KEYWORDS: Systems modeling, Fin field effect transistors, Logic, TCAD, Standards development, Signal generators, Resistance, Metals, Materials processing, Field effect transistors
In this paper, we describe a framework to enable the memory array simulations for Materials to Systems CooptimizationTM (MSCOTM) flows. The methodology is applied for projected 3 nm logic FinFET technology node SRAM array. To form the SRAM array, a “tiling” approach is utilized, where neighbor cells are created by copying and mirroring the first cell. Then this process is repeated to create the rest of the array. Electrical pulses are applied to the word-line and bit-line to activate the read and write operations. We demonstrate 128 ×128 SRAM array simulations and find that the farthest cell from the word-line driver is vulnerable.
An efficient Pathfinding DTCO analysis flow which allows rapid block-level power, performance, and area (PPA) characterization is presented. To optimize this flow for the exploration of innovative technology-architecture definitions, i.e. new devices and their integration into functional logic cells, the time consuming task of generating and validating a process design kit (PDK) for each technology definition is eliminated by taking advantage of automated standard cell generation and direct emulation-based parasitic extraction. Further efficiency gains are obtained through a customized flow that allows a large number of place and route (PnR) experiments to be executed automatically. The efficiency of the presented Pathfinding DTCO flow is demonstrated in experiments quantifying block-level PPA changes in different implementations of finFET and CFET devices.
Fueled by higher bandwidth wireless communication and ubiquitous AI, the demand for more affordable and power efficient transistors is accelerating at a time when Dennard scaling is undeniably crawling to a halt. Escalating wafer and design cost are commonly identified as the primary culprits bringing Moore's law to its knees. However, a third component: the cost of making the wrong technology choice early in the development cycle, is equally responsible for slowing the progress of the semiconductor industry. The enormous complexity of leading edge technology nodes has been achieved incrementally over time by limiting each technology node to mostly small evolutionary steps. Forcing too much innovation in one technology node would have catastrophically disrupted the continuous learning curve. As we approach the fundamental device-physics and material-science limits of dimensional scaling, we are forced to look at far more disruptive device and interconnect innovations to achieve meaningful power-performance-area-cost (PPAC) improvement. For example, the complexity versus benefit tradeoffs of innovative 3-dimensional device architectures with non-standard power-distribution networks are so hard to quantify that rigorous yet efficient prototyping becomes indispensable even prior to committing foundry R&D resources. In this paper we present our work on developing a purpose-built suite of tools to vastly accelerate the quantitative pre-screening and optimization of technology options to help the industry maintain its relentless pace of PPAC scaling. We share several examples that demonstrate how we tune a candidate technology definition with this tool-suite. We also describe the important common technologies in successful Design Technology Co-Optimization (DTCO) flows including physical material and process modeling; electrical and circuit simulation; detailed design analysis and modification to reduce weak points; handling enormous datasets; silicon learning feedback loop and intuitive visualization.
While current and next generation lithographic techniques mostly focus on increasing resolution, line edge roughness
(LER) remains one of the primary problems that limit the progress of scaling. In this paper, we examine the impact of
lithographically induced line edge roughness on device performance using 3D TCAD (Technology CAD) simulation.
We propose a methodology to reduce line edge roughness and examine the impact using simulation-based atomistic
analysis of microscopic surface roughness. We show that several alternative wafer processing options - such as
orientation dependent etching, selective epitaxy, and amorphization followed by solid phase epitaxial recrystallization -
significantly reduce the lithography-induced line edge roughness. In particular, this is possible for the {111} silicon
surfaces, due to their abnormally low etching and epitaxy rates compared to the other crystal orientations. For FinFETs
and memory devices, this corresponds to non-standard (110) wafers with structures aligned across the <111> crystal
direction. A detailed example is given on how the crystal self-assembly suppresses line edge roughness and cuts the
average surface slope by a factor of four during a ten minute selective epitaxy process. The remaining surface roughness
is limited to a few atomic steps and enables transistor scaling to the end of the roadmap.
Rigorous 3D process and device simulation has been applied to transistors with curved channel shapes that are inevitable
due to the optical proximity effects. The impact of channel curvature on the transistor performance has been
benchmarked using the universal Ion/Ioff coordinates. Systematic study of the different non-rectangular channel shapes
included straight lines at an angle different than 90 degrees and concave and convex shapes with different curvature
radii. The study reveals that any deviation from the ideal rectangular shape affects transistor performance. The amount
of enhancement or degradation depends on particular shape, with on current, threshold voltage, and off current
responding very differently to the same shape variation. The type and amount of performance variation is very different
for the distorted channel length (i.e. poly gate shape) vs distorted channel width (i.e. active layer shape). Degradation of
over 50% in the on current at a fixed off current has been observed in the most unfavorable cases for each of the two
critical mask layers. On the other hand, a desirable over 3x off current reduction at a fixed on current can be achieved by
selecting a beneficial channel shape.
Proximity Correction is the technology for which the most of IC manufacturers are committed already. The final intended result of correction is affected by many factors other than the optical characteristics of the mask-stepper system, such as photoresist exposure, post-exposure bake and development parameters, etch selectivity and anisotropy, and underlying topography. The most advanced industry and research groups already reported immediate need to consider wafer topography as one of the major components during a Proximity Correction procedure. In the present work we are discussing the corners rounding effect (which eventually cause electrical leakage) observed for the elements of Poly2 layer for a Flash Memory Design. It was found that the rounding originated by three- dimensional effects due to variation of photoresist thickness resulting from the non-planar substrate. Our major goal was to understand the reasons and correct corner rounding. As a result of this work highly effective layout correction methodology was demonstrated and manufacturable Depth Of Focus was achieved. Another purpose of the work was to demonstrate complete integration flow for a Flash Memory Design based on photolithography; deposition/etch; ion implantation/oxidation/diffusion; and device simulators.
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