An ART1 microchip and its use in multi-ART1 systems

Recently, a real-time clustering microchip neural engine based on the ART1 architecture has been reported. Such chip is able to cluster 100-b patterns into up to 18 categories at a speed of 1.8 μs per pattern. However, that chip rendered an extremely high silicon area consumption of 1 cm2, and consequently an extremely low yield of 6%. Redundant circuit techniques can be introduced to improve yield performance at the cost of further increasing chip size. In this paper we present an improved ART1 chip prototype based on a different approach to implement the most area consuming circuit elements of the first prototype: an array of several thousand current sources which have to match within a precision of around 1%. Such achievement was possible after a careful transistor mismatch characterization of the fabrication process (ES2-1.0 μm CMOS). A new prototype chip has been fabricated which can cluster 50-b input patterns into up to ten categories. The chip has 15 times less area, shows a yield performance of 98%, and presents the same precision and speed than the previous prototype. Due to its higher robustness multichip systems are easily assembled. As a demonstration we show results of a two-chip ART1 system, and of an ARTMAP system made of two ART1 chips and an extra interfacing chip

Investigation into yield and reliability enhancement of TSV-based three-dimensional integration circuits

Three dimensional integrated circuits (3D ICs) have been acknowledged as a promising technology to overcome the interconnect delay bottleneck brought by continuous CMOS scaling. Recent research shows that through-silicon-vias (TSVs), which act as vertical links between layers, pose yield and reliability challenges for 3D design. This thesis presents three original contributions.The first contribution presents a grouping-based technique to improve the yield of 3D ICs under manufacturing TSV defects, where regular and redundant TSVs are partitioned into groups. In each group, signals can select good TSVs using rerouting multiplexers avoiding defective TSVs. Grouping ratio (regular to redundant TSVs in one group) has an impact on yield and hardware overhead. Mathematical probabilistic models are presented for yield analysis under the influence of independent and clustering defect distributions. Simulation results using MATLAB show that for a given number of TSVs and TSV failure rate, careful selection of grouping ratio results in achieving 100% yield at minimal hardware cost (number of multiplexers and redundant TSVs) in comparison to a design that does not exploit TSV grouping ratios. The second contribution presents an efficient online fault tolerance technique based on redundant TSVs, to detect TSV manufacturing defects and address thermal-induced reliability issue. The proposed technique accounts for both fault detection and recovery in the presence of three TSV defects: voids, delamination between TSV and landing pad, and TSV short-to-substrate. Simulations using HSPICE and ModelSim are carried out to validate fault detection and recovery. Results show that regular and redundant TSVs can be divided into groups to minimise area overhead without affecting the fault tolerance capability of the technique. Synthesis results using 130-nm design library show that 100% repair capability can be achieved with low area overhead (4% for the best case). The last contribution proposes a technique with joint consideration of temperature mitigation and fault tolerance without introducing additional redundant TSVs. This is achieved by reusing spare TSVs that are frequently deployed for improving yield and reliability in 3D ICs. The proposed technique consists of two steps: TSV determination step, which is for achieving optimal partition between regular and spare TSVs into groups; The second step is TSV placement, where temperature mitigation is targeted while optimizing total wirelength and routing difference. Simulation results show that using the proposed technique, 100% repair capability is achieved across all (five) benchmarks with an average temperature reduction of 75.2? (34.1%) (best case is 99.8? (58.5%)), while increasing wirelength by a small amount

A Novel Method to Improve the Test Efficiency of VLSI Tests

This paper considers reducing the cost of test application by permuting test vectors to improve their defect coverage. Algorithms for test reordering are developed with the goal of minimizing the test cost. Best and worst case bounds are established for the performance of a reordered sequence compared to the original sequence of test application. SEMATECH test data and simulation results are used throughout to illustrate the ideas

Self-Learning Hot Data Prediction: Where Echo State Network Meets NAND Flash Memories

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Well understanding the access behavior of hot data is significant for NAND flash memory due to its crucial impact on the efficiency of garbage collection (GC) and wear leveling (WL), which respectively dominate the performance and life span of SSD. Generally, both GC and WL rely greatly on the recognition accuracy of hot data identification (HDI). However, in this paper, the first time we propose a novel concept of hot data prediction (HDP), where the conventional HDI becomes unnecessary. First, we develop a hybrid optimized echo state network (HOESN), where sufficiently unbiased and continuously shrunk output weights are learnt by a sparse regression based on L2 and L1/2 regularization. Second, quantum-behaved particle swarm optimization (QPSO) is employed to compute reservoir parameters (i.e., global scaling factor, reservoir size, scaling coefficient and sparsity degree) for further improving prediction accuracy and reliability. Third, in the test on a chaotic benchmark (Rossler), the HOESN performs better than those of six recent state-of-the-art methods. Finally, simulation results about six typical metrics tested on five real disk workloads and on-chip experiment outcomes verified from an actual SSD prototype indicate that our HOESN-based HDP can reliably promote the access performance and endurance of NAND flash memories.Peer reviewe

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

A Wafer Bin Map "Relaxed" Clustering Algorithm for Improving Semiconductor Production Yield

AbstractThe semiconductor manufacturing process involves long and complex activities, with intensive use of resources. Producers compete through the introduction of new technologies for increasing yield and reducing costs. So, yield improvement is becoming increasingly important since advanced production technologies are complex and interrelated. In particular, Wafer Bin Maps (WBMs) presenting specific fault models provide crucial information to keep track of process problems in semiconductor manufacturing. Production control is often based on the "judgement" of expert engineers who, however, carry out the analysis of map templates through simple visual exploration. In this way, existing studies are subjective, time consuming, and are also limited by the capacity of human recognition. This study proposes a network-based data mining approach, which integrates correlation graphs with clustering analysis to quickly extract patterns from WBMs and then bind them to manufacturing defects. An empirical study has been conducted on real production data for validating the proposed clustering algorithm, which showed a perfect correspondence between the malfunction patterns found by the algorithm and those discovered by human experts, so confirming the validity of our approach in its ability of correctly identifying actual defective patterns to help improving production yield