The competitive landscape of next-generation technology (particularly in electrification, autonomy and 5G) requires a fast go-to-market strategy.
As a result, industries like automotive, aerospace and defense are rushing to ensure that products and devices are hitting the market sooner — while facing new and tougher reliability and warranty standards.
As demand increases for electronic products, it will become increasingly necessary for companies to expedite their product design optimization processes.
A potential solution is simulation, which virtually models and tests products to determine ideal configurations and potential failure risks early in the design process. This can prevent costly redesigns later in product development and enables quicker, more reliable manufacturing.
One of the biggest challenges standing in the way of implementing a comprehensive simulation workflow is the lack of shared data between supplier and customer. This is usually caused by intellectual property concerns or an excessive focus on creating minimum viable product (MVP) datasheets necessary for selling electronic products.
This lack of communication stifles the simulation process and causes misunderstandings that can result in unnecessary delays or significant warranty claims.
The Problem with Data Sheets
Data sheets are documents, provided by a supplier, that summarize how a component is expected to operate over a range of conditions. However, data sheets do not always provide the information needed for specific parameters and use environments. For instance, some buyers require certain mechanical, electrical and reliability material properties, which are often absent from the document. If the customer is unable to obtain these properties, material characterization may be necessary to extract the data needed to create accurate simulation results.
How to Perform Material Characterizations of Electronic Components
There are typically three main material characterization processes for electronic components:
- Mechanical characterization
- Electrical characterization
- Reliability characterization
Mechanical Material Characterization
A major driver of mechanical characterization is to prevent unexpected product failures due to changes in temperature or mechanical loads (such as vibration, shock or bending). This need is especially prevalent in the automotive industry, where failures during design validation (DV) can set back product launches.
To avoid these kinds of failures, customers, such as automotive Tier 1 suppliers, expect part manufacturers to complete and pass all testing standards from JEDEC, the global leader in developing open standards for the microelectronics industry. Unfortunately, these tests do not capture solder fatigue risk in complex systems where the enclosure, or housing, interacts with the printed circuit board (PCB). A three-pronged approach to mechanical characterization that help solve these issues include:
- 3D X-ray
- Digital image correlation (DIC)
3D X-ray is a tool that captures internal geometries not provided in the data sheet. Typically, dimensions included in a data sheet are based on external perspectives (like height, length, width and number of leads). However, only describing the external properties of a component can lead to simulation and manufacturing errors. For example, from an external image, an engineer may not know that there are two dies in a device instead of one. This information plays a critical role when it comes to simulation.
Digital Image Correlation (DIC)
DIC is a no-contact optical method to measure deformations of an object’s surface through the stereoscopic tracking of pattern features. With information on deformation, the DIC can determine expansion and contraction within the in-plane (X and Y axis) and out-of-plane (Z axis) as a function of temperature. This behavior can be critical when assessing the risk of solder fatigue within an assembly or component.
To perform this procedure, a stereo camera records images of the component through a window cut into a thermal chamber. These images are decomposed into deformations along any axis. The user can then compute the coefficient of thermal expansion (CTE) in any direction — including along the diagonal. The magnitude of CTE, and its relative difference between the electronic part and the PCB, will have a huge influence on the solder fatigue risk and the overall life cycle of the product.
Decapsulation, or deconstruction, are techniques that enable the inspection of internal elements of components (like dies, interconnects and other features). They also characterize the individual materials that make up the electronic components (including its encapsulant, substrate and die attach).
For example, when characterizing a ball grid array (BGA) for solder fatigue, an important material property is the modulus of the die attach material. The lower the modulus, the more the die is decoupled from the package and the less influence it has on solder fatigue behavior. Since the die attach is an internal feature, decapsulation, followed by deconstruction, is necessary to expose its material. Nanoindentation can then be used to accurately measure the modulus as a function of temperature. Knowing these material properties provides the necessary information to create accurate simulations.
Electrical Material Characterization
Electrical characterization provides important parametric information about an electronic component (including its electrical performance and failure behavior). Customers, such as OEMs, typically require electrical characterization when:
- There is a need to simulate a product’s behavior and survivability with respect to:
- Electromagnetic compatibility (EMC)
- Electromagnetic interference (EMI)
- Electrostatic discharge (ESD)
- Electrical overstress (EOS)
- Key parametric behaviors are missing from the data sheet
- There is a need to know what will happen if the voltage or temperature goes beyond specification
As an example, there are a number of methods that can be used to characterize electrical behavior for accurate EMI and EMC simulation. For example, relatively straightforward electrical material characterization includes using EMI scanners to capture board-level, passive behaviors like resonances, plane impedances and loop inductances.
For higher simulation fidelity, electrical material characterization would involve capturing transient signals that depend on driver models (like an input/output buffer information [IBIS] model or a simulation program with integrated circuit emphasis [SPICE] model).
The most advanced simulation efforts require electrical material characterization that replicates a full EMC environment, including an anechoic chamber, absorber elements and antennae.
Reliability Material Characterization
Reliability is often missing from data sheets because it is application specific. In order to supplement this missing data, accelerated life testing (ALT) can be used to answer reliability and failure risk questions before the product goes to prototype.
Specifically, ALT analyzes how a product, or component, will function under stressors (such as the temperature changes, shocks and vibrations) it will face throughout its life cycle but at a much faster rate than in the field. ALT can indicate the rate of failure, risk factors and design weaknesses. With reliability characterization, the product can be released into the field with the confidence that it will survive its intended application and design life.
The results of ALT can also be used to benchmark the results from simulation and modeling. This increases an organization’s confidence in the outputs from these early stage efforts and lays a path toward simulation-driven engineering and validation.
Characterization of electronic components is critical for both the designing and manufacturing success of electronic products. However, relevant properties of electronic components are not always included in data sheets, meaning that additional resources are needed for proper parts characterization. With an understanding of what characterization is required, electronic component properties can be validated, failure risks can be simulated and late development redesigns can be avoided. This leads to a faster time-to-market and cost-efficiencies.
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