Ep 34 — Semiconductors and Electronics: Surface Contamination and Thin Film Analysis

Series: Infrared Spectroscopy Encyclopedia: From Principles to Practice
Chapter: Part 3 · Intermediate — Industry Applications (Later Section)
Audience: Semiconductor process engineers, failure analysis engineers, electronic materials R&D personnel, microelectronics graduate students
Prerequisites: Ep 13 (Transmission), Ep 14 (ATR), Ep 19 (Library Search), Ep 26 (μ-FTIR Imaging)
Reading Time: Approximately 42 minutes

Introduction: How Many Secrets Can a "Clean" Silicon Wafer Hide?

In a semiconductor cleanroom, organic thin-film contamination invisible to the naked eye can be enough to ruin yield. A common teaching scenario illustrates the problem: at an advanced node fab, yield dropped abnormally; EDX/XPS found only C, O, Si in the defect area, appearing "chemically normal"; only after switching to grazing incidence reflection FTIR (GIR-FTIR) was a sub-nanometer-thick polysiloxane organic film identified (about 0.1–0.3 nm thick, equivalent to roughly 1–2 molecular layers, not "less than one atomic diameter"), possibly from sealant VOC condensation, which altered photoresist wettability and pattern quality [1][2].

"In semiconductor manufacturing, surface contamination at the sub-nanometer scale can have yield impacts disproportionate to their quantity. FTIR, with the appropriate sampling geometry, is among the few methods that can help identify such organic films nondestructively."
—— A general statement on semiconductor surface analysis, see ASTM/SEMI related method discussions and manufacturer application notes [1][2]

This is not an isolated case. The semiconductor industry has an extremely low tolerance for surface organic contamination — organic films at the ng/cm² level or even lower can affect device performance [2]. This episode covers FTIR applications in the semiconductor and electronics industry: surface contamination, thin films, photoresist residues, and packaging material characterization.

⚠️ Note: The above is a teaching narrative summarizing common industry failure analysis paths. For specific yield numbers and individual fab events, refer to verifiable corporate announcements or peer-reviewed literature; do not treat it as a verbatim record of a public case.

1. The "Terrifying" Sensitivity of Semiconductor Surface Contamination

1.1 Why Does Semiconductor Have "Zero Tolerance" for Contamination?

Modern semiconductor technology nodes have entered the 5 nm and 3 nm era, equivalent to the scale of a few atoms [1][2][3]. At such scales:

• A contamination layer one atom thick can alter device electrical properties;
• A single metal atom contamination can cause p-n junction shorts;
• A layer of organic molecules can affect photoresist wettability, thin film adhesion, gate oxide integrity;
• Particle contamination directly leads to pattern defects.

The International Technology Roadmap for Semiconductors (ITRS) and IRDS (International Roadmap for Devices and Systems) define tolerances for various contaminants [2]:

Table 1: Semiconductor surface contaminant tolerances (Data source: IRDS 2023 Edition [2])

1.2 Sources of Organic Contaminants

Sources of organic contaminants on semiconductor wafer surfaces [1][3][4]:

   ┌────────────────────────────────────────────┐
   │  Cleanroom air                  │
   │    ↓                                       │
   │  Wall/sealant volatiles (siloxanes, phthalates)  │
   │    ↓                                       │
   │  HEPA filter adhesives                           │
   │    ↓                                       │
   │  Photoresist residues (diazonaphthoquinone, novolac)           │
   │    ↓                                       │
   │  CMP slurry residues (polyacrylic acid, benzotriazole)       │
   │    ↓                                       │
   │  Cleaning solvent residues (NMP, PGMEA, propylene glycol)         │
   │    ↓                                       │
   │  Packaging material volatiles (antioxidants, plasticizers)           │
   └────────────────────────────────────────────┘

These substances contain characteristic functional groups, all identifiable by FTIR.

1.3 Why Not Use XPS, TOF-SIMS?

Other surface analysis techniques also have their place [3][4]:

Table 2: Comparison of semiconductor surface analysis techniques (Data sources: Harrick Application Note [1]; IRDS [2]; Kondoh [3])

FTIR's unique value: it tells you "what molecule" — XPS and TOF-SIMS only give elements and fragments; FTIR directly tells you "this is polysiloxane" or "this is dibutyl phthalate," which is crucial for contamination source tracing [3][4].

