SRAM and DRAM cannot be used interchangeably in a computer system due to their distinct characteristics and roles. SRAM, with its fast access time and stability, is ideally suited for cache memory. This includes CPU cache, where the speed of memory access is critical for performance. The use of SRAM in this context helps to bridge the speed gap between the processor and the slower main memory, enhancing the overall efficiency of the system. Conversely, DRAM is preferred for main system memory due to its higher density and lower cost. Its larger capacity at a more affordable price makes it ideal for storing the bulk of the data and applications in use. While DRAM is slower and requires constant refreshing, these drawbacks are acceptable for main memory, where the sheer volume of storage is more critical than the speed of access. In summary, the choice between SRAM and DRAM is not about interchangeability but about using each type of memory where its advantages are most beneficial – SRAM for speed-sensitive applications like cache, and DRAM for capacity-focused applications like main system memory.

The choice between SRAM and DRAM can significantly influence the overall cost of a computer system. SRAM is more expensive to produce due to its complex internal structure, typically utilizing six transistors per bit of memory. This high cost limits its use to applications where speed is critical, such as cache memory. The use of SRAM in these areas, though costly, is justified by the significant performance improvements it brings. In contrast, DRAM uses a simpler structure with one capacitor and one transistor per bit, making it much cheaper to produce. This cost-effectiveness allows DRAM to be used extensively for main system memory, providing large memory capacities at a lower price. Consequently, a computer system with more SRAM will generally be more expensive, but also faster, particularly in tasks requiring rapid memory access. Systems prioritizing cost over speed, like budget PCs or entry-level laptops, will rely more heavily on DRAM to keep prices down. In summary, the balance between SRAM and DRAM in a computer system is a trade-off between cost and performance, with the choice heavily influencing the overall price and capabilities of the system.

The choice between SRAM and DRAM in mobile devices significantly affects both battery life and performance. SRAM, with its higher speed and lower power consumption due to the lack of a need for refreshing, can lead to more efficient processing and longer battery life. However, the larger size and higher cost of SRAM make it less practical for the bulk of memory in mobile devices. DRAM, while slower and more power-intensive due to its need for constant refreshing, offers a higher storage capacity at a lower cost and smaller size, making it more suitable for the main memory in these devices. The impact on battery life is a critical consideration in mobile devices; thus, manufacturers often opt for a combination of both – using SRAM for cache memory where speed is essential and DRAM for main memory where capacity is paramount. This combination balances the need for performance and efficient power usage, but it also means that managing the power consumption of DRAM becomes a critical aspect of mobile device design. Techniques like "deep sleep" modes for DRAM can help reduce power draw when the device is not actively in use.

Environmental factors such as temperature, humidity, and electromagnetic interference can significantly impact the performance of SRAM and DRAM. SRAM, being less dense and more stable, generally performs better under a wider range of temperatures and is less susceptible to environmental stresses. However, in extreme conditions, even SRAM can suffer from issues like increased power consumption and reduced data retention capability. On the other hand, DRAM is more vulnerable due to its high density and the nature of its storage mechanism (capacitors). High temperatures can accelerate the leakage of charge from DRAM capacitors, necessitating more frequent refreshing, which in turn increases power consumption and reduces the overall efficiency of the memory. Humidity can also lead to condensation on the circuitry, potentially causing short circuits or corrosion. Additionally, electromagnetic interference can disrupt the operation of both types of RAM, but DRAM is generally more susceptible to such disturbances because of its reliance on the charge stored in capacitors, which can be more easily disrupted than the bistable states of SRAM.

Recent advancements in SRAM and DRAM technology have significantly impacted computer performance. For SRAM, developments have focused on reducing power consumption and increasing speed. Techniques such as deep trench isolation and FinFET (Fin Field-Effect Transistor) technology have allowed for more compact SRAM cells, leading to higher cache densities and improved performance in processors. These advancements enable CPUs to process data more quickly and efficiently, reducing bottlenecks in high-speed computing tasks.

In the realm of DRAM, advancements have been geared towards increasing density and reducing power consumption. Technologies like 3D stacking and through-silicon vias (TSVs) have allowed for higher capacity DRAM modules within the same physical footprint. This has been pivotal in supporting the growing memory demands of modern applications and operating systems. Furthermore, improvements in DRAM manufacturing processes have led to finer lithography, allowing for more memory cells on the same silicon area, thus increasing capacity and reducing cost.

Both SRAM and DRAM advancements have also focused on reducing power consumption, a critical factor in mobile and portable devices. Newer memory technologies, including low-power variants of DRAM like LPDDR4 and LPDDR5, offer significant power savings over traditional DRAM, contributing to longer battery life in laptops, smartphones, and tablets.

Overall, these technological advancements in SRAM and DRAM have led to computers that are faster, more efficient, and capable of handling more complex tasks and larger datasets, thereby enhancing the overall user experience.