The DOI matches its Crossref record.
SOFT LITHOGRAPHY
Abstract
Abstract Soft lithography represents a non-photolithographic strategy based on self-assembly and replica molding for carrying out micro- and nanofabrication. It provides a convenient, effective, and low-cost method for the formation and manufacturing of micro- and nanostructures. In soft lithography, an elastomeric stamp with patterned relief structures on its surface is used to generate patterns and structures with feature sizes ranging from 30 nm to 100 μm. Five techniques have been demonstrated: microcontact printing (μCP), replica molding (REM), microtransfer molding (μTM), micromolding in capillaries (MIMIC), and solvent-assisted micromolding (SAMIM). In this chapter we discuss the procedures for these techniques and their applications in micro- and nanofabrication, surface chemistry, materials science, optics, MEMS, and microelectronics.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Design, fabrication and control of soft robots
Rus, D., & Tolley, M. T. (2015) · Nature · doi:10.1038/nature14543 · Cited by 5,825
Biomaterials: Where We Have Been and Where We Are Going
Ratner, B. D., & Bryant, S. J. (2004) · Annual Review of Biomedical Engineering · doi:10.1146/annurev.bioeng.6.040803.140027 · Cited by 1,507
Soft Actuators for Small-Scale Robotics
Hines, L., Petersen, K., Lum, G. Z., & Sitti, M. (2017) · Advanced Materials · doi:10.1002/adma.201603483 · Cited by 1,337
A guide to the organ-on-a-chip
Leung, C. M., de Haan, P., Ronaldson-Bouchard, K., Kim, G.-A., Ko, J., Rho, H. S., Chen, Z., Habibovic, P., Jeon, N. L., Takayama, S., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00118-6 · Cited by 1,116
Advances in organ-on-a-chip engineering
Zhang, B., Korolj, A., Lai, B. F. L., & Radisic, M. (2018) · Nature Reviews Materials · doi:10.1038/s41578-018-0034-7 · Cited by 1,017
Metadata
- Authors
- Xia, Y., & Whitesides, G. M.
- Venue
- Annual Review of Materials Science
- Publisher
- Annual Reviews
- Published
- 1998-08
- Volume
- 28
- Issue
- 1
- Pages
- 153-184
- ISSN
- 0084-6600 (ISSN-L)
- OpenAlex
- W4247419735
- Cited by
- 4,611
- References
- 0
- FWCI
- 19.54
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: SOFT LITHOGRAPHY. https://app.cochat.ai/papers/10.1146/annurev.matsci.28.1.153
- The paper
- Xia, Y., & Whitesides, G. M. (1998). SOFT LITHOGRAPHY. Annual Review of Materials Science. https://doi.org/10.1146/annurev.matsci.28.1.153
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.