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

Product assurance technology for custom LSI/VLSI electronics

The technology for obtaining custom integrated circuits from CMOS-bulk silicon foundries using a universal set of layout rules is presented. The technical efforts were guided by the requirement to develop a 3 micron CMOS test chip for the Combined Release and Radiation Effects Satellite (CRRES). This chip contains both analog and digital circuits. The development employed all the elements required to obtain custom circuits from silicon foundries, including circuit design, foundry interfacing, circuit test, and circuit qualification

Early analysis of VLSI systems with packaging considerations

There is an explosive growth in the size of the VLSI (Very Large Scale Integration) systems today. Microelectronic system designers are packing millions of transistors in a single IC chip. Packaging techniques like Multi-chip module (MCM) and flip-chip bonding offer faster interconnects and IC\u27s capable of accommodating a larger number of inputs and outputs. The complexity of today\u27s designs and the availability of advanced packaging techniques call for an early analysis of the system based on estimation of system parameters to select from a wide choice of circuit partitioning, architecture alternatives and packaging options which give the best cost/performance. A procedure for the early analysis of VLSI systems under packaging considerations has been developed and implemented in this dissertation work. The early analysis tool was used to evaluate the inter-relationship between partitioning and packaging and to determine the best system design considering cost, size and delays. The functional unit level description of a 750,000-transistor MicroSparc processor was studied using an exhaustive search technique. The early analysis performed on the MicroSparc design suggested that the three chip multi-chip design using flip-chip IC\u27s interconnected on a MCM-D substrate is the most cost effective. An early bond pitch analysis performed using the tool concluded that a 250-micron bond pitch is the best choice for the multi-chip MicroSparc designs. The tool was also used to perform an early cache analysis which showed that the use of separate memory and logic processes made it feasible to design the MicroSparc design with larger cache sizes than the use of a combined logic and memory process. The designs based on the separate processes gave equivalent or better performance than the design candidates with smaller cache sizes. Future extensions of the procedure are also outlined here

Technology, design, simulation, and evaluation for SEP-hardened circuits

This paper describes the technology, design, simulation, and evaluation for improvement of the Single Event Phenomena (SEP) hardness of gate-array and SRAM cells. Through the use of design and processing techniques, it is possible to achieve an SEP error rate less than 1.0 x 10(exp -10) errors/bit-day for a 9O percent worst-case geosynchronous orbit environment

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