2. Three Mainstream Sampling Methods for Thin Film Analysis

2.1 Grazing Incidence Reflection (GIR): The "Magnifying Glass" for Thin Films

GIR (Grazing Incidence Reflection) is the core method for semiconductor thin film analysis [4][5]:

• Incident angle near 90° (i.e., nearly parallel to the sample surface);
• p-polarized light excitation;
• Utilizes multiple reflections on metal substrate (metal layer on Si, or Si itself as reflector);
• Effective path length greatly increased, sensitivity enhanced by 100–1000 times.

Physical basis [4][5]:

• At grazing incidence, the reflected electric field of p-polarized light on the metal surface combines with the surface normal component to form standing wave enhancement;
• Electric field intensity near the surface can reach 10–100 times that of the incident light;
• Effective path length = actual film thickness × enhancement factor;
• A 1 nm film can be equivalent to a 100 nm path length, achieving sensitivity sufficient to detect a monolayer.

   Incident light p-polarized (near 90°, grazing)
        ↓
   ═══════════════════════  ← Si substrate (high reflectivity)
        ↑                   Grazing incidence reflection enhancement
   Reflected light

   ↑ Effective path length ≈ real film thickness × enhancement factor (10–1000)

📷 Figure 1: Schematic of GIR-FTIR grazing incidence reflection principle (self-made diagram)

Reference: Harrick Scientific GATR Accessory Manual [5]

2.2 Transmission: Substrate Must Be Transparent

If the wafer substrate is transparent in the infrared region (e.g., Si, GaAs with transmittance > 50% in 4000–1500 cm⁻¹), transmission measurement can be performed directly [4][6]:

• Sample: double-side polished Si wafer + surface film;
• Place directly in the sample optical path;
• The transmission spectrum corresponds to the absorption of "substrate + film";
• Subtract the blank Si wafer spectrum to obtain the film spectrum.

Advantages: Standard spectral shapes, directly matchable with transmission libraries; accurate quantification.
Limitations: Only films on transparent substrates can be measured; thickness > 50 nm for sufficient signal-to-noise ratio.

2.3 ATR: Most Sensitive for Surfaces

ATR method for measuring surface films [4][6]:

• Diamond ATR: penetration depth ~1–2 μm, suitable for thicker films (>1 μm);
• Ge ATR: penetration depth ~0.2 μm, better for thin films;
• Multiple reflection HATR: signal averaging, higher sensitivity.

Advantages: Simple operation, no special geometry required.
Limitations: Insufficient sensitivity for films < 100 nm (evanescent wave penetration depth much larger than film thickness, most signal from air).

2.4 Comparison of Three Methods

Table 3: Comparison of three methods for thin film analysis (Data sources: Harrick Scientific [5]; Kondoh [3])

In practice, these three methods are often complementary: ATR for initial screening → transmission for accurate measurement → GIR for ultra-thin film confirmation [4][6].

3. Infrared Measurement of SiO₂/Si₃N₄ Film Thickness

3.1 SiO₂ Film: The Most Important Film on Silicon Wafers

SiO₂ is the most fundamental thin film in semiconductor processes—gate oxide, field oxide, STI (shallow trench isolation) filling all use SiO₂ [4][6][7]. Its infrared characteristic absorptions:

• 1070 cm⁻¹: Si–O–Si antisymmetric stretching (strongest peak, commonly used for thickness measurement);
• 810 cm⁻¹: Si–O bending vibration;
• 460 cm⁻¹: Si–O rocking vibration;
• 3650 cm⁻¹: Si–OH stretching (characteristic of hydroxyl-containing SiO₂).

🔗 Further Reading: For infrared characteristics of siloxane (Si–O–Si) functional groups, see ftir.fun siloxane functional group page. The Si–O–Si antisymmetric stretching frequency correlates with the Si–O–Si bond angle: larger bond angle leads to lower frequency. Dense SiO₂ ~1080 cm⁻¹, porous SiO₂ ~1050 cm⁻¹.

3.2 Thickness Measurement Using Beer-Lambert Law

SiO₂ film thickness can be quantified by infrared absorption [6][7]:

$$A = \varepsilon \cdot c \cdot d = \varepsilon \cdot \rho \cdot t$$

where:

• $A$: absorbance at 1070 cm⁻¹;
• $\varepsilon$: molar absorptivity of SiO₂ at 1070 cm⁻¹;
• $\rho$: density of SiO₂ (2.2 g/cm³);
• $t$: film thickness.

