Книги2 / 1993 Dutton , Yu -Technology CAD_Computer Simulation

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Figure 1.1: Schematic representation of the use of device simulation to generate I-V and C-V data for bipolar (npn) model parameter extraction.

no implantation. For small VDS values the curves show identical drain currents. At higher drain bias, the device with the ion-implanted channel shows substantially more current handling capability. Subsequent chapters pursue the nature of this and other technology-dependent device effects. For now, suffice it to say that both the technologist and the circuit designer are anxious to understand and control these dependencies to realize optimum circuit performance.

1.2.3Motivation for Process CAD

The previous section demonstrated that device simulation is a powerful tool for determining circuit design parameters. Now we will discuss certain critical parameters in both bipolar and MOS devices that depend directly on quantitative features of the doping profiles. This dependence

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Figure 1.2: Schematic view of the role of device simulation for MOS device design. Variations in profile shapes and doping densities have a dramatic impact on device performance.

provides incentive for process simulation, since doping profiles are determined by process variables such as ion implantation energy, total implanted dose, and drive-in temperature/time cycles.

In bipolar devices, simulated capacitance and transport current values generally show good agreement with measured data, but simulated current gain is often several orders of magnitude too large. The current gain parameter is determined by the emitter efficiency, which in turn is determined by the shape of the emitter doping profile as well as electrical parameters there. Therefore, in order for the device simulator to produce accurate values for the emitter efficiency and current gain in bipolar transistors, it must receive an exact emitter profile description, which calls for a process simulator.

Historically, the desire to understand the emitter efficiency in bipolar devices resulted in attempts to simulate the double-diffused bipolar process [1.2]. These early attempts indicated that difficulties in modeling

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Figure 1.3: Selected one-dimensional process simulation results for the junction-isolated, phosphorus emitter bipolar process. In the emitter region (left panel) the boron diffusion is enhanced compared to the extrinsic base (right panel).

bipolar devices are not restricted to the electrical effects of the emitter. Figure 1.3 shows selected process cross-sections of a phosphorus emitter bipolar device. The cross-section on the left shows the doping profile under the emitter region and the cross-section on the right shows the doping profile of the base-collector region without an emitter present. A number of details of the technology are apparent from the crosssections shown. First, the base-collector junction is deeper in the region with the emitter present than in the region without the emitter. The emitter diffusion has enhanced the boron diffusion, resulting in what is known as the base "push-out." Second, the phosphorus emitter diffusion has a kink at high concentrations. This is where the simulation task gets tricky. The actual dopant profile looks like a superposition of two separate profiles with different diffusion coefficients. Therefore,

first-generation simulation models based on the use of a single diffusion constant do not yield realistic results. In order to create simulated profiles that match experiment, a rather unusual and unphysical set of assumptions must be made. "First generation," in this context, refers to both the time period in which the results were achieved and the relative sophistication of the physical models themselves.

For MOS devices, as we saw in Figure 1.2, threshold voltage and other device parameters are directly related to channel doping profile features. In contrast to the first-generation bipolar models, firstgeneration nMOS process models were quite accurate, primarily because the impurity profiles used for threshold voltage adjustments are of relatively low concentration. Impurity diffusion coefficients for low concentrations are well characterized, and model representations are straightforward. However, with the evolution of CMOS technology and gate dimensions in the range of 1 j.lm, device models that approximate two dimensional physical effects are approaching the limits of their adequacy. In later sections we will develop and present the second-generation models which are suited for CMOS. In fact, CMOS technology is the motivating theme of the entire text.

1.2.4The Role of Process CAD for Device CAD

The role of process CAD is to couple relevant fabrication information into the device CAD. The challenge is to capture those aspects of the fabrication process that will ultimately lead to limitations in device performance. For some parameters, analytical approximations describing doping profiles are sufficient to give good agreement between simulated and measured values. In other cases, more exact doping profile descriptions are needed to predict realistic values. The next section discusses how process simulators go about finding such "exact" profile descriptions. Then, Section 1.4 considers the issue of program interfacing, or how relevant information gets from the process simulator to the device simulator.

the physical characteristics of the system are changing with both time and space during each processing step. For example, boron diffusivity may be enhanced due to its own high concentration. In this case, the boron will diffuse and redistribute with time, but the rate of redistribution will not be constant. Instead, the diffusion rate at a given point will depend on the boron concentration at that point, which will vary with space. Numerical techniques help to give accurate results because they take both time and space variations into account.

1.3.2Numerical Implementation

The necessity for numerical simulation was briefly explored above. This subsection describes the numerical approach. The approach is not simple. Accuracy is gained only at the expense of increased complexity.

The first step in numerical simulation is the discretization of time and space. In other words, the device cross section must be represented as a collection of small cells, each of which must be evaluated at discrete time intervals. Time and space must be divided so that the concentration of the various impurities present are constant over each individual cell during each time increment, as are the diffusivity and other physical parameters. The grid spacing must be sufficiently dense so that all profile features are accurately represented. Increments of time must be short enough to not "step over" important effects. On the other hand, it is important not to use excessively small intervals, or the numerical solution will become time consuming and expensive.

The schematic representation of process simulation is illustrated in Figure 1.4. For each fabrication step, physical and chemical changes are simulated on a spatial grid as a function of time. This proceeds as follows:

1.Diffusion equations for each impurity are calculated for every point on the grid.

2.Time is advanced by an incremental step, the surface boundary layer is updated, and the diffusion equations are solved again.

This inner loop is continued until the final specified time for that fabrication step is completed. Then the next fabrication step is considered

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Figure 1.4: Schematic view of how process simulation proceeds. Based on input statements (often including physical parameters) the spatial distribution of impurities is solved by means of a time-domain simulation. The "physical change" refers to oxide growth while the "chemical change" here refers to impurity diffusion.

is to have MOS devices with low threshold voltage while field regions away from active devices have threshold voltages greater than the power supply in order to avoid inadvertent "turn-on" of the isolated regions. Figure 1.5 shows a typical input specification for this process. The first fabrication step is a boron ion implant. This step does not require looping in time to simulate. Values for the spatial distribution of the profile are taken from a lookup table. The second and third steps are both oxidations; first a dry oxidation, then a wet oxidation. Both oxidation steps require inner simulation loops in time as shown in Figure 1.4. The input statements for the oxidation steps give process-dependent information such as times and temperatures. The physical change in Figure 1.4 corresponds to the added thickness of oxide in each incremental time step. The chemical change corresponds to the redistribution of boron

1.4Interfaces in Process and Device CAD

1.4.1Introduction

Coupled process and device CAD allows designers to directly investigate the effect of process specifications on electrical variations. The key task of process CAD is to capture these features that accurately reflect the performance limitations of a given technology. Given the complexity and diversity of modern IC technology, this task is indeed formidable. Yet with the broad range of available fabrication techniques, it is essential to have coupled device and process CAD in order to assess the effect of technology changes.

Figure 1.7 presents an overview of the tools that can be used to link process specifications to circuit performance. The device simulator produces current-voltage data based on the output ofthe process simulator. The current-voltage data are reduced to circuit-oriented data by wel1established parameter extraction techniques, and the circuit-oriented data are used for circuit simulation. At each step, the emphasis is on focusing a broad spectrum of inputs into a coherent set of outputs that will be of maximum utility in the next design step.

Consideration of Figure 1.7 leads to two interface-related questions:

(1) How does the user interface with the program to provide input and to receive output? and (2) How do program-to-program data transfers take place? The following sub-sections consider each of these questions in turn.

1.4.2User-Specified Input and Program Output

Figure 1.8 illustrates the format of a typical user input for process simulation. The file shown simulates the process steps used to adjust the field threshold voltage, which is essentially a more detailed version of Figure 1.5, as discussed in Section 1.3. For the most part, the user input is simply a description of how the actual fabrication sequence progresses. However, the input also provides important information about the process, such as pressure and oxidation coefficients.

Within the simulator, process-dependent parameters are contained in models. The simulator has one or more built-in models for each