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Solicitation

Semiconductor Process Residual Gas Analyzer

COMMERCE, DEPARTMENT OF · NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY · DEPT OF COMMERCE NIST

NAICS 334516PSC 6640
Response deadlineSep 14, 2026
PostedAug 31, 2026
Archive dateSep 29, 2026
Solicitation1333ND26QNB640573
Notice IDc2d8306f9f524c509e8d9f56ca1c9f02
Notice typeSolicitation
Set-aside
Place of performanceGaithersburg, MD, 20899
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5 citations · 1 source

SOLICITATION 1333ND26QNB640573 Semiconductor Process Residual Gas Analyzer This is a solicitation for commercial items prepared in accordance with the format in RFO Subpart 12.2, as supplemented with additional information included in this notice.

Extracted from the official notice and archived solicitation files. Confirm controlling requirements in the source documents.

Official notice text

SOLICITATION 1333ND26QNB640573 Semiconductor Process Residual Gas Analyzer This is a solicitation for commercial items prepared in accordance with the format in RFO Subpart 12.2, as supplemented with additional information included in this notice. This announcement constitutes the only solicitation; quotations are being requested, and a separate written document will not be issued. This solicitation is a Request for Quotation (RFQ). The associated North American Industrial Classification System (NAICS) code for this procurement is 334516 - Analytical Laboratory Instrument Manufacturing with a Small Business size standard of 1,000 Employees. This requirement will be a Full and Open Competition. The Chemical Process and Nuclear Measurements Group is seeking a semiconductor-grade Process Residual Gas Analyzer (RGA) for use on flow systems, including an ALD reactor, as part of a CHIPS R&D Metrology program. This program focuses on developing digital twin technology for semiconductor manufacturing equipment. The flow systems, equipped with in situ and inline sensors, are used to acquire process data on thin film growth characterization. The RGA will serve as an online process diagnostic. The RGA must be compatible with low-volatility organometallic and metal halide precursors and process gases. The data and models generated by this project will lead to improvements in process optimization and control, ultimately resulting in cost savings for the US semiconductor industry.

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