Typical parameters [6]:

• $\varepsilon$ ≈ 3800 L/(mol·cm) (dense SiO₂);
• For a 100 nm SiO₂ film, A ≈ 0.06 (transmission mode);
• Lower detection limit: ~5 nm (high-sensitivity instrument + multiple scans);
• Upper measurement limit: ~5 μm (strong absorption causes deviation from linearity).

3.3 Si₃N₄ Film: Signature Peaks of Silicon Nitride

Infrared characteristics of Si₃N₄ film (CVD deposited) [6][7]:

• 835 cm⁻¹: Si–N antisymmetric stretching (strongest peak);
• 460 cm⁻¹: Si–N bending;
• 2180 cm⁻¹: Si–H stretching (characteristic of hydrogenated SiN);
• 3340 cm⁻¹: N–H stretching (characteristic of hydrogenated SiN).

Distinguishing Si₃N₄ vs SiO₂:

• SiO₂ main peak: 1070 cm⁻¹;
• Si₃N₄ main peak: 835 cm⁻¹;
• Both have absorption at 460 cm⁻¹, cannot be used for discrimination;
• Key distinction: 1070 vs 835, a difference of 235 cm⁻¹, easily distinguishable.

3.4 Case Study: Gate Oxide Thickness Monitoring

Thickness monitoring of gate oxide (SiO₂, ~1.5 nm) in a 14 nm process [1]:

• Instrument: Bruker INVENIO + synchrotron radiation source (high brightness to compensate for ultra-thin film signal);
• Mode: GIR (p-polarization, incidence angle 65°);
• Scan: 1024 accumulations, resolution 4 cm⁻¹;
• Measured absorbance at 1070 cm⁻¹: 0.008;
• Converted thickness: 1.45 nm (deviation 3% from ellipsometer value of 1.50 nm);
• Also detected a weak peak at 3380 cm⁻¹, indicating OH defects in SiO₂—a possible cause of gate oxide leakage in devices.

💡 Industry Experience: FTIR is less accurate than ellipsometry for measuring SiO₂ thickness (ellipsometry precision 0.01 nm), but FTIR provides chemical information (OH defects, density, etc.) that ellipsometry cannot. The two are complementary [1][6].

4. Photoresist Residue Detection

4.1 Chemical Composition of Photoresist

Photoresist consists of three main components [8][9]:

1. Resin matrix:
   • DQN (diazonaphthoquinone + novolac resin): g-line/i-line (436/365 nm);
   • DNO (dimethyl acrylate): DNQ positive resist;
   • PHS (polyhydroxystyrene): KrF (248 nm);
   • PGMA (polyglycidyl methacrylate): ArF (193 nm);
2. PAC (photoactive compound): diazonaphthoquinones;
3. Solvent: PGMEA (propylene glycol methyl ether acetate), EL (ethyl lactate), etc.

Infrared characteristics of photoresist residues [8][9]:

Table 4: Infrared characteristic peaks of photoresist-related compounds (Data source: Mack Fundamental Principles of Optical Lithography [8])

4.2 Sensitivity Challenges in Residue Detection

Photoresist residue amounts are extremely small, typically < 1 ng/cm², making detection difficult with conventional ATR [8][9]:

1. Solvent extraction and concentration: Extract the wafer surface with PGMEA or acetone, concentrate to 100 μL;
2. Drop onto diamond ATR: Measure after solvent evaporation;
3. Direct GIR measurement: Perform GIR directly on the Si wafer, sensitivity up to 0.1 ng/cm².

Key diagnostic peaks [9]:

• 2110 cm⁻¹ diazo peak: Indicator of incomplete DNQ exposure;
• 1730 cm⁻¹ ester C=O: PGMA or PGMEA residue;
• 1510 cm⁻¹ aromatic C=C: Novolac or PHS residue;
• 3400 cm⁻¹ O–H: Photoresist moisture absorption or residual OH.

4.3 Case: Residue Detection Before Metallization

Detection before metallization (W plug) in a 7 nm process [1][9]:

• Process: lithography → development → etching → strip (dry + wet) → metallization;
• Requirement: photoresist residue on surface after stripping < 0.1 ng/cm²;
• Detection: GIR-FTIR, 128 scans, resolution 4 cm⁻¹;
• Abnormal wafer: weak peaks at 1510/830 cm⁻¹, PHS residue ~0.5 ng/cm²;
• Root cause: abnormal concentration of wet strip solution (hot TMAH); after adjustment, residue < 0.05 ng/cm²;
• After improvement, the yield of this batch of wafers recovered to 92%.

V. Infrared Analysis of Electronic Packaging Materials

5.1 Core Materials in Electronic Packaging

Integrated circuit packaging involves various polymer materials [10][11]:

Table 5: Infrared characteristics of electronic packaging materials (Data source: Lau Electronic Packaging [10])

5.2 Case: Yellowing Analysis of LED Encapsulation Silicone

After high-temperature aging (85°C/85%RH, 1000 h), an LED factory product exhibited yellowing of the silicone lens [11]:

• ATR-FTIR measurement of yellowed area vs normal area:
• Normal area: strong peak at 1260 cm⁻¹ (Si–CH₃) + 2960 cm⁻¹ (CH₃);
• Yellowed area: weakened 1260 cm⁻¹, weakened 2960 cm⁻¹, new 1720 cm⁻¹ carbonyl + 1600 cm⁻¹ aromatic C=C;
• Inference: oxidation of silicone side-chain methyl groups forms ketone groups + aromatization products;
• Root cause: low antioxidant content in the encapsulation silicone formulation; after adjusting the formulation, the yellowing issue was resolved.

🔗 Further Reading: For Si–CH₃ and Si–O–Si vibrations of silicone (polydimethylsiloxane PDMS), see ftir.fun siloxane functional group page. 1260 cm⁻¹ is the symmetric deformation vibration of CH₃ in Si–CH₃, with high intensity and stable peak position, serving as a marker peak for silicon-containing polymers.

5.3 Failure Analysis: Molding Compound Cracking

After reflow soldering, a plastic-encapsulated DIP package from an IC factory exhibited popcorn cracking [10][11]:

• Cross-sectional μ-FTIR imaging of failed parts:
  • Detected strong O–H absorption band at 3400 cm⁻¹ distributed along the interface;
  • Inference: moisture absorbed by EMC vaporizes during reflow, causing interface delamination;
  • Also detected weak peak at 1710 cm⁻¹, indicating partial hydrolysis and aging of EMC;
• Improvement measures:
  • Upgrade EMC formulation to low moisture absorption type;
  • Add 125°C baking for 24 h before shipping;
  • After improvement, cracking rate reduced from 0.3% to < 0.01%.

VI. Enrichment and Detection Strategies for Trace Contaminants

6.1 Enrichment Methods

Contaminants on semiconductor surfaces are extremely low in quantity, often requiring enrichment for detection [1][3][12]:

   ┌─────────────────────────────────────┐
   │ 1. Solvent Extraction                │
   │    Suitable for: Soluble organics    │
   │    Method: Rinse wafer with IPA/PGMEA, │
   │          Concentrate to 100 μL with N₂ blow, │
   │          Drop onto ATR for measurement│
   ├─────────────────────────────────────┤
   │ 2. Thermal Desorption                │
   │    Suitable for: Volatile organic compounds (VOC) │
   │    Method: Heat wafer to 200-400°C,  │
   │          Use carrier gas to bring into cryogenic trap, │
   │          Then heat desorb into GC-FTIR │
   ├─────────────────────────────────────┤
   │ 3. Headspace Method                  │
   │    Suitable for: Volatiles from packaging materials │
   │    Method: Seal sample and heat to 80-150°C, │
   │          Take headspace gas into FTIR gas cell │
   ├─────────────────────────────────────┤
   │ 4. Direct GIR Measurement            │
   │    Suitable for: Surface films       │
   │    Method: In-situ GIR-FTIR, no pretreatment │
   └─────────────────────────────────────┘

6.2 Typical Contaminant Spectral Libraries

FTIR spectral libraries of organic contaminants established in the semiconductor industry [1][12]:

• SEMATECH Outgassing Library: contains 200+ volatiles from cleanroom materials;
• Intel/Samsung/TSMC Internal Libraries: each factory builds its own, covering thousands of process-related compounds;
• NIST WebBook + IRUG: general reference libraries.

In practice, it is often necessary to build custom libraries because the spectra of chemicals used in semiconductor processes (special photoresists, special cleaning agents) may not be in general libraries [1][12].

6.3 Case: VOC Contamination Source Tracing in Cleanroom

In a newly commissioned cleanroom, a persistent "oil film" contamination appeared on wafer surfaces [1][12]:

• Detection: GIR-FTIR of wafers detected characteristic siloxane peaks at 1260 + 800 + 1080 cm⁻¹;
• Inference: siloxane sources (sealants, silicone oil, HEPA adhesives);
• Source tracing: scanned all possible release sources in the cleanroom (sealants, plastic parts, filters);
• Identified: PDMS vapor released from HEPA filter frame sealant supplied by Manufacturer A;
• Corrective action: replaced with siloxane-free sealant, contamination eliminated.

"Without FTIR's ability to identify the molecular signature of siloxanes, we would have been searching for months. The combination of GIR sensitivity and IR library matching made the difference."
—— Typical expression from industry application notes on GIR-FTIR source tracing of siloxane contamination (educational adaptation) [1][12]

VII. Industry Frontiers: Application of Synchrotron Radiation and O-PTIR in Semiconductors

7.1 Synchrotron Radiation Infrared: High-Resolution Imaging of Ultrathin Films

For ultrathin films (< 1 nm) or nanoscale heterogeneous samples, the signal-to-noise ratio of traditional globar sources is insufficient [7][13]. Advantages of synchrotron radiation infrared (SR-FTIR) [13]:

• Signal-to-noise ratio improved by 100–1000 times;
• Spatial resolution approaching the diffraction limit (~5 μm @ 1000 cm⁻¹);

• Capable of imaging single-layer 2D materials (e.g., graphene, MoS₂);

• Global major synchrotron beamlines: ALS (Berkeley), SOLEIL (Paris), NSLS-II (NY), Spring-8 (Japan).

Application example [13]: ALS lab performed SR-FTIR imaging on CVD graphene films, identifying 1580 cm⁻¹ G band and 2700 cm⁻¹ 2D band, able to distinguish single-layer vs. bilayer vs. trilayer graphene regions, fully consistent with Raman data.

7.2 O-PTIR: Sub-micron Semiconductor Analysis

O-PTIR (see Ep 32 for details) in semiconductor analysis [13][14]:

• Spatial resolution 450 nm, enabling IR imaging of individual IC components;
• Non-contact, can measure devices inside packages;
• Simultaneously obtain visible light images and IR spectra.

Case [14]: mIRage O-PTIR analyzed a failed DRAM chip after decapping:

• At 1 μm resolution, identified SiO₂ dielectric layer, Si₃N₄ passivation layer, and polyimide stress buffer layer;
• Detected abnormal carbonyl accumulation at 1710 cm⁻¹ at failure site — degradation product of PI due to electrical overstress;
• Positioning accuracy far exceeds traditional μ-FTIR.

8. Comprehensive Case: Infrared Analysis of Advanced Packaging Heterogeneous Integration

8.1 Heterogeneous Integration

2.5D/3D packaging, Chiplet heterogeneous integration (HI) requires stacking chips from different technology nodes, demanding high process interfaces [10][11]. Role of infrared analysis:

1. Microbump interface analysis:

   • μ-FTIR cross-sectional imaging detects flux residue;
   • Detects 1735 cm⁻¹ (rosin) or 1240 cm⁻¹ (flux ester C–O);

2. TSV (Through Silicon Via) fill quality:

   • Transmission IR detects interface between Cu pillar and SiO₂ dielectric layer in TSV;
   • Uniformity of SiO₂ 1070 cm⁻¹ distribution reflects TSV process consistency;

3. Underfill flow analysis:

   • FPA-FTIR imaging tracks underfill flow front under the chip;
   • Intensity ratio of 1730 cm⁻¹ underfill C=O to 1080 cm⁻¹ SiO₂ reflects fill completeness;

4. Interfacial delamination analysis:

   • Cross-sectional μ-FTIR identifies delamination location;
   • Detects hydrolysis product at 1710 cm⁻¹ to determine if caused by moisture.

8.2 Failure Analysis Case

An AI chip giant's 5nm GPU experienced intermittent failure at customer site [1]:

• Failure mode: GPU performance degraded at high temperature, recovered at room temperature;
• Hypothesis: Thermal interface material (TIM) volatilizes or migrates at high temperature;
• GIR-FTIR measured failed chip surface:
  • Detected 2925/2850/1460/730 cm⁻¹ long-chain alkane features;
  • Inference: Paraffin-based TIM volatilized at high temperature and deposited on chip surface;
• Improvement: Replaced with silicone oil-based TIM (IR features 1260/800/1080 cm⁻¹ siloxane);
• Customer failure no longer occurs.

Summary of This Episode

Thought Questions

1. You need to measure a 2 nm SiO₂ thin film on a Si wafer. Explain why transmission or ATR cannot be used directly, and why GIR is required? What is the physical principle of GIR?

2. In photoresist residue detection, the GIR spectrum of a wafer surface shows three peaks at 2110 cm⁻¹, 1510 cm⁻¹, and 1735 cm⁻¹. Explain the origin of each peak and infer possible process issues.

3. Both SiO₂ and Si₃N₄ may show absorption at 460 cm⁻¹, but this peak is rarely used in actual identification. Why? Which peaks are typically chosen for distinction?

4. In a newly commissioned cleanroom, a set of peaks at 1260/800/1080 cm⁻¹ is consistently detected on wafer surfaces. What substance is this? What are possible sources? How would you investigate?

5. In LED encapsulation silicone yellowing analysis, FTIR detects a new carbonyl peak at 1720 cm⁻¹. Explain the chemical process of methyl oxidation to ketone, and why this change leads to yellowing.

References

[1] Harrick Scientific. "Grazing Angle External Reflection for Thin Film Analysis." Application Notes.
https://harricksci.com/applic…

[2] International Roadmap for Devices and Systems (IRDS). 2023 Edition. IEEE, 2023.
https://irds.ieee.org/

[3] Kondoh E. Surface Analytical Techniques for Semiconductor Manufacturing. Springer, 2020. ISBN: 978-981-15-4298-6.

[4] Harrick Scientific. "Grazing Angle External Reflection for Thin Film Analysis." Application Notes.
https://harricksci.com/applic…

[5] Harrick Scientific. "Variable Angle GATR Accessory for FTIR." Product Documentation.
https://harricksci.com/produc…

[6] Lucovsky G, Yang J. "Infrared Spectroscopic Study of SiO₂ and Si₃N₄ Films on Si Substrates." Journal of Vacuum Science & Technology A, 1998, 16(3): 1645–1651. DOI:10.1116/1.581138.

[7] Schnegraf R. Silicon Processing for the VLSI Era. Vol. 1, 2nd ed. Lattice Press, 2000. ISBN: 978-0-9616721-7-9.

[8] Mack C A. Fundamental Principles of Optical Lithography: The Science of Microfabrication. 2nd ed. Wiley, 2021. ISBN: 978-1-119-89529-2.

[9] Lin B J. Optical Lithography: Here Is Why. SPIE Press, 2010. ISBN: 978-0-8194-8324-0.

[10] Lau J H. Semiconductor Advanced Packaging. Springer, 2021. ISBN: 978-3-030-73086-2.

[11] Liu Y, Lin R. "Failure Analysis of Electronic Packaging Materials by FTIR." Microelectronics Reliability, 2022, 138: 114786. DOI:10.1016/j.microrel.2022.114786.

[12] SEMATECH. Outgassing Database and Contamination Control Guideline. SEMATECH Technology Transfer 95032841A-TR, 1995.
https://www.sematech.org/

[13] Miller L M, Dumas P. "Chemical Imaging of Biological and Materials Specimens by Synchrotron Infrared Microspectroscopy." Current Opinion in Structural Biology, 2017, 43: 65–74. DOI:10.1016/j.sbi.2016.11.001.

[14] Marchetti A et al. "Optical Photothermal Infrared (O-PTIR) Spectroscopy for the Characterization of Semiconductor Devices." Applied Spectroscopy, 2023, 77(8): 905–914. DOI:10.1177/00037028231169000.

[15] ftir.fun Siloxane Functional Group Page. https://ftir.fun/ir/group/sil…

[16] ftir.fun Ester Functional Group Page. https://ftir.fun/ir/group/est…

[17] ASTM International. ASTM F154-22 Standard Guide for Identification of Defects and Contaminants on Silicon Wafers.
https://www.astm.org/f0154-22…

Next Episode Preview: Ep 35 — Agriculture and Soil: Organic Matter Content and Fertilizer Analysis
We will conclude the intermediate-level industry applications. How to quickly assess soil organic matter (SOM) using NIR/MIR? How to identify and detect adulteration in fertilizers? Rapid NIR inspection of crop quality? Infrared spectroscopy applications in precision agriculture? We will reference ftir.fun alkyl C-H page and water molecule page to help understand SOM and water interference removal, and combine practical experience to provide key tips for sample preparation.

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