Architectural level delay and leakage power modelling of manufacturing process variation

PhD ThesisThe effect of manufacturing process variations has become a major issue regarding the estimation of circuit delay and power dissipation, and will gain more importance in the future as device scaling continues in order to satisfy market place demands for circuits with greater performance and functionality per unit area. Statistical modelling and analysis approaches have been widely used to reflect the effects of a variety of variational process parameters on system performance factor which will be described as probability density functions (PDFs). At present most of the investigations into statistical models has been limited to small circuits such as a logic gate. However, the massive size of present day electronic systems precludes the use of design techniques which consider a system to comprise these basic gates, as this level of design is very inefficient and error prone. This thesis proposes a methodology to bring the effects of process variation from transistor level up to architectural level in terms of circuit delay and leakage power dissipation. Using a first order canonical model and statistical analysis approach, a statistical cell library has been built which comprises not only the basic gate cell models, but also more complex functional blocks such as registers, FIFOs, counters, ALUs etc. Furthermore, other sensitive factors to the overall system performance, such as input signal slope, output load capacitance, different signal switching cases and transition types are also taken into account for each cell in the library, which makes it adaptive to an incremental circuit design. The proposed methodology enables an efficient analysis of process variation effects on system performance with significantly reduced computation time compared to the Monte Carlo simulation approach. As a demonstration vehicle for this technique, the delay and leakage power distributions of a 2-stage asynchronous micropipeline circuit has been simulated using this cell library. The experimental results show that the proposed method can predict the delay and leakage power distribution with less than 5% error and at least 50,000 times faster computation time compare to 5000-sample SPICE based Monte Carlo simulation. The methodology presented here for modelling process variability plays a significant role in Design for Manufacturability (DFM) by quantifying the direct impact of process variations on system performance. The advantages of being able to undertake this analysis at a high level of abstraction and thus early in the design cycle are two fold. First, if the predicted effects of process variation render the circuit performance to be outwith specification, design modifications can be readily incorporated to rectify the situation. Second, knowing what the acceptable limits of process variation are to maintain design performance within its specification, informed choices can be made regarding the implementation technology and manufacturer selected to fabricate the design

Characterization of optical interconnects

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 72-75).Interconnect has become a major issue in deep sub-micron technology. Even with copper and low-k dielectrics, parasitic effects of interconnects will eventually impede advances in integrated electronics. One technique that has the potential to provide a paradigm shift is optics. This project evaluates the feasibility of optical interconnects for distributing data and clock signals. In adopting this scheme, variation is introduced by the detector, the waveguides, and the optoelectronic circuit, which includes device, power supply and temperature variations. We attempt to characterize the effects of the aforementioned sources of variation by designing a baseline optoelectronic circuitry and fabricating a test chip which consists of the circuitry and detectors. Simulations are also performed to supplement the effort. The results are compared with the performance of traditional metal interconnects. The feasibility of optical interconnects is found to be sensitive to the optoelectronic circuitry used. Variation effects from the devices and operating conditions have profound impact on the performance of optical interconnects since they introduce substantial skew and delay in the otherwise ideal system.by Shiou Lin Sam.S.M

HIGH PERFORMANCE CLOCK DISTRIBUTION FOR HIGH-SPEED VLSI SYSTEMS

Tohoku University堀口 進課

A test structure for the measurement and characterization of layout-induced transistor variation

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 131-139).Transistor scaling has enabled us to design circuits with higher performance, lower cost, and higher density; billions of transistors can now be integrated onto a single die. However, this trend also magnifies the significance of device variability. In this thesis, we focus on the study of layout-induced systematic variation. Specifically, we investigate how pattern densities can affect transistor behavior. Two pattern densities are chosen in our design: polysilicon density and shallow-trench isolation (STI) density. A test structure is designed to study the systematic spatial dependency between transistors in order to determine the impact of different variation sources on transistor characteristics and understand the radius of influence that defines the neighborhood of shapes which play a part in determining the transistor characteristics. A more accurate transistor model based on surrounding layout details can be built using these results. The test structure is divided into six blocks, each having a different polysilicon density or STI density. A rapid change of pattern density between blocks is designed to emulate a step response for future modeling. The two pattern densities are chosen to reflect the introduction of new process technologies, such as strain engineering and rapid thermal annealing. The test structure is designed to have more than 260K devices under test (DUT). In addition to the changes in pattern density, the impact of transistor sizing, number of polysilicon fingers, finger spacing, and active area are also explored and studied in this thesis. Two different test circuits are designed to perform the measurement.(cont.) The first test circuit is designed to work with of-chip wafer probe testing equipment; the second test circuit is designed to have on-chip current measurement capabilities using a high dynamic range analog-to-digital converter (ADC). The ADC has a dynamic range of over four orders of magnitude to measure currents from 50nA to 1mA. The test chip also implements a hierarchical design with a minimum amount of peripheral circuitry, so that most of the chip area is dedicated for the transistors under test.by Albert Hsu Ting Chang.S.M

A 256-input micro-electrode array with integrated cmos amplifiers for neural signal recording

