulation of resistive bridges to reduce the computation time of delay fault when considering process variation. An accelerated delay fault simulation methodology of resistive bridges is developed by employing a three-step strategy to speed up the calculation of transient gate output voltage which is needed to accurately compute delay faults. Simulation results show that the methodology is on average 17.4 times faster, with 5.2% error in accuracy, when compared with HSPICE. The final contribution presents an accelerated simulation methodology of resistive opens to address the problem of long simulation time of delay fault when considering process variation. The methodology is implemented by using two efficient algorithms to accelerate the computation of transient gate output voltage and timing critical resistance of an open fault-site. Simulation results show that the methodology is on average up to 52 times faster than HSPICE, with 4.2% error in accuracy

Integrated circuit outlier identification by multiple parameter correlation

Semiconductor manufacturers must ensure that chips conform to their specifications before they are shipped to customers. This is achieved by testing various parameters of a chip to determine whether it is defective or not. Separating defective chips from fault-free ones is relatively straightforward for functional or other Boolean tests that produce a go/no-go type of result. However, making this distinction is extremely challenging for parametric tests. Owing to continuous distributions of parameters, any pass/fail threshold results in yield loss and/or test escapes. The continuous advances in process technology, increased process variations and inaccurate fault models all make this even worse. The pass/fail thresholds for such tests are usually set using prior experience or by a combination of visual inspection and engineering judgment. Many chips have parameters that exceed certain thresholds but pass Boolean tests. Owing to the imperfect nature of tests, to determine whether these chips (called "outliers") are indeed defective is nontrivial. To avoid wasted investment in packaging or further testing it is important to screen defective chips early in a test flow. Moreover, if seemingly strange behavior of outlier chips can be explained with the help of certain process parameters or by correlating additional test data, such chips can be retained in the test flow before they are proved to be fatally flawed. In this research, we investigate several methods to identify true outliers (defective chips, or chips that lead to functional failure) from apparent outliers (seemingly defective, but fault-free chips). The outlier identification methods in this research primarily rely on wafer-level spatial correlation, but also use additional test parameters. These methods are evaluated and validated using industrial test data. The potential of these methods to reduce burn-in is discussed

Programmable CMOS Analog-to-Digital Converter Design and Testability

In this work, a programmable second order oversampling CMOS delta-sigma analog-to-digital converter (ADC) design in 0.5µm n-well CMOS processes is presented for integration in sensor nodes for wireless sensor networks. The digital cascaded integrator comb (CIC) decimation filter is designed to operate at three different oversampling ratios of 16, 32 and 64 to give three different resolutions of 9, 12 and 14 bits, respectively which impact the power consumption of the sensor nodes. Since the major part of power consumed in the CIC decimator is by the integrators, an alternate design is introduced by inserting coder circuits and reusing the same integrators for different resolutions and oversampling ratios to reduce power consumption. The measured peak signal-to-noise ratio (SNR) for the designed second order delta-sigma modulator is 75.6dB at an oversampling ratio of 64, 62.3dB at an oversampling ratio of 32 and 45.3dB at an oversampling ratio of 16. The implementation of a built-in current sensor (BICS) which takes into account the increased background current of defect-free circuits and the effects of process variation on ΔIDDQ testing of CMOS data converters is also presented. The BICS uses frequency as the output for fault detection in CUT. A fault is detected when the output frequency deviates more than ±10% from the reference frequency. The output frequencies of the BICS for various model parameters are simulated to check for the effect of process variation on the frequency deviation. A design for on-chip testability of CMOS ADC by linear ramp histogram technique using synchronous counter as register in code detection unit (CDU) is also presented. A brief overview of the histogram technique, the formulae used to calculate the ADC parameters, the design implemented in 0.5µm n-well CMOS process, the results and effectiveness of the design are described. Registers in this design are replaced by 6T-SRAM cells and a hardware optimized on-chip testability of CMOS ADC by linear ramp histogram technique using 6T-SRAM as register in CDU is presented. The on-chip linear ramp histogram technique can be seamlessly combined with ΔIDDQ technique for improved testability, increased fault coverage and reliable operation

Technology and layout-related testing of static random-access memories

Static random-access memories (SRAMs) exhibit faults that are electrical in nature. Functional and electrical testing are performed to diagnose faulty operation. These tests are usually designed from simple fault models that describe the chip interface behavior without a thorough analysis of the chip layout and technology. However, there are certain technology and layout-related defects that are internal to the chip and are mostly time-dependent in nature. The resulting failures may or may not seriously degrade the input/output interface behavior. They may show up as electrical faults (such as a slow access fault) and/or functional faults (such as a pattern sensitive fault). However, these faults cannot be described properly with the functional fault models because these models do not take timing into account. Also, electrical fault models that describe merely the input/output interface behavior are inadequate to characterize every possible defect in the basic SRAM cell. Examples of faults produced by these defects are: (a) static data loss, (b) abnormally high currents drawn from the power supply, etc. Generating tests for such faults often requires a thorough understanding and analysis of the circuit technology and layout. In this article, we shall examine ways to characterize and test such faults. We shall divide such faults into two categories depending on the types of SRAMs they effect—silicon SRAMs and GaAs SRAMs.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43015/1/10836_2004_Article_BF00972519.pd

A Behavioral Model of a Built-in Current Sensor for IDDQ Testing

IDDQ testing is one of the most effective methods for detecting defects in integrated circuits. Higher leakage currents in more advanced semiconductor technologies have reduced the resolution of IDDQ test. One solution is to use built-in current sensors. Several sensor techniques for measuring the current based on the magnetic field or voltage drop across the supply line have been proposed. In this work, we develop a behavioral model for a built-in current sensor measuring voltage drop and use this model to better understand sensor operation, identify the effect of different parameters on sensor resolution, and suggest design modifications to improve future sensor performance