In this episode, Françoise von Trapp talks with Onto Innovation’s Monita Pau and Jiangtao Hu about metrology for advanced packaging – why do we need it? What are the challenges, and how do we solve them?
In semiconductor manufacturing front-end processes, metrology has always been a critical step to ensure consistency of very fine features. It’s only recently become important to back-end advanced packaging processes – especially for heterogeneous integration. As chips are designed with smaller features, advanced packaging processes are becoming more front-end like.
You’ll learn about how metrology designed for front-end manufacturing is being reimagined for wafer-level and assembly applications such as:
- Hybrid bonding
- 3D stacking with micro bumps
- RDL applications for interposers
- TSV applications
The speakers discuss the challenges, gaps, and solutions for each. You’ll also learn what makes Onto Innovation uniquely qualified to support this.
The United States is seeking to breathe new life into its domestic semiconductor packaging sector with the National Advanced Packaging Manufacturing Program (NAPMP), an initiative to “establish and accelerate domestic capacity for advanced packaging substrates and substrate materials,” according to a Commerce Department announcement from earlier this year [1]. In part, this program is the result of two very distinct trends, both of which are high-priority pursuits for governments and manufacturers. On the one hand, many nations like the U.S. are looking to shore up their semiconductor manufacturing capabilities to better protect themselves from potential geopolitical complications. Still another consideration is today’s rapidly growing demand for high-end applications like artificial intelligence (AI) and high performance computing (HPC) that are driving the need for advanced packages with 2.5 and 3D architectures. Such structures are built upon advanced integrated circuit substrates (AICS). Furthermore, the coming era of glass core substrates in advanced packaging will offer another level of challenges. The future is coming, and few want to be left behind.
The road to the future is not always a smooth, trouble-free drive. Along the way, there may be unforeseen detours, potholes and accidents, each one capable of setting progress back. But for those behind the wheel, those obstacles are just a part of the journey.
Such is the case for the automotive industry as it continues to steer away from gas-powered vehicles and turn toward hybrid and electric vehicles. To accomplish this, manufacturers of power devices are opting to use wide-bandwidth compound semiconductors like SiC and GaN. The reason: compound semiconductors accommodate higher voltages, faster switching speeds and lower losses than traditional silicon-based power devices.
For the purpose of our three-part series, we have been focusing on SiC power devices, the challenges presented by trench-based architectures that reduce on-resistance and increase carrier mobility, and the need to accurately measure epi layer growth and the depth of implant layers. Before we move onto the details of this blog, let’s take a quick look back at the previous two blogs.
Abstract
DRAM storage node profile measurement during high aspect ratio (HAR) etch has been one of the most challenging metrology steps. DRAM storage node profile affects refresh time and device electric quality. So, controlling this profile is one of the key challenges. Conventional 3D modeling in Optical Critical Dimension (OCD) metrology has typically used multiple cylinder stacks. This method cannot provide an accurate model and computed spectrum through the RCWA engine. This means we need a more accurate model. In this paper, we used hyper-profile to accurately measure a hole profile for better process control. Hyper-profile uses a polynomial to describe the smooth shape of a hole profile, which is much closer to the real product and provides a more accurate computed spectrum. With hyper -profile, a continuous storage node hole profile and managed CD correlation are achieved. It can maintain the same profile complexity with less degree of freedom, reducing the model uncertainty and ensuring more robust regression. On the other hand, as the metrology error budget becomes stricter and the process variation cycle is increasing, the OCD based model-guided machine learning (MGML) approach can provide a faster solution turnaround time with more accurate measurements than either pure OCD or pure ML approaches. It also can better decorrelate profile CDs and achieve more robust profile monitoring. In this paper, we will demonstrate the above benefits of hyper -profile and MGML in the DRAM storage node application.
Abstract
As scaling in semiconductor devices continues, the aspect ratios of deep trench isolation (DTI) structures have increased. DTI structures are used in power devices, power management ICs and image sensors as a method to isolate active devices by reducing crosstalk, parasitic capacitance, latch-up and allowing for an increase breakdown voltage of active devices. Measurement of these structures in high volume manufacturing (HVM), with non-destructive technology, has mostly been limited to the depth and top width of the DTI structure, while the bottom width (BCD) has not been able to be reliably measured. Here we present two different optical metrologies, “conventional” OCD and IRCD, that operate in the UV-VIS-NIR and MIR region of the electromagnetic spectrum, respectively, and discuss the measurability of DTI sidewall profile, bottom width, and depth in BCD (Bipolar CMOS DMOS) power management IC devices for each method at various pitches and line to space ratios. Experimental data will be presented showing sensitivity and discrimination of IRCD to a DOE specifically on the bottom width for three different structures.
Abstract
Fourier Transform Infrared spectroscopy offers inline solutions for chemical bonding, epi thickness, and trench depth measurements. Through optical modeling of the transmission or reflectance spectra, information about the electronic structure and chemical composition may be obtained, which can be used for process control and monitoring. In this article, we demonstrate the measurement capabilities of FTIR for the hydrogen bonding in cell silicon nitride and amorphous carbon hard masks (ACHM), which are used for 3D NAND fabrication. For cell silicon nitride, deconvolution of the spectra allows differentiation between individual peaks corresponding to Si-N, Si-H, N-H, Si-O, and Si-OH bonds. This differentiation identifies wafers with varying hydrogen content and distinct processes. Similarly, for ACHM, peak areas related to sp2 C-H bonds and aromatic C=C bending reveals the hydrogen skew conditions in three wafers. Notably, a linear relationship between high broadband absorption and low C-H bonds (and aromatic C=C) peak area is observed. The measurements exhibit good repeatability across ultrathin silicon nitride and thick ACHM samples. We believe the technique can be valuable for compositional process control, considering the significance of hydrogen content in cell nitride performance and endurance, as well as the influence of hydrogen content and carbon sp2/sp3 ratio on selective etch ratios in dry etch processes involving ACHM and mechanical properties of the films.