Targeting applications that require resolution of around 25nm on substrates to 200mm, automated equipment is
described that performs the molecular transfer lithography process in which water-soluble templates of polyvinyl
alcohol, replicated from master topography on silicon, are coated with resist and bonded onto substrates. Moreover, to
eliminate the need for handling wet resist, dry bondable epoxy-based resist is demonstrated, which is pre-coated on the
templates and shipped to the fabrication facility where the automation equipment is housed thereby improving ease-of-use,
efficiency and throughput, while lowering costs.
The paper describes the use of water-dissolvable masks, formed from a polyvinyl alcohol film forming solution, for high-resolution pattern definition and materials-transfer printing. The approach replicates surface patterns as water-soluble polymer masks (templates) by spin-casting the film-forming solution onto a master pattern. The water-soluble mask is coupled to a substrate by polymer adhesion to form a solid two-layer structure. Water is used to dissolve the mask layer to uncover the formed pattern in the adhesive layer, thereby providing a new release mechanism for contact-based methods of pattern formation. Moreover, the patterned polymer adhesion transfer process enables a large-area, conformable, single-use template addressed towards meeting registration and defect control challenges in contact printing. The process further incorporates the capability to replicate with loaded nanostructured materials to form a composite of nanoparticles in a soluble polymeric matrix with a patterned surface. The embedded particles are accessible at the surface of the template and thereby are concurrently transferred to the substrate through the polymer adhesion process and subsequently released from the soluble template after water-dissolution in a structured manner. The paper also describes applications of PVA in forming polymer masks as (a) suspended thin-film templates, (b) imprinting templates for repeated use, and (c) as templates for nanoparticle formation by collimated deposition. Polyvinyl alcohol thus provides an additional material for consideration as a mask (template) for nanofabrication, and would be an alternative to quartz, silicon, and polydimethylsiloxane (PDMS) in that regard. The class of printing techniques using PVA as a mask material is referred to as molecular transfer lithography (MxL).
A new class of printing strategies is described for the manufacture of microstructures and nanostructures. This class collectively is referred to as molecular transfer lithography (MxL). The approach is based on the room-temperature fabrication of water-soluble polymer templates by spin casting a polyvinyl alcohol film-forming solution to replicate surface patterns. The templates are useful not only for pattern formation, but also for materials transfer printing, employing a low-cost, convenient, biocompatible chemical approach to high-resolution processing. Results are provided to demonstrate deep submicrometer feature sizes of holes, pillars and lines, 3-D patterns, materials transfer printing of metallic thin films, planarization of wafer topography, and water-soluble polymer templates for 100- and 200-mm wafer patterning. The alignment tooling is discussed and it is shown that MxL can be adapted for use on standard contact aligners with a replacement of the quartz photomask with a water-soluble polymer template to improve resolution without a change of equipment. A high-throughput alignment system for MxL is also discussed. The MxL class of pattern formation and materials transfer printing strategies is differentiated with respect to imprint lithography and soft lithography methods.
A novel chemical procedure is described that presents, for the first time, a dissolvable template (as opposed to a hard or soft template) for printing. The method, called Molecular Transfer Lithography (MxL), is a pseudo-maskless approach to high-throughput fabrication of devices over large areas with feature sizes that can extend below 100 nm. The MxL procedure utilizes a chemical process that replicates the surface topography of a master pattern into soluble polymeric templates. The template is dissolvable in water but not most organic materials. This property of differential solubility is useful for transferring topography or functional materials onto substrate surfaces. The template is generally chemically dissolved at the conclusion of the pattern formation process. The paper presents a variety of printing strategies and results including lines at 53 nm, three-dimensional patterns, 250 nm contact hole levels, diffraction and optical structures and 200 mm wafer printing. The technique is also useful for planarization. Multi-level printing is achieved by integration of the MxL process and dissolvable templates with standard contact aligners.
As the line-width goes sub-100 nm, process windows for al lithographic processes become smaller. Process control and monitoring of the process parameters become increasingly important and necessary as small variation in process variables such as exposure dose, temperature, resist thickness, developer concentration, etc. may cause the final critical dimension to differ from the specification.
KEYWORDS: Temperature metrology, Semiconducting wafers, Photomasks, Photoresist materials, Chemical elements, Photoresist processing, Control systems, Feedback control, Process control, Silicon
Preliminary performance data is presented for a new thermal processing module. The system is directed towards conducting the temperature sensitive baking and chilling steps for chemically amplified photoresists. The module is comprised of 49 individual heating zones. The zones can be controlled independently with separate temperature sensing, actuation and feedback control mechanisms. A supervisory control strategy is applied to coordinate the individual zones. An in-situ chill plate is used to enable a temperature controlled cool-down phase without the need for substrate movement. Results are presented to demonstrate temperature control over the plate to within plus or minus 0.02 degrees Celsius. Wafer temperature is controlled to within plus or minus 0.05 degrees Celsius as measured at 5 sites. Photomask processing results are presented depicting steady-state control to within plus or minus 0.05 degrees Celsius as measured at 16 sites within one quadrant of the substrate. The advantages of the system are discussed including better temperature uniformity than conventional systems and the ability to conduct multiple experiments in a single run by biasing the setpoint across the substrate.
