mpw-005 / NSM Nitride Seal Mask

the NSM shapes of all of mpw-005

40 of 40 dies rendered.

1,440 shapes in total, each die at 0.125 µm per pixel.

result.json · job.json · reticle overview PNG

1234567
A A1 A2 A3 A4 A5 A6
B B1 B2 B3 B4 B5 B6 B7
C C1 C2 C3 C4 C6 C7
D D1 D2 D3 D4 D5 D6 D7
E E1 E2 E3 E4 E5 E6 E7
F F1 F2 F3 F4 F5 F6 F7

Downloading the full-resolution image

The stitched reticle is 53,432×65,056 pixels — 3.48 gigapixels. The file itself is small, but decoding it needs about 3.5 GB of memory however small the file is, and most viewers refuse it outright. So it is also published as 42 tiles of about 7576×10776 px (4.4 MB in total, largest 111.0 kB), cut in the gaps between dies so that every tile holds whole projects and none is split.

To put the full-resolution PNG back together from them, byte for byte, with nothing installed but Python itself:

python3 reconstruct_reticle.py

Run it in a directory holding the tiles and mpw-005_NSM_tiles.json and it finds them itself, checks every tile against its checksum as it reads it, and checks the finished file against the manifest. It works because the tiles and the full image are written with PNG's Up filter, which never looks at the pixel to its left — so a row of tiles' scanlines laid end to end is the full image's scanline, and rebuilding moves bytes rather than doing pixel arithmetic.

Whole-reticle files

FileSizeMD5SHA-1SHA-256
mpw-005_NSM_reticle.png
the stitched reticle, full resolution
3.5 MB fa8f46f05ed99c167bed6eafcadbbf10984e7606d2bf2a8c427e92356b5f10754132999d 6697f89be35c7d562e2d488972569d1a66a48b520fb563acd20a45d2e108dc4e
mpw-005_NSM_reticle_overview.png
the same image scaled to fit 4096 px
13.8 kB 0c05ddf05b941824ce507f1b477f169623a4c9b623586c8da0c62b3ce47069d6ddb83679 8b0a0ee52a20bba2478d6de62356b951e1c0f6e5d68cecef14f80b473b655c98
mpw-005_NSM_tiles.json
where every tile belongs, and its checksum
13.5 kB 4573398feaf93abe1b633eee342ee3919ee3dbc85df26c2cd393bd32031b73670a5f2482 bc80b2f6962a044064d27bcd83c48a434ef4276f928807a87e8efe567a37e8f7
reconstruct_reticle.py
rebuilds the reticle from the tiles, standard library only
11.9 kB 4406f1f3db8a4a85756610b7c432c47a294714566ceb084187594ced0020f9260faac32a d2e72bf4b2541f1a8acb11eb758917c5b5fa080b41b3a7118487c03f8af2a769

Every file in this directory is listed in MD5SUMS · SHA1SUMS · SHA256SUMS, in the format the standard tools read:

sha256sum -c SHA256SUMS

The 42 tiles

FrameProjectPixelsPositionSizeSHA-256
A1 pll-based_capacitance-to-digital_converter_mpw4 7576×107760, 0 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
A2 mpw5_cache 7576×107767576, 0 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
A3 zero_to_asic_course_group_submission_mpw5 7576×1077615152, 0 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
A4 zero_to_asic_mpw2_rerun_on_mpw5 7576×1077622728, 0 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
A5 zero_to_asic_mpw3_rerun_on_mpw5 7576×1077630304, 0 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
A6 zero_to_asic_mpw4-rerun 7576×1077637880, 0 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
A7 SkyWater test structures 7976×1077645456, 0 106.8 kB56c5b78f9b86a53fdb64304acc12fba53d045b0526cc20ac1552594029f8a19a
B1 actuatorcontroller 7576×107760, 10776 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
B2 microwatt_mpw5 7576×107767576, 10776 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
B3 armleo_gpio_mpw5 7576×1077615152, 10776 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
B4 fabulous_efpga 7576×1077622728, 10776 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
B5 i2c_controller 7576×1077630304, 10776 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
B6 lbist-mbist 7576×1077637880, 10776 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
B7 riscduino-dual_core__rd0_ 7976×1077645456, 10776 107.0 kB487ec85f022a82c52889e3817d3be0241a7f710f7b99ee632b114eb658d6ca58
C1 riscduino-qcore__q0_ 7576×107760, 21552 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
C2 high_speed_vco 7576×107767576, 21552 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
C3 openfasoc-cryo-gen 7576×1077615152, 21552 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
C4 microwave_signal_generator 7576×1077622728, 21552 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
C5 SkyWater test structures 7576×1077630304, 21552 102.7 kB404d2b68827b8f64404c8d26b19179b934888b57260a33a16e25162769e85650
C6 pico_design_resubmission_mpw5 7576×1077637880, 21552 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
C7 ask_modulator_and_impedance_transformer 7976×1077645456, 21552 107.0 kB487ec85f022a82c52889e3817d3be0241a7f710f7b99ee632b114eb658d6ca58
D1 baseband_inductorless_automatic_gain_control 7576×107760, 32328 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
D2 azadi_iii 7576×107767576, 32328 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
D3 tia_for_physic_experiments 7576×1077615152, 32328 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
D4 qf105 7576×1077622728, 32328 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
D5 coriolis_test_soc_-_mpw5_vexriscv 7576×1077630304, 32328 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
D6 junga_soc_mpw5 7576×1077637880, 32328 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
D7 nand_flash_mpw-5 7976×1077645456, 32328 107.0 kB487ec85f022a82c52889e3817d3be0241a7f710f7b99ee632b114eb658d6ca58
E1 kasirga-c0-mpw-5 7576×107760, 43104 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
E2 delta-sigma_audio_dac__mpw5_ 7576×107767576, 43104 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
E3 wirelessenergyharvesting 7576×1077615152, 43104 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
E4 caravel_raster_engine 7576×1077622728, 43104 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
E5 DDR3_SSTL_TEST 7576×1077630304, 43104 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
E6 accelerated_risc-v_cpus 7576×1077637880, 43104 103.0 kBf09a354f1038e53480b60cb0a9b0048f715438d2fc1c33a37036970cfc7ba3b1
E7 alu 7976×1077645456, 43104 107.0 kB487ec85f022a82c52889e3817d3be0241a7f710f7b99ee632b114eb658d6ca58
F1 4ft4 7576×111760, 53880 106.9 kB902c6750d7e9bddb503183eb9dcde6eb494d3adef8602a53f088756b9ff48679
F2 rng_chaos 7576×111767576, 53880 106.9 kB902c6750d7e9bddb503183eb9dcde6eb494d3adef8602a53f088756b9ff48679
F3 uetrv-ecore 7576×1117615152, 53880 106.9 kB902c6750d7e9bddb503183eb9dcde6eb494d3adef8602a53f088756b9ff48679
F4 systolic_array 7576×1117622728, 53880 106.9 kB902c6750d7e9bddb503183eb9dcde6eb494d3adef8602a53f088756b9ff48679
F5 reram_test 7576×1117630304, 53880 106.9 kB902c6750d7e9bddb503183eb9dcde6eb494d3adef8602a53f088756b9ff48679
F6 secure_memory 7576×1117637880, 53880 106.9 kB902c6750d7e9bddb503183eb9dcde6eb494d3adef8602a53f088756b9ff48679
F7 asynchronous_fibonacci_counter_testfix 7976×1117645456, 53880 111.0 kB075cdf0ea54fdde2e66ecd0d444735c84f8b43521db2ecb9f8679f140714c3f0

Position is the tile's top-left corner in the full-resolution image. MD5 and SHA-1 for the same files are in the checksum files linked above.

About NSM on mpw-005

Resolved: reticle 5CS8011AC, run mpw-005, mask NSM

Mask layer
NSM — Nitride Seal Mask
Process step
165 — Nitride seal mask
Drawn layer
61:20
Layer on the mask-layer GDS
22:0
Used in SKY130
yes

mpw-005 — SKY130 MPW-5

Foundry product
EF MPW#5 (+ 'With ReRAM' split lot)
Reticle set
5CS8011AC
Run date
2022-03-21
Submissions closed
2022-03-21
Projects
74
Fab lot
4229389
Identification
confirmed — Lot records: EF MPW#5, lot 4229389 = 'SKY130 MPW 5'
Shuttle page
archived copy of https://platform.efabless.com/shuttles/MPW-5 — the platform that hosted it is gone
Foundry data
third_party/shuttle/sky130/mpw-005/foundry

These are renders of the drawn shapes that go on each photomask, taken from the layouts the shuttle's projects taped out — they are not photomask artwork: reticle pitch, the 4x reduction, mirroring and the frame features the fab adds are not modelled. An empty image is a real result; several mask layers are used by no project on a given shuttle. Each die's source layout is named in result.json and lives in the public shuttle repositories under foss-eda-tools.googlesource.com, so every image here can be traced back to the tape-out it came from. Generated with sky130-mask-decoder.