Aqueous cleaning fluid plays a crucial role in the cleaning of semiconductor packaging devices, and the detection of its residues directly affects the reliability and yield of the devices. Contaminants that may remain after cleaning include ionic impurities, organic matter, particulate matter, and metal ions. If these residues are not effectively removed, they may cause electrochemical migration, leakage, or even device failure. Therefore, multi-dimensional detection methods are necessary to ensure that the cleaning effect meets standards.
Ion residue detection is a core step, primarily targeting conductive impurities such as chloride and sodium ions that may remain in the aqueous cleaning fluid. If these ions adhere to the device surface, they can form micro-conductive pathways, leading to a decrease in insulation performance. Ion chromatography is typically used for detection; through separation and quantitative analysis, the types of ions can be accurately identified and their concentrations measured. For high-requirement scenarios, dynamic methods (such as omega-meters) are also combined to test the conductivity of the extract solution, further verifying whether the ion residue level is within a safe range.
Organic residue detection focuses on organic contaminants such as photoresist and flux that may remain after cleaning. If these substances are not completely removed, they will form an insulating layer or attract dust on the device surface, affecting the adhesion of subsequent processes. Detection methods include Fourier Transmission Infrared Spectroscopy (FTIR), which identifies organic components through characteristic absorption peaks; or using a Total Organic Carbon (TOC) analyzer to determine the total amount of organic carbon in the sample, indirectly reflecting the degree of organic contamination. For specific organic compounds, High Performance Liquid Chromatography (HPLC) can also be used for separation and quantification.
Particulate contamination detection is an important indicator for evaluating cleaning effectiveness. Semiconductor manufacturing is extremely sensitive to particle size, especially submicron particles, which can become the source of device defects. Detection typically uses a laser particle counter, where the device is ultrasonically vibrated in ultrapure water, and the number and size distribution of particles in the liquid are analyzed. For critical areas, scanning electron microscopy (SEM) is also used for high-resolution imaging to observe the presence of tiny particles or scratches on the surface, ensuring that particulate contamination is controlled within the process tolerance range.
Metal ion residue detection targets metallic impurities such as iron and copper that may be introduced into the aqueous cleaning fluid. If these ions deposit on the device surface, they will form a metallic impurity layer, altering the electrical properties of the semiconductor. The detection methods include inductively coupled plasma mass spectrometry (ICP-MS), with trace analysis capabilities down to the part-in-a-trillion level, capable of simultaneously determining the content of multiple metal elements. For specific metals, atomic absorption spectrometry (AAS) can also be used for specialized testing to ensure that metal ion residues meet industry standards.
Surface morphology and roughness detection is achieved using atomic force microscopy (AFM). This technique can quantify parameters such as root mean square roughness (RMS) and peak-to-valley height difference to assess the impact of cleaning on the device surface microstructure. Devices that have passed cleaning should maintain atomic-level flatness to avoid localized protrusions caused by mechanical wear or chemical corrosion, which could affect the accuracy of subsequent photolithography alignment. Furthermore, AFM can observe whether there are films or spots formed by residual cleaning solution on the surface, providing a direct basis for process optimization.
Functional performance testing is the final step in verifying the cleaning effect. By simulating the actual operating conditions of the device, such as electroplating adhesion testing and bonding wire bonding strength testing, it can be assessed whether the cleaning meets the performance requirements of the final product. For example, performing multiple cyclic stress tests on MOSFET devices and observing the threshold voltage drift can diagnose whether tiny leakage current channels reappear after cleaning. This type of testing, combined with the aforementioned physical and chemical detection methods, forms a closed-loop quality control system to ensure the reliable application of aqueous cleaning fluid in the cleaning of semiconductor packaged devices.