Thesis (Ph.D.)--Boston UniversityThe nervous system communicates and processes information through its basic structural units -- individual neurons (nerve cells). Neurons convey neural information via electrical and chemical signals, which makes electrophysiological recording techniques very important in the study of neurophysiology. Specifically, active microelectrode arrays (MEAs) with amplifiers integrated on the same substrate are used because they provide a very powerful neural electrical recording technique that can be directly interfaced to acute slices and cell cultures. 2D planer electrodes are typically used for recording from neural cultures in vitro, while in vivo recording in live animals invariably requires the use of 3D electrodes. I have designed an active MEA with neural amplifiers and 3D electrodes, all integrated on a single chip. The electrodes are commercially available 3D C4 (Controlled Collapse Chip Connect) flip-chip bonding solder balls that have a diameter of 100 µm and a pitch of 200 µm. An active MEA neural recording chip -- the Multiple-Input Neural Sensor (MINS) chip -- was designed and fabricated using the IBM BiCMOS 8HP 0.13 µm technology. The MINS IC has 256 input channels that are time-division multiplexed into two output pads. Each channel was designed to work at a 20 kHz frame rate with a total voltage gain of 60 dB per channel with an input-referred noise voltage of 5.3 µVrms over 10 Hz to 10 kHz. The entire MINS chip has an area of 4 x 4 mm^2 with 256 input C4s plus 20 wire-bond pads on two adjacent edges of the chip for power, control, and outputs. The fabricated MINS chips are wire-bonded to standard pin grid array (PGA), open-top PGA, and custom-designed printed circuit board (PCB) packages for electrical, in vitro, and in vivo testing, respectively. After process variation correction, the voltage gain of the 256 neural amplifiers, measured in vitro across several chips, has a mean value of 58.7 dB and a standard deviation of 0.37 dB. Measurements done with the electrical testing package demonstrate that the MINS IC has a flat frequency response from 0.05 Hz to 1.4 MHz, an input-referred noise voltage of 4.6 µVrms over 10 Hz to 10 kHz, an output voltage swing as large as 1.5 V peak-to-peak, and a total power consumption of 11.25 mW, or 43.9 µW per input channel

Characterization and mitigation of process variation in digital circuits and systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 155-166).Process variation threatens to negate a whole generation of scaling in advanced process technologies due to performance and power spreads of greater than 30-50%. Mitigating this impact requires a thorough understanding of the variation sources, magnitudes and spatial components at the device, circuit and architectural levels. This thesis explores the impacts of variation at each of these levels and evaluates techniques to alleviate them in the context of digital circuits and systems. At the device level, we propose isolation and measurement of variation in the intrinsic threshold voltage of a MOSFET using sub-threshold leakage currents. Analysis of the measured data, from a test-chip implemented on a 0. 18[mu]m CMOS process, indicates that variation in MOSFET threshold voltage is a truly random process dependent only on device dimensions. Further decomposition of the observed variation reveals no systematic within-die variation components nor any spatial correlation. A second test-chip capable of characterizing spatial variation in digital circuits is developed and implemented in a 90nm triple-well CMOS process. Measured variation results show that the within-die component of variation is small at high voltages but is an increasing fraction of the total variation as power-supply voltage decreases. Once again, the data shows no evidence of within-die spatial correlation and only weak systematic components. Evaluation of adaptive body-biasing and voltage scaling as variation mitigation techniques proves voltage scaling is more effective in performance modification with reduced impact to idle power compared to body-biasing.(cont.) Finally, the addition of power-supply voltages in a massively parallel multicore processor is explored to reduce the energy required to cope with process variation. An analytic optimization framework is developed and analyzed; using a custom simulation methodology, total energy of a hypothetical 1K-core processor based on the RAW core is reduced by 6-16% with the addition of only a single voltage. Analysis of yield versus required energy demonstrates that a combination of disabling poor-performing cores and additional power-supply voltages results in an optimal trade-off between performance and energy.by Nigel Anthony Drego.Ph.D

Modeling the Impact of Process Variation on Resistive Bridge Defects

Recent research has shown that tests generated without taking process variation into account may lead to loss of test quality. At present there is no efficient device-level modeling technique that models the effect of process variation on resistive bridges. This paper presents a fast and accurate technique to model the effect of process variation on resistive bridge defects. The proposed model is implemented in two stages: firstly, it employs an accurate transistor model (BSIM4) to calculate the critical resistance of a bridge; secondly, the effect of process variation is incorporated in this model by using three transistor parameters: gate length (L), threshold voltage (V) and effective mobility (ueff) where each follow Gaussian distribution. Experiments are conducted on a 65-nm gate library (for illustration purposes), and results show that on average the proposed modeling technique is more than 7 times faster and in the worst case, error in bridge critical resistance is 0.8% when compared with HSPICE

inSense: A Variation and Fault Tolerant Architecture for Nanoscale Devices

Transistor technology scaling has been the driving force in improving the size, speed, and power consumption of digital systems. As devices approach atomic size, however, their reliability and performance are increasingly compromised due to reduced noise margins, difficulties in fabrication, and emergent nano-scale phenomena. Scaled CMOS devices, in particular, suffer from process variations such as random dopant fluctuation (RDF) and line edge roughness (LER), transistor degradation mechanisms such as negative-bias temperature instability (NBTI) and hot-carrier injection (HCI), and increased sensitivity to single event upsets (SEUs). Consequently, future devices may exhibit reduced performance, diminished lifetimes, and poor reliability. This research proposes a variation and fault tolerant architecture, the inSense architecture, as a circuit-level solution to the problems induced by the aforementioned phenomena. The inSense architecture entails augmenting circuits with introspective and sensory capabilities which are able to dynamically detect and compensate for process variations, transistor degradation, and soft errors. This approach creates ``smart\u27\u27 circuits able to function despite the use of unreliable devices and is applicable to current CMOS technology as well as next-generation devices using new materials and structures. Furthermore, this work presents an automated prototype implementation of the inSense architecture targeted to CMOS devices and is evaluated via implementation in ISCAS \u2785 benchmark circuits. The automated prototype implementation is functionally verified and characterized: it is found that error detection capability (with error windows from $\approx$30-400ps) can be added for less than 2\% area overhead for circuits of non-trivial complexity. Single event transient (SET) detection capability (configurable with target set-points) is found to be functional, although it generally tracks the standard DMR implementation with respect to overheads