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Atomic Force Microscope Product List and Ranking from 12 Manufacturers, Suppliers and Companies

Last Updated： Aggregation Period:Aug 06, 2025～Sep 02, 2025
This ranking is based on the number of page views on our site.

Atomic Force Microscope Manufacturer, Suppliers and Company Rankings

Last Updated： Aggregation Period:Aug 06, 2025～Sep 02, 2025
This ranking is based on the number of page views on our site.

  1. パーク・システムズ・ジャパン Tokyo//Testing, Analysis and Measurement
  2. 日本カンタム・デザイン Tokyo//Testing, Analysis and Measurement
  3. 一般財団法人材料科学技術振興財団　MST Tokyo//Testing, Analysis and Measurement
  4. 4 オックスフォード・インストゥルメンツ Tokyo//Testing, Analysis and Measurement
  5. 5 東陽テクニカ Kanagawa//Testing, Analysis and Measurement 慶應義塾大学理工学部中央試験所・ 東陽テクニカ産学連携室 ナノイメージングセンター
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Atomic Force Microscope Product ranking

Last Updated： Aggregation Period:Aug 06, 2025～Sep 02, 2025
This ranking is based on the number of page views on our site.

  1. Atomic Force Microscope (AFM) 日本カンタム・デザイン
  2. [New Release!] Atomic Force Microscope 'Park NX7' パーク・システムズ・ジャパン
  3. Environmentally controlled, ultra-high performance AFM/SPM Cypher ES オックスフォード・インストゥルメンツ
  4. Atomic Force Microscope (AFM) "Park NX-3DM" パーク・システムズ・ジャパン
  5. 4 Atomic Force Microscope 'LiteScope 2.0' 東陽テクニカ 慶應義塾大学理工学部中央試験所・ 東陽テクニカ産学連携室 ナノイメージングセンター

Atomic Force Microscope Product List

31～33 item / All 33 items

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AFM Atomic Force Microscope Nano Observer 2

AFM Atomic Force Microscope Nano Observer 2

Achieving measurement quality comparable to high-end products from established AFM manufacturers.

Combining flexibility, outstanding performance, and user-friendly operability, it achieves measurement quality comparable to high-end products from established AFM manufacturers. It is equipped with a wide range of functions for nanoscale imaging and characterization. It also enables electrical property measurements (KFM, C-AFM) and features a soft IC mode that can measure fragile samples.

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[Analysis Case] Surface Shape Analysis of GaN Substrates

[Analysis Case] Surface Shape Analysis of GaN Substrates

Visualization of step-terrace structures by AFM.

Gallium nitride (GaN), a wide bandgap semiconductor, is used in a wide range of fields such as power devices and communication/optical devices. When fabricating devices, the shape and roughness of the wafer surface significantly impact device performance. During the growth of GaN wafers, a step-terrace structure is formed on the surface due to stress effects from lattice mismatch with the supporting substrate. This document introduces a case where the step-terrace structure of the GaN substrate surface was visualized using AFM, and the terrace width, step height, surface roughness, and off-angle were evaluated. Measurement method: AFM Product fields: Power devices, electronic components, lighting Analysis purpose: Shape evaluation, structural evaluation For more details, please download the document or contact us.

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  • Contract Analysis

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Investigate the structures and dynamics of biomolecules that are difficult to measure experimentally through simulation.

Investigate the structures and dynamics of biomolecules that are difficult to measure experimentally through simulation.

Keywords: Molecular simulation, biomolecules, integrated modeling, free energy calculation.

Many life phenomena, including various diseases, are caused by biomolecules such as proteins, and observing molecular structures and dynamics in the microscopic realm is crucial for understanding these phenomena and for fundamental treatments of diseases. Molecular dynamics simulation is a technique that allows for the "direct" observation of microscopic behavior by reconstructing biomolecular models with atomic resolution in a computer and moving the molecules according to physical laws. Coupled with the computational power of computers, this technique has developed to the point where it is referred to as a computational microscope and is actively used as a method to complement experiments. However, there are two challenges to contributing to drug discovery and materials development: "too much computation time" and "limitations in model accuracy." To address the first challenge, we are working on introducing efficient algorithms to predict loop structures of next-generation antibodies in a shorter time. For the second challenge, we are developing methods that integrate experimental data and simulations using statistical mathematics and machine learning to achieve more accurate observations.

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