Due to progressing process technology, yield of chips is reduced by timing violation caused by delay variation of gates and wires in fabrication. Recently, post-silicon delay tuning, which inserts programmable delay elements (PDEs) into clock trees before the fabrication and adjusts the delays of the PDEs to recover the timing violation after the fabrication, is promising to improve the yield. Although post-silicon delay tuning improves the yield, it increases circuit area and power consumption since the PDEs are inserted. In this paper, a PDE structure is taken into consideration to reduce the circuit area and the power consumption. Moreover, a delay selection algorithm, and a clustering method, in which some PDEs are merged into a PDE and the PDE is inserted for multiple registers, are proposed to reduce the circuit area and the power consumption. In computational experiments, the proposed method reduced the circuit area and the power consumption in comparison with an existing method.

The AuGe-alloy source and drain (S/D) formed on SiO2/Si(100) by the lithography process was investigated for the scaling of the organic field-effect transistors (OFETs) with bottom-contact geometry. The S/D was fabricated by the lift-off process utilizing the resist of OFPR. The OFETs with minimum channel length of 2.4 µm was successfully fabricated by the lift-off process. The fabrication yield of Au S/D was 57%, while it was increased to 93% and 100% in case of the Au-1%Ge and Au-7.4%Ge S/D, respectively. Although the mobility of the OFETs with Au-7.4%Ge S/D was decreased to 1.1×10-3 cm2/(Vs), it was able to be increased to 5.5×10-2 cm2/(Vs) by the surface cleaning utilizing H2SO4/H2O2 mixture solution (SPM) and post metallization annealing (PMA) after lift-off process, which was higher than that of OFET with Au S/D.

When the automated driving cars are in widespread usage, traffic will coexist with prioritized vehicles (e.g., ambulances, fire trucks, police vehicles) and automated driving cars. Automated driving cars are expected to be safer and lower stress than manual driving vehicles because of passengers paying less attention to driving. However, there are many challenges for automated driving cars to get along with surrounding transport participants. In particular, when an ambulance is driving into an intersection with the red traffic signal, the automated driving car is required to deal with a situation differently from normal traffic situations. In order to continue safe driving, it is necessary to recognize the approach of the ambulance at an earlier time. Possible means of recognizing ambulances include siren sound, rotating red lights and vehicle to vehicle communication. Based on actual traffic data, the authors created a mathematical model of deceleration for giving way and consider the status of suitable behavior by automated driving cars. The authors calculate the detection distance required to take suitable action. The results indicate that there are advantages in vehicle to vehicle communication in detecting ambulances by automated driving cars.

Several new memories are being studied as candidates of future DRAM that seems difficult to be scaled. However, the read signal in these new memories needs to be amplified in a single-end manner with reference signal supplied if they are aimed for being applied to the high-density main memory. This scheme, which is fortunately not necessary in DRAM's 1/2Vdd pre-charge sense amp, can become a serious bottleneck in the new memory development, because the device electrical parameters in these new memory cells are prone to large cell-to-cell variations without exception. Furthermore, the extent to which the parameter fluctuates in data “1” is generally not the same as in data “0”. In these situations, a new sensing scheme is proposed that can minimize the sensing error rate for high-density single-end emerging memories like STT-MRAM, ReRAM and PCRAM. The scheme is based on averaging multiple dummy cell pairs that are written “1” and “0” in a weighted manner according to the fluctuation unbalance between “1” and “0”. A detailed analysis shows that this scheme is effective in designing 128Mb 1T1MTJ STT-MRAM with the results that the required TMR ratio of an MTJ can be relaxed from 130% to 90% for the fluctuation of 6% sigma-to-average ratio of MTJ resistance in a 16 pair-dummy cell averaging case by using this technology when compared with the arithmetic averaging method.

An optimal design method for a sub-ranging Analog-to-Digital Converter (ADC) based on stochastic comparator is demonstrated by performing theoretical analysis of random comparator offset voltages. If the Cumulative Distribution Function (CDF) of the comparator offset is defined appropriately, we can calculate the PDFs of the output code and the effective resolution of a stochastic comparator. It is possible to model the analog-to-digital conversion accuracy (defined as yield) of a stochastic comparator by assuming that the correlations among the number of comparator offsets within different analog steps corresponding to the Least Significant Bit (LSB) of the output transfer function are negligible. Comparison with Monte Carlo simulation verifies that the proposed model precisely estimates the yield of the ADC when it is designed for a reasonable target yield of >0.8. By applying this model to a stochastic comparator we reveal that an additional calibration significantly enhances the resolution, i.e., it increases the Number of Bits (NOB) by ∼ 2 bits for the same target yield. Extending the model to a stochastic-comparator-based sub-ranging ADC indicates that the ADC design parameters can be tuned to find the optimal resource distribution between the deterministic coarse stage and the stochastic fine stage.

This paper discusses design challenges and possible solutions for 3D NAND. A 3D NAND array inherently has a larger parasitic capacitance and thereby critical area in terms of product yield. To mitigate such issues associated with 3D NAND technology, array control and divided array architecture for improving reliability and yield and for reducing area overhead, program time, energy per bit and array noise are proposed.

Three-dimensional integrated circuits (3D ICs) that employ through-silicon vias (TSVs) integrating multiple dies vertically have opened up the potential of highly improved circuit designs. However, various types of TSV defects may occur during the assembly process, especially the clustered TSV faults because of the winding level of thinned wafer, the surface roughness and cleanness of silicon dies,inducing TSV yield reduction greatly. To tackle this fault clustering problem, router-based and ring-based TSV redundancy architectures were previously proposed. However, these schemes either require too much area overhead or have limited reparability to tolerant clustered TSV faults. Furthermore, the repairing lengths of these schemes are too long to be ignored, leading to additional delay overhead, which may cause timing violation. In this paper, we propose a region-based TSV redundancy design to achieve relatively high reparability as well as low additional delay overhead. Simulation results show that for a given number of TSVs (8*8) and TSV failure rate (1%), our design achieves 11.27% and 20.79% reduction of delay overhead as compared with router-based design and ring-based scheme, respectively. In addition, the reparability of our proposed scheme is much better than ring-based design by 30.84%, while it is close to that of the router-based scheme. More importantly, the overall TSV yield of our design achieves 99.88%, which is slightly higher than that of both router-based method (99.53%) and ring-based design (99.00%).

As devices continue to shrink, the parameter shift due to process variation and aging effects has an increasing impact on the circuit yield and reliability. However, predicting how long a circuit can maintain its design yield above the design specification is difficult because the design yield changes during the aging process. Moreover, performing Monte Carlo (MC) simulation iteratively during aging analysis is infeasible. Therefore, most existing approaches ignore the continuity during simulations to obtain high speed, which may result in accumulation of extrapolation errors with time. In this paper, an incremental simulation technique is proposed for lifetime yield analysis to improve the simulation speed while maintaining the analysis accuracy. Because aging is often a gradual process, the proposed incremental technique is effective for reducing the simulation time. For yield analysis with degraded performance, this incremental technique also reduces the simulation time because each sample is the same circuit with small parameter changes in the MC analysis. When the proposed dynamic aging sampling technique is employed, 50× speedup can be obtained with almost no decline accuracy, which considerably improves the efficiency of lifetime yield analysis.

An efficient Monte Carlo (MC) method for the calculation of failure probability degradation of an SRAM cell due to negative bias temperature instability (NBTI) is proposed. In the proposed method, a particle filter is utilized to incrementally track temporal performance changes in an SRAM cell. The number of simulations required to obtain stable particle distribution is greatly reduced, by reusing the final distribution of the particles in the last time step as the initial distribution. Combining with the use of a binary classifier, with which an MC sample is quickly judged whether it causes a malfunction of the cell or not, the total number of simulations to capture the temporal change of failure probability is significantly reduced. The proposed method achieves 13.4× speed-up over the state-of-the-art method.

Due to the progress of the process technology in LSI, the yield of LSI chips is reduced by timing violations caused by delay variations. To recover the timing violations, delay tuning methods insert programmable delay elements called PDEs into the clock tree before fabrication and tune their delays after fabrication. The yield improvement of existing methods is not enough. In this paper, a delay tuning method of PDEs with an ordered finite set of delays is proposed for the yield improvement. The proposed delay tuning method is based on the modified Bellman-Ford algorithm. Therefore, its optimality is guaranteed and its time complexity is polynomial. In the experiments under Monte-Carlo simulation, the yield of the proposed method is improved higher when the number of delays in each PDE is increased.

Yield enhancements and quality improvements must be considered as factors of the utmost importance in VLSI (Very Large Scale Integration circuits) manufacturing in order to reduce cost and ensure customer satisfaction. This paper will present a study of the yield theory, an analysis of actual manufacturing data, and the challenges of yield enhancement.

This paper proposes a novel and an efficient method termed hypersphere sampling to estimate the circuit yield of low-failure probability with a large number of variable sources. Importance sampling using a mean-shift Gaussian mixture distribution as an alternative distribution is used for yield estimation. Further, the proposed method is used to determine the shift locations of the Gaussian distributions. This method involves the bisection of cones whose bases are part of the hyperspheres, in order to locate probabilistically important regions of failure; the determination of these regions accelerates the convergence speed of importance sampling. Clustering of the failure samples determines the required number of Gaussian distributions. Successful static random access memory (SRAM) yield estimations of 6- to 24-dimensional problems are presented. The number of Monte Carlo trials has been reduced by 2-5 orders of magnitude as compared to conventional Monte Carlo simulation methods.

NROM is one of the emerging non-volatile-memory technologies, which is promising for replacing current floating-gate-based non-volatile memory such as flash memory. In order to raise the fabrication yield and enhance its reliability, a novel test and repair flow is proposed in this paper. Instead of the conventional fault replacement techniques, a novel fault masking technique is also exploited by considering the logical effects of physical defects when the customer's code is to be programmed. In order to maximize the possibilities of fault masking, a novel data inversion technique is proposed. The corresponding BIST architectures are also presented. According to experimental results, the repair rate and fabrication yield can be improved significantly. Moreover, the incurred hardware overhead is almost negligible.

In this paper, a high performance current latch sense amplifier (CLSA) with vertical MOSFET is proposed, and its performances are investigated. The proposed CLSA with the vertical MOSFET realizes a 11% faster sensing time with about 3% smaller current consumption relative to the conventional CLSA with the planar MOSFET. Moreover, the proposed CLSA with the vertical MOSFET achieves an 1.11 dB increased voltage gain G(f) relative to the conventional CLSA with the planar MOSFET. Furthermore, the proposed CLSA realizes up to about 1.7% larger yield than the conventional CLSA, and its circuit area is 42% smaller than the conventional CLSA.

Yield-driven clock skew scheduling was previously formulated as a minimum cost-to-time ratio cycle problem, by assuming that variational path delays are in Gaussian distributions. However in today's nanometer technology, process variations show growing impacts on this assumption, as variational delays with non-Gaussian distributions have been observed on these paths. In this paper, we propose a novel yield-driven clock skew scheduling method for arbitrary distributions of critical path delays. Firstly, a general problem formulation is proposed. By integrating the cumulative distribution function (CDF) of critical path delays, the formulation is able to handle path delays with any distributions. It also generalizes the previous formulations on yield-driven clock skew scheduling and indicates their statistical interpretations. Generalized Howard algorithm is derived for finding the critical cycles of the underlying timing constraint graphs. Moreover, an effective algorithm based on minimum balancing is proposed for the overall yield improvement. Experimental results on ISCAS89 benchmarks show that, compared with two representative existing methods, our method remarkably improves the yield by 10.25% on average (up to 14.66%).

We investigate a design strategy for subthreshold circuits focusing on energy-consumption minimization and yield maximization under process variations. The design strategy is based on the following findings related to the operation of low-power CMOS circuits: (1) The minimum operation voltage (VDDmin) of a circuit is dominated by flip-flops (FFs), and VDDmin of an FF can be improved by upsizing a few key transistors, (2) VDDmin of an FF is stochastically modeled by a log-normal distribution, (3) VDDmin of a large circuit can be efficiently estimated by using the above model, which eliminates extensive Monte Carlo simulations, and (4) improving VDDmin may substantially contribute to decreasing energy consumption. The effectiveness of the proposed design strategy has been verified through circuit simulations on various circuits, which clearly show the design tradeoff between voltage scaling and transistor sizing.

Device mismatch plays an important role in the design of accurate analog circuits. The common centroid structure is commonly employed to reduce device mismatches caused by symmetrical layouts and processing gradients. Among the candidate placements generated by the common centroid approach, however, whichever achieves better matching is generally difficult to be determined without performing the time-consuming yield evaluation process. In addition, this rule-based methodology makes it difficult to achieve acceptable matching between multiple capacitors and to handle an irregular layout area. Based on a spatial correlation model, this study proposed a design methodology for yield enhancement of analog circuits using switched-capacitor techniques. An efficient and effective placement generator is developed to derive a placement for a circuit to achieve the highest or near highest correlation coefficient and thus accomplishing a better yield performance. A simple yield analysis is also developed to evaluate the achieved yield performance of a derived placement. Results show that the proposed methodology derives a placement which achieves better yield performance than those generated by the common centroid approach.

Parameter variations reveal themselves as different frequency and leakage powers per instances of the same MPSoC. By the increasing variation with technology scaling, worst-case-based scheduling algorithms result in either increasingly less optimal schedules or otherwise more lost yield. To address this problem, this paper introduces a variation-aware task and communication scheduling algorithm for multiprocessor system-on-chip (MPSoC). We consider both delay and leakage power variations during the process of finding the best schedule so that leakier processors are less utilized and can be more frequently put in sleep mode to reduce power. Our algorithm takes advantage of event tables to accelerate the statistical timing and power analysis. We use genetic algorithm to find the best schedule that maximizes power-yield under a performance-yield constraint. Experimental results on real world benchmarks show that our proposed algorithm achieves 16.6% power-yield improvement on average over deterministic worst-case-based scheduling.

An accurate behavioral model of a DAC-embedded opamp (DAC-opamp) is developed for a yield-ensuring LCD column driver design. A lookup table for the V-I curve of the unit differential pair in the DAC-opamp is extracted from a circuit simulation and is later manipulated through a random error insertion. Virtual ground assumption simplifies the output voltage estimation algorithm. The developed behavioral model of a 5-bit DAC-opamp shows good agreement with the circuit level simulation with less than 5% INL difference.

To improve product yield in high-product-mix semiconductor manufacturing, it is important to estimate the systematic yield inherent to each product and to extract problematic products that have low systematic yields. We propose a simplified and available yield model using a critical area analysis. This model enables the extraction of problematic products by the relationship between actual yields and the short sensitivities of the products. Furthermore, we present an enterprise-wide yield management system using this model and some useful applications. As a result, the system increases the efficiency of the yield management and enhancement dramatically.