KEYWORDS: Semiconducting wafers, Temperature metrology, Data modeling, Control systems, Computer programming, Feedback control, Photomasks, Photoresist processing, Temperature sensors, Thermal modeling
An optimal control scheme is designed to improve repeatability by minimizing the loading effects induced by the common processing condition of placement of a semiconductor wafer/photomask at ambient temperature on a large thermal-mass bake plate at processing temperature. The optimal control strategy is a model-based method using linear programming to minimize the worst-case deviation from a nominal temperature set-point during the load disturbance condition. This results in a predictive controller that performs a pre-determined heating sequence prior to the arrival of the wafer as part of the resulting feedforward/feedback strategy to eliminate the load disturbance. This procedure is based on an empirical model generated from data obtained during closed-loop operation. It is easy to design and implement for conventional thermal processing equipment. Experimental results are performed for a commercial conventional bake plate and depict an order-of-magnitude improvement in the settling time and the integral-square temperature error between the optimal predictive controller and a feedback controller for a typical load disturbance.
Novel edge detection and line-fitting machine vision algorithms are applied for linewidth measurement on optical images of integrated circuits. The techniques are used to achieve subpixel resolution. The strategy employs a two-step procedure. In the first step, a neural network is used for edge detection ofthe image. Three neural network approaches are investigated: self-organizing, bootstrap linear threshold, and constrained maximization strategies. The weights of the neural networks are estimated using unsupervised learning procedures, the advantage of which is the ability to adapt to the imaging environment. Consequently, these proposed neural network approaches for edge detection do not require an a priori data base of images with known linewidths for calibration. In the second step, line-fitting methods are applied to the edge maps defined by the neural network to compute linewidth. Two methods are investigated: the Hough transform method and an eigenvector strategy. By employing this two-step strategy, the entire image is used to estimate linewidth as opposed to the use of just a single or a few line scans. Thus, edge roughness effects can be spatially averaged to obtain an optimal estimate of linewidth, and subpixel resolution can be achieved. However, the accuracy (or variance) of this estimate will, of course, be dependent on issues such as pixel size and the capability of the imaging system. The techniques are general and can be used on images from a variety of microscopes, including optical and electron-beam microscopes.
Novel edge detection and line fitting pattern recognition algorithms are applied for linewidth measurement on images of integrated circuits. The strategy employs a two step procedure. In the first step, a neural network is used for edge detection of the image. Three neural network approaches are investigated: bootstrap linear threshold, self-organizing, and constrained maximization strategies. These neural networks combine filtering and thresholding to reduce noise and aberrations in the image. Further, the parameters of the neural network are estimated using an unsupervised learning procedure. The advantage of this learning strategy is the ability to adapt to the imaging environment. Consequently, these proposed neural network approaches for edge detection do not require an a priori database of images with known linewidths for calibration. In the second step, new line-fitting methods are applied to the edge maps defined by the neural network to compute linewidth. Two methods are investigated: an eigenvector strategy and a technique that is based on a reformulation of the line-fitting problem such that advanced signal processing techniques can be employed. The latter algorithm is capable of fitting multiple lines in an image which need not be parallel and possesses computational speed superiority over conventional techniques and can be implemented on-line. By employing this two-step strategy, the entire image is used to estimate linewidth as opposed to a single or few line scans. Thus, edge roughness effects can be spatially averaged to obtain an optimal estimate of linewidth. The techniques are general and can be used on images form a variety of microscopes including optical and electron-beam. The pattern recognition algorithms are applied to images of patterned wafers with lines smaller than 1 micrometers wide. These images are obtained by optical microscopes. The estimated linewidths are shown to be in close agreement with those measured by scanning electron microscopes. The application of the proposed pattern recognition techniques to solve other problems in IC metrology, such as rotational wafer alignment, is also discussed.
Phase-shifting masks are expected to improve the resolution of photolithography without renovation of exposure systems. However, the problem of finding an effective method for designing phase-shifting masks for arbitrary IC patterns has been open for several years. We propose here a computational strategy to solve this problem. The computational complexity of the proposed technique is 0(N3), where N is the total number of pixels on the image plane. Simulation results show that with optimally designed phase-shifting masks, 0.45?/NA contact hole, 0.45?/NA single space, and 0.30?/NA periodic lines/spaces may be printed.
Charles Schaper, Young Cho, Poogyeon Park, Stephen Norman, Paul Gyugyi, G. Hoffmann, S. Balemi, Stephen Boyd, Gregory Franklin, Thomas Kailath, Krishna Saraswat
KEYWORDS: Semiconducting wafers, Lamps, Temperature metrology, Sensors, Systems modeling, Thermal modeling, Data modeling, Control systems, Feedback control, Signal processing
A first-principles low-order model of rapid thermal processing ofsemiconductor wafers is derived.
The nonlinear model describes the steady-state and transient thermal behavior of a wafer with
approximate spatial temperature uniformity undergoing rapid heating and cooling in a multilamp
RTP chamber. The model is verified experimentally for a range of operating temperatures from
400°C to 900°C and pressure of 1 torr in an inert N2 environment. Advantages of the low-order
model over detailed models include ease of identification and implementation for real-time predictive
applications in signal processing and temperature control. This physics-based model is used in the
design of an advanced real-time multivariable control strategy. The strategy employed a feedforward
mechanism to predict temperature transients and a feedback mechanism to correct for errors in the
prediction. The controller is applied to achieve a ramp from 20°C to 900°C at a rate of 45°C/second
in a one atmosphere environment with less than 15°C nonuniformity during the ramp and less than
1°C average nonuniformity during the hold as measured by three thermocouples.
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