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

IDDQ testing of a CMOS first order sigma-delta modulator of an 8-bit oversampling ADC

This work presents IDDQ testing of a CMOS first order sigma-delta modulator of an 8-bit oversampling analog-to-digital converter using a built-in current sensor [BICS]. Gate-drain, source-drain, gate-source and gate-substrate bridging faults are injected using fault injection transistors. All the four faults cause varying fault currents and are successfully detected by the BICS at a good operation speed. The BICS have a negligible impact on the performance of the modulator and an external pin is provided to completely cut-off the BICS from the modulator. The modulator was designed and fabricated in 1.5 μm n-well CMOS process. The decimator was designed on Altera\u27s FLEXE20K board using Verilog. The modulator and decimator were assembled together to form a sigma-delta ADC

On the deployment of on-chip noise sensors

The relentless technology scaling has led to significantly reduced noise margin and complicated functionalities. As such, design time techniques per se are less likely to ensure power integrity, resulting in runtime voltage emergencies. To alleviate the issue, recently several works have shed light on the possibilities of dynamic noise management systems. Most of these works rely on on-chip noise sensors to accurately capture voltage emergencies. However, they all assume that the placement of the sensors is given. It remains an open problem in the literature how to optimally place a given number of noise sensors for best voltage emergency detection. The problem of noise sensor placement is defined at first along with a novel sensing quality metric (SQM) to be maximized. The threshold voltage for noise sensors to report emergencies serves as a critical tuning knob between the system failure rate and false alarms. The problem of minimizing the system alarm rate subject to a given system failure rate constraint is formulated. It is further shown that with the help of IDDQ measurements during testing which reveal process variation information, it is possible and efficient to compute a per-chip optimal threshold voltage threshold. In the third chapter, a novel framework to predict the resonance frequency using existing on-chip noise sensors, based on the theory of 1-bit compressed sensing is proposed. The proposed framework can help to achieve the resonance frequency of individual chips so as to effectively avoid resonance noise at runtime --Abstract, page iii

[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

Iddq testing of a CMOS 10-bit charge scaling digital-to-analog converter

This work presents an effective built-in current sensor (BICS), which has a very small impact on the performance of the circuit under test (CUT). The proposed BICS works in two-modes the normal mode and the test mode. In the normal mode the BICS is isolated from the CUT due to which there is no performance degradation of the CUT. In the testing mode, our BICS detects the abnormal current caused by permanent manufacturing defects. Further more our BICS can also distinguish the type of defect induced (Gate-source short, source-drain short and drain-gate short). Our BICS requires neither an external voltage source nor current source. Hence the BICS requires less area and is more efficient than the conventional current sensors. The circuit under test is a 10-bit digital to analog converter using charge-scaling architecture

An efficient logic fault diagnosis framework based on effect-cause approach

Fault diagnosis plays an important role in improving the circuit design process and the manufacturing yield. With the increasing number of gates in modern circuits, determining the source of failure in a defective circuit is becoming more and more challenging. In this research, we present an efficient effect-cause diagnosis framework for combinational VLSI circuits. The framework consists of three stages to obtain an accurate and reasonably precise diagnosis. First, an improved critical path tracing algorithm is proposed to identify an initial suspect list by backtracing from faulty primary outputs toward primary inputs. Compared to the traditional critical path tracing approach, our algorithm is faster and exact. Second, a novel probabilistic ranking model is applied to rank the suspects so that the most suspicious one will be ranked at or near the top. Several fast filtering methods are used to prune unrelated suspects. Finally, to refine the diagnosis, fault simulation is performed on the top suspect nets using several common fault models. The difference between the observed faulty behavior and the simulated behavior is used to rank each suspect. Experimental results on ISCAS85 benchmark circuits show that this diagnosis approach is efficient both in terms of memory space and CPU time and the diagnosis results are accurate and reasonably precise

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

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