Fully digital-compatible built-in self-test solutions to linearity testing of embedded mixed-signal functions

Mixed-signal circuits, especially analog-to-digital and digital-to-analog converters, are the most widely used circuitry in electronic systems. In the most of the cases, mixed-signal circuits form the interface between the analog and digital worlds and enable the processing and recovering of the real-world information. Performance of mixed-signal circuits, such as linearity and noise, are then critical to any applications. Conventionally, mixed-signal circuits are tested by mixed-signal automatic test equipment (ATE). However, along with the continuous performance improvement, using conventionally methods increases test costs significantly since it takes much more time to test high-performance parts than low-performance ones and mixed-signal ATE testers could be extremely expensive depending on the test precision they provide. Another factor that makes mixed-signal testing more and more challenging is the advance of the integration level. In the popular system-on-chip applications, mixed-signal circuits are deeply embedded in the systems. With less observability and accessibility, conventionally external test methods can not guarantee the precision of the source signals and evaluations. Test performance is then degraded. This work investigates new methods using digital testers incorporated with on-chip, built-in self-test circuits to test the linearity performance of data converters with less test cost and better test performance. Digital testers are cheap to use since they only offer logic signals with direct connections. The analog sourcing and evaluation capabilities have to be absorbed by the on-chip BIST circuits, which, meanwhile, could benefit the test performance with access to the internal circuit nodes. The main challenge of the digital-compatible BIST methods is to implement the BIST circuits with enough high test performance but with low design complexity and cost. High-resolution data converter testing needs much higher-precision analog source signals and evaluation circuits. However, high-precision analog circuits are conventionally hard to design and costly, and their performance is subject to mismatch errors and process variations and cannot be guaranteed without careful testing. On the digital side, BIST circuits usually conduct procedure control and data processing. To make the BIST solution more universal, the control and processing performed by the digital BIST circuits should be simple and not rely on any complex microcontroller and DSP block. Therefore, the major tasks of this dissertation are 1) performance-robust analog BIST circuit design and 2) test procedure development. Analog BIST circuits in this work consist of only low-accuracy analog components, which are usually easy to design and cost effective. The precision is then obtained by applying the so-called deterministic dynamic element matching technique to the low-accuracy analog cells. The test procedure and data processing designed for the BIST system are simple and can be implemented by small logic circuits. In this dissertation, we discuss the proposed BIST solutions to ADC and DAC linearity testing in chapter 3 and chapter 5, respectively. In each case, the structure of the test system, the test procedure, and the theoretical analysis of the test performance are presented. Simulation results are shown to verify the efficacy of the methods. The ADC BIST system is also verified experimentally. In addition, chapter 4 introduces a system-identification based reduced-code testing method for pipeline ADCs. This method is able to reduce test time by more than 95%. And it is compatible with the proposed BIST method discussed in chapter 3

Non-parametric identification of higher order sinusoidal output describing functions

In this paper the concept of the Higher Order Sinusoidal Output Describing Functions (HOSODF) is presented. HOSODF can be defined for the class of causal, stable, time invariant non-linear systems which give a sinusoidal response to a specific harmonic excitation. The HOSODF relate the magnitude and phase of the individual harmonics, which together compose that specific input signal, to the sinusoidal output signal of such a system. HOSODF are the dual of the Higher Order Sinusoidal Input Describing Functions (Nuij 2006). Like the HOSIDF, the HOSODF are the results of an extension of linear techniques towards non-linear systems analysis. Using the HOSODF, the non-linear systems under investigation can be modeled as a cascade of the HOSODF and a Virtual Harmonics Compressor (VHC). The VHC is defined as a non-linear component which transforms a harmonic input signal ¿(t) into a sinusoidal output signal y(t) with frequency ¿, amplitude â and phase f. This input signal ¿(t) consists of an infinite amount of harmonics of the output signal y(t) with frequency n¿, amplitude â and phase nf with n=0,1,…8. Special attention is paid to the non-parametric identification of the HOSODF. The identification requires control of the frequency and amplitude of the sinusoidal output of the system within its domain of possible sinusoidal output signals. This specific state of these non-linear systems can be reached by incorporating the system under test in a feedback loop. In this loop the desired sinusoidal output is defined as the control objective of a dedicated repetitive controller consisting of a memory loop with positive feedback. The design of the learning filter required for stability is also addressed. As a spinoff of the identification technique, the authors see opportunities for advanced non-linear control of shaker systems aimed at sinusoidal excitation of non-linear systems

Non-parametric identification of higher order sinusoidal output describing functions

In this paper the concept of the Higher Order Sinusoidal Output Describing Functions (HOSODF) is presented. HOSODF can be defined for the class of causal, stable, time invariant non-linear systems which give a sinusoidal response to a specific harmonic excitation. The HOSODF relate the magnitude and phase of the individual harmonics, which together compose that specific input signal, to the sinusoidal output signal of such a system. HOSODF are the dual of the Higher Order Sinusoidal Input Describing Functions (Nuij 2006). Like the HOSIDF, the HOSODF are the results of an extension of linear techniques towards non-linear systems analysis. Using the HOSODF, the non-linear systems under investigation can be modeled as a cascade of the HOSODF and a Virtual Harmonics Compressor (VHC). The VHC is defined as a non-linear component which transforms a harmonic input signal ¿(t) into a sinusoidal output signal y(t) with frequency ¿, amplitude â and phase f. This input signal ¿(t) consists of an infinite amount of harmonics of the output signal y(t) with frequency n¿, amplitude â and phase nf with n=0,1,…8. Special attention is paid to the non-parametric identification of the HOSODF. The identification requires control of the frequency and amplitude of the sinusoidal output of the system within its domain of possible sinusoidal output signals. This specific state of these non-linear systems can be reached by incorporating the system under test in a feedback loop. In this loop the desired sinusoidal output is defined as the control objective of a dedicated repetitive controller consisting of a memory loop with positive feedback. The design of the learning filter required for stability is also addressed. As a spinoff of the identification technique, the authors see opportunities for advanced non-linear control of shaker systems aimed at sinusoidal excitation of non-linear systems

Characterizing the Dynamic Response of a Chassis Frame in a Heavy-Duty Dump Vehicle based on an Improved Stochastic System Identification

This paper presents an online method for the assessment of the dynamic performance of the chassis frame in a heavy-duty dump truck based on a novel stochastic subspace identification (SSI) method. It introduces the use of an average correlation signal as the input data to conventional SSI methods in order to reduce the noisy and nonstationary contents in the vibration signals from the frame, allowing accurate modal properties to be attained for realistically assessing the dynamic behaviour of the frame when the vehicle travels on both bumped and unpaved roads under different operating conditions. The modal results show that the modal properties obtained online are significantly different from the offline ones in that the identifiable modes are less because of the integration of different vehicle systems onto the frame. Moreover, the modal shapes between 7Hz and 40Hz clearly indicate the weak section of the structure where earlier fatigues and unsafe operations may occur due to the high relative changes in the modal shapes. In addition, the loaded operations show more modes which cause high deformation on the weak section. These results have verified the performance of the proposed SSI method and provide reliable references for optimizing the construction of the frame

On-Line Instruction-checking in Pipelined Microprocessors

Microprocessors performances have increased by more than five orders of magnitude in the last three decades. As technology scales down, these components become inherently unreliable posing major design and test challenges. This paper proposes an instruction-checking architecture to detect erroneous instruction executions caused by both permanent and transient errors in the internal logic of a microprocessor. Monitoring the correct activation sequence of a set of predefined microprocessor control/status signals allow distinguishing between correctly and not correctly executed instruction

Optimal control of ankle joint moment: Toward unsupported standing in paraplegia

This paper considers part of the problem of how to provide unsupported standing for paraplegics by feedback control. In this work our overall objective is to stabilize the subject by stimulation only of his ankle joints while the other joints are braced, Here, we investigate the problem of ankle joint moment control. The ankle plantarflexion muscles are first identified with pseudorandom binary sequence (PRBS) signals, periodic sinusoidal signals, and twitches. The muscle is modeled in Hammerstein form as a static recruitment nonlinearity followed by a linear transfer function. A linear-quadratic-Gaussian (LQG)-optimal controller design procedure for ankle joint moment was proposed based on the polynomial equation formulation, The approach was verified by experiments in the special Wobbler apparatus with a neurologically intact subject, and these experimental results are reported. The controller structure is formulated in such a way that there are only two scalar design parameters, each of which has a clear physical interpretation. This facilitates fast controller synthesis and tuning in the laboratory environment. Experimental results show the effects of the controller tuning parameters: the control weighting and the observer response time, which determine closed-loop properties. Using these two parameters the tradeoff between disturbance rejection and measurement noise sensitivity can be straightforwardly balanced while maintaining a desired speed of tracking. The experimentally measured reference tracking, disturbance rejection, and noise sensitivity are good and agree with theoretical